modelling a commercial-scale bagasse liquefaction plant using aspen … · 2018-07-17 · modelling...

Modelling a Commercial-Scale Bagasse

Liquefaction Plant Using ASPEN Plus

Jerome Luigi A. Ramirez

Bachelor of Science (Chemical Engineering)

Submitted in fulfilment of the requirement for the degree of

Doctor of Philosophy

School of Chemistry, Physics and Mechanical Engineering (CPME)

Science and Engineering Faculty

Queensland University of Technology

2018

This work is dedicated to the memory of my grandmother, Aurora.

Her light shone upon the letters that compose this thesis.

“In nova fert animus mutatas dicere formas corpora”

“Of bodies changed to other forms I tell”

-- Ovid, Metamorphoses, Book I

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus v

Abstract

The development of alternative energy technologies in the modern age

requires not only a reliable and inexpensive supply, but also sustainable

production that takes into account economic, environmental and social impacts.

The development of biofuels have ushered in the use of clean, renewable

transportation fuels; however, to meet energy demand without affecting food

security, technologies that can process non-food crops are posed to make biofuels

more sustainable. Of these technologies, liquefaction is emerging to be a preferred

conversion process for its high overall yield and ability to process whole biomass

feedstock, regardless of composition and moisture. Liquefaction is a

thermochemical process that operates at relatively moderate temperatures of

280-400 °C and high pressures of 5-20 MPa. The process produces biocrude,

which has better properties compared with pyrolysis bio-oil, but similarly cannot be

directly used as fuel in vehicles without further upgrading. Compared with

pyrolysis, liquefaction has not been developed commercially. This may be partially

due to the numerous lab-scale liquefaction studies that have not been

demonstrated as technically- and economically-feasible.

Biocrude properties and the prospective processes required to upgrade

biocrude to fuel were reviewed. Many potential processes were based on pyrolysis

bio-oil upgrading due to the similarity of biocrude with pyrolysis bio-oil. Most of the

processes were also petroleum refining analogues, which suggests a potential for

co-processing in existing refineries.

The blending of biocrude with petroleum crude oil was explored with a view

of co-processing biocrude in conventional distillation columns. Parameters such

as blending ratio and temperature were varied. It was observed that blending at

50 °C or higher and vigorous mixing enabled a consistent blend of biocrude and

petroleum crude. A model of the blend was also developed in ASPEN Plus. The

generated distillation curves in ASPEN and the measured distillation curves of the

blends had good matching in blends prepared above ambient temperatures.

Blends in different ratios were also modelled and verified. Distillation curves of

various biocrude types were also presented. The study verifies ASPEN's usefulness

vi Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus

in modelling biocrude distillation for processing in refinery distillation columns

either as a blend or on its own.

A process model of a 10 t/h liquefaction plant converting sugarcane bagasse

to liquid fuels in an Australian setting was modelled in ASPEN Plus. Results of the

process model were used in an economic model to calculate economic indicators.

Ethanol was investigated as a liquefaction solvent due to its use resulting in higher

yields and higher biocrude HHV compared to the use of water (i.e. hydrothermal

liquefaction), and its availability as a renewable solvent. The plant produced 0.67

kg biocrude per kg dry feed, which was further processed to 0.46 kg liquid fuels

per kg of dry feed for a total of 25.8 million L/y of biofuel product. Separation to

two equal streams generated gasoline-like and diesel-like products that can be

sold to generate US$ 11 million/y in revenue. When sold as a crude oil-like product

without separation, at the current market price, the revenue is US$ 8.4 million/y.

Ethanol losses incurred the highest share in operating costs, although there are

opportunities for cost reduction around lower solvent to biomass ratio. Over the

plant life of 20 years and a corporate tax rate of 30%, it was determined that the

minimum selling price for the fuel products is US$ 0.99/L. By comparing capital

and operational costs, it was demonstrated that continuous operation mode was

economically more advantageous than semi-batch production.

Techno-economic models for pyrolysis and gasification plants in the same

setting with similar feedstock and capacity were also developed. This was done to

laterally compare the liquefaction plant to thermochemical processes that have

been commercially-developed. Factory models were generated reflecting current

methods in heat and material recovery. The liquefaction plant generated the

highest amount of fuel product per kg feed, followed by pyrolysis, and gasification

had the least. Based on net present values, the profitability of the three plants

were ranked as follows: pyrolysis>liquefaction>gasification. The profitability of the

three plants were all sensitive to product price, thermochemical conversion ratio,

and refining conversion ratio. Conversion ratios also sharply affect the minimum

selling price of products, but the effect is attenuated by high production volumes

in liquefaction. Varying tax rates and capital costs affect the minimum selling price

moderately, but not as much as conversion ratios, therefore, incentives geared

towards improving conversion rates and thermal efficiency were recommended to

increase product volume and decrease the minimum selling price to be

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus vii

competitive. Gasification required more heating due to high temperatures, while

liquefaction required more electrical power due to slurry pumping. The liquefaction

plant emitted more CO2 per kg feed than the rest.

In summary, this research presents the modelling of the liquefaction

process to demonstrate its technical and economic viability in the commercial

scale. Biocrudes are typically challenging to represent in modelling software due

to its complex chemical composition. This thesis demonstrated the utility of ASPEN

Plus in modelling biocrude using distillation curves. The techno-economic models

developed in this research have demonstrated the viability of liquefaction as a

biofuel production process compared with similar thermochemical processes.

Overall, the models can facilitate the development of lignocellulosic biofuels by

providing an approach that is easily adaptable for different locations and

feedstock, or to reflect improved yields and efficiencies as technology progresses.

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus ix

Keywords

ASPEN; Australia; Bagasse; Biocrude; Biocrude Upgrading; Bioenergy;

Biofuels; Comparative Analysis; Distillation; HTL; Hydrothermal Liquefaction;

Lateral Study; Liquefaction; Process Model; Queensland; Sugarcane Bagasse;

Techno-Economic Analysis; Upgrading

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xi

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: ______July 2018________

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xiii

Table of Contents

Abstract ............................................................................................................................... v

Keywords............................................................................................................................ ix

Statement of Original Authorship ....................................................................................... xi

Table of Contents ............................................................................................................. xiii

List of Publications Arising From This Work ...................................................................... xv

List of Figures ................................................................................................................... xvi

List of Tables ................................................................................................................... xviii

List of Abbreviations..........................................................................................................xix

Acknowledgements ...........................................................................................................xxi

Chapter 1: Introduction .................................................................................. 23

1.1 Background ............................................................................................................ 23

1.2 Research Aims ....................................................................................................... 26

1.3 Specific Study Objectives ....................................................................................... 27

1.4 Research Methodology .......................................................................................... 28

1.5 Thesis Structure ..................................................................................................... 34

1.6 Novelty of this work ................................................................................................ 37

1.7 References ............................................................................................................. 38

Chapter 2: Literature Review .......................................................................... 41

2.1 Current Fuel Production and Consumption ............................................................ 42

2.2 Biomass Feedstocks for Conversion to Biofuels .................................................... 44

2.3 Liquefaction ........................................................................................................... 47

2.4 Biocrude ................................................................................................................. 47

2.5 Upgrading ............................................................................................................... 53

2.6 Liquefaction Biofuels at the Larger Scale .............................................................. 54

2.7 Modelling Liquefaction in Different Scales ............................................................ 55

2.8 Techno-economic Studies of Liquefaction Plants .................................................. 59

2.9 Conclusion.............................................................................................................. 60

2.10 References ............................................................................................................. 62

Chapter 3: A review of hydrothermal liquefaction biocrude properties and

prospects for upgrading to transportation fuels ................................................. 69

3.1 Introduction ............................................................................................................ 72

3.2 Biocrude Properties................................................................................................ 75

3.3 Upgrading Processes.............................................................................................. 82

3.4 Challenges and Future Research Prospects .......................................................... 97

xiv Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus

3.5 Author Contributions ............................................................................................ 100

3.6 Conflicts of Interest .............................................................................................. 100

3.7 References and Notes ......................................................................................... 101

Chapter 4: Liquefaction biocrudes and their petroleum crude blends for

processing in conventional distillation units .................................................... 109

4.1 Introduction.......................................................................................................... 112

4.2 Materials and Methods ........................................................................................ 114

4.3 Results and Discussion........................................................................................ 119

4.4 Conclusion ........................................................................................................... 134

4.5 Acknowledgments ................................................................................................ 135

4.6 References ........................................................................................................... 136

Chapter 5: Techno-economic analysis of the thermal liquefaction of sugarcane

bagasse in ethanol to produce liquid fuels ...................................................... 139

5.1 Introduction.......................................................................................................... 142

5.2 Methodology ........................................................................................................ 146


5.4 Conclusion ........................................................................................................... 165

5.5 Acknowledgments ................................................................................................ 165

5.6 References ........................................................................................................... 166

Chapter 6: Comparative techno-economic analysis of biofuel production

through gasification thermal liquefaction and pyrolysis of sugarcane bagasse 173

6.1 Introduction.......................................................................................................... 176

6.2 Methodology ........................................................................................................ 180


6.4 Conclusions.......................................................................................................... 205

6.5 Conflicts of Interest .............................................................................................. 205

6.6 Acknowledgements .............................................................................................. 206

6.7 References ........................................................................................................... 207

Chapter 7: Conclusions ................................................................................ 213

7.1 Conclusions and significance of the results ........................................................ 215

7.2 Recommendations for future research ................................................................ 217

7.3 References ........................................................................................................... 220

Appendices .................................................................................................................... 221

Appendix A: Gas Chromatography Traces ...................................................................... 221

Appendix B: Process Flow Diagrams of the Plants ........................................................ 223

Appendix C: List of compounds used in the liquefaction model .................................... 228

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xv

List of Publications Arising from This Work

Ramirez, J.A., R.J. Brown, and T.J. Rainey, A Review of Hydrothermal Liquefaction

Biocrude Properties and Prospects for Upgrading to Transportation Fuels.

Energies, 2015. Vol 8, Issue 7: p. 6765.

Ramirez, J.A., R.J. Brown, and T.J. Rainey, Liquefaction biocrudes and their

petroleum crude blends for processing in conventional distillation units. Fuel

Processing Technology, 2017. Vol 167: p. 674-683.

Ramirez, J.A., R.J. Brown, and T.J. Rainey, Techno-economic analysis of the

thermal liquefaction of sugarcane bagasse in ethanol to produce liquid fuels.

Applied Energy, 2018, submitted.

Ramirez, J.A. and T.J. Rainey, Comparative techno-economic analysis of the

thermochemical conversion of sugarcane bagasse to produce liquid fuels. 2018

submitted.

xvi Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus

List of Figures

Figure 1.1. Diagrammatic representation of the thesis structure mapping the objectives of the research to publications. ............................................................................................. 34

Figure 2.1. Number of publications with the keyword “liquefaction”, related to the context of this research (Scopus, 24 Nov 2017) plotted with the crude oil price

variations [1] from 1981-2017. ........................................................................................ 41

Figure 2.2. Trends in the number of publications with the keyword “liquefaction”, in specific areas of interest. (Scopus, 24 Nov 2017) .......................................................... 42

Figure 2.3. World final energy consumption by fuel [3]. ................................................................... 43

Figure 2.4. Global biofuel production [1]. .......................................................................................... 44

Figure 2.5. Pressure and temperature regimes of thermochemical processes to convert biomass to liquid biofuels. Adapted from Forde et al. [19] ............................................. 46

Figure 2.6 High heating value of feedstock compared with their corresponding biocrudes

[22, 24, 27, 29-31, 34, 44, 58-60]. ................................................................................. 51

Figure 2.7 Van Krevelen plot of molar H/C and O/C ratios of HTL biocrudes from

lignocellulosic materials, compared with upgraded products and reference fuels [22, 24, 27, 30, 44, 58, 60, 63-66]. ................................................................................ 52

Figure 2.8. Improvements in (a) HHV and (b) oxygen content by upgrading biocrude [65,

66, 78]. ............................................................................................................................... 54

Figure 2.9. Summary of literature estimates of capital costs and product prices from techno-economic analyses of liquefaction plants [95-98, 117-120].............................. 59

Figure 3.1. Thermochemical process conceptual diagram and outline of the article..................... 75

Figure 3.2. Block diagram of upgrading processes discussed in this section. ............................... 83

Figure 3.3. Continuous Binary Fractional Distillation. [84]............................................................... 87

Figure 3.4. Typical reactions in hydrogenation and cracking processes [86]. ................................ 89

Figure 3.5. Microporous molecular structure of ZSM-5. ................................................................... 93

Figure 3.6. Esterification reaction. ..................................................................................................... 95

Figure 4.1. Distillation curves of biocrudes and petroleum crudes, with typical petroleum

fractional distillation temperature ranges shown based on Behrenbruch [29]. .......... 124

Figure 4.2. FTIR spectra of BBA (green), PCRU (red) and BIOBL-COOL (blue) ............................... 126

Figure 4.3. FTIR spectra of BIOBL-COOL sampled from two sections: top (green) and

bottom (blue) .................................................................................................................... 127

Figure 4.4. FTIR spectra of BIOBL-WARM sampled after two different cooling times................... 128

Figure 4.5. Distillation curves of biocrude-petroleum crude blends composed at different

temperatures .................................................................................................................... 130

Figure 4.6. Distillation curves of (a) the petroleum crude blend and (b) biocrude-petroleum crude blends with varying blend ratio ............................................................................. 131

Figure 5.1. Block diagram of the liquefaction plant modelled in this study. ................................. 147

Figure 5.2. Van Krevelen diagram of bagasse, biocrude, HDO biocrude and light and heavy fractions presented in molar O/C and H/C ratios. ......................................................... 153

Figure 5.3. Breakdown of the liquefaction plant operating costs. ................................................. 156

Figure 5.4. Sensitivity of plant NPV to changes in technical and economic parameters

within a ± 50% base case value range. Base NPV is US$ -96 Million. Delta NPV is the difference of the NPV calculated using the range of values for each

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xvii

parameter to the base NPV. Red bars indicate result from low (-50%) values and blue bars represent result from high (+50%) values. White lines indicate result

from ±10% values. ........................................................................................................... 160

Figure 5.5. MSP values for varying liquefaction process parameters............................................ 162

Figure 5.6. MSP values for varying economic parameters. ............................................................ 163

Figure 5.7. Profitable (green) and non-profitable (red) operating regions separated by the

MSP line (blue), for varying HDO conversion. ................................................................. 164

Figure 6.1. Van Krevelen diagram for the feedstock (orange dot), intermediate products

(ring) and refined products (dot) of the processes in this study. Arrows show progression through gasification (green), liquefaction (blue) and pyrolysis

(purple). ............................................................................................................................. 191

Figure 6.2. Distillation curves of the products of the thermochemical processes in this study, compared with the distillation curve of crude oil obtained from [84]. ............... 193

Figure 6.3. Operating cost breakdown of each plant, compared to annual revenue

generated. Revenue was calculated using a unit price calculated from the crude oil price.............................................................................................................................. 196

Figure 6.4. The sensitivity of NPV to prospective changes in capital costs. .................................. 198

Figure 6.5. Sensitivity of the NPV to selected process and economic variables (±50%). Green bars represent gasification, blue bars represent liquefaction and purple

bars represent pyrolysis values. Change in NPV values were derived from the base case value for each process. .................................................................................. 199

Figure 6.6. Effect of varying thermochemical conversion (circles, solid lines) and refining

conversion (triangles, dashed lines) to the minimum selling price of the product....... 202

Figure 6.7. Effect of varying feedstock price (triangles, solid lines), natural gas price (circles) and hydrogen price (diamonds, dashed lines) to the minimum selling

price of the product. ......................................................................................................... 203

Figure 6.8. The effect of varying tax rate (solid line) and capital costs (dashed line) to the

minimum selling price for gasification (green), liquefaction (blue) and pyrolysis (purple). Zero percent in capital costs refers to the base case cost. Base case tax

rate is 30%. ....................................................................................................................... 204

Figure A1. GC Trace of BB biocrude. ................................................................................................ 221

Figure A2. GC Trace of PCRU ............................................................................................................ 221

Figure A3. GC Trace of BIOBL-COOL blend....................................................................................... 221

Figure B1: Liquefaction Plant described in Chapter 5. ................................................................... 224

Figure B2: Liquefaction Plant described in Chapter 6. ................................................................... 225

Figure B3: Gasification Plant described in Chapter 6. .................................................................... 226

Figure B4: Pyrolysis Plant described in Chapter 6. ......................................................................... 227

xviii Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus

List of Tables

Table 2.1. Physical properties of biocrude from various feedstock and comparison with diesel and biodiesel. .......................................................................................................... 49

Table 2.2. Operational liquefaction demonstration plants [81]. ...................................................... 55

Table 3.1. Biocrude produced from various feedstock and their properties................................... 77

Table 3.2. Groups of chemicals of hydrothermal liquefaction biocrude. ......................................... 80

Table 3.3. Properties of various chemical groups and their effect on diesel properties [62]. ....... 81

Table 3.4. Yields of solvent extraction of HTL biocrude from the liquid fraction using polar

and non-polar solvents. ..................................................................................................... 85

Table 3.5. Activation energies, iso-reactive temperature and hydrogen consumption of hydrodeoxygenation of model compounds with a CoMo catalyst, presented by

Grange et al. [92]. .............................................................................................................. 91

Table 3.6. Upgrading processes and their effect on physical and chemical properties.

Direct influence of processes to biocrude property towards standard values. .............. 97

Table 4.1. Description of biocrudes and petroleum crudes ........................................................... 115

Table 4.2. Description of blends ...................................................................................................... 116

Table 4.3a. Composition of biocrudes, listed in relative abundance within the group ................. 119

Table 4.3b. Composition of petroleum crude and biocrude-petroleum crude blend, listed in

relative abundance within the group .............................................................................. 122

Table 5.1. Pilot-scale biomass liquefaction plants in operation. ................................................... 143

Table 5.2. Quantities used in base case model of the liquefaction plant. .................................... 151

Table 5.3. Mass and energy balance results from the liquefaction process model. .................... 152

Table 5.4. Adjusted price for products modelled in this study. ...................................................... 154

Table 5.6. Comparison of semi-batch and continuous operating modes. ..................................... 158

Table 6.1. Comparison of thermochemical processes from [13-16]. ............................................ 178

Table 6.2. Properties of sugarcane bagasse used as feedstock in this study. ............................. 181

Table 6.3. Modelling data used in the gasification model.............................................................. 183

Table 6.4. Modelling data used in the liquefaction model. ............................................................ 184

Table 6.5. Modelling data used in the pyrolysis model. ................................................................. 186

Table 6.6. Economic modelling data used in this study. ................................................................ 187

Table 6.7. Process model results for the three plants. ................................................................... 188

Table 6.8. Economic results of the modelling of gasification, liquefaction and pyrolysis processes.......................................................................................................................... 194

Table C1: Compounds used in the modeling liquefaction biocrude and HDO biocrude

streams. ............................................................................................................................ 228

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xix

List of Abbreviations

ADA Assay Data Analysis

ASTM American Standard Testing Of Materials

CARF Central Analytical Research Facility

CEPCI Chemical Engineering Plant Cost Index

CHP Combined Heat And Power

CN Cetane Number

CPI Consumer Price Index

DCM Dichloromethane

EROI Energy Return On Investment

FCI Fixed Capital Investment

FID Flame Ionisation Detector

FT Fischer-Tropsch

FTICR Fourier Transform Ion Cyclotron Resonance

GC Gas Chromatography

GDP Gross Domestic Product

GGE Gallons Gasoline Equivalent

GHG Greenhouse Gases

GREET Greenhouse Gases Regulated Emissions and Energy Use in Transportation

HDN Hydrodenitrogenation

HDO Hydrodeoxygenation

HDS Hydrodesulphurisation

HHV Higher Heating Value

HTL Hydrothermal Liquefaction

IBP Initial Boiling Point

IEA International Energy Agency

IPCC Intergovernmental Panel For Climate Change

IRR Internal Rate Of Return

LDE Litres Diesel Equivalent

LEA Lipid Extracted Algae

LGE Litres Gasoline Equivalent

LHV Lower Heating Value

MDEA Methyldiethanolanime

MFSP Minimum Fuel Selling Price

MS Mass Spectrometry

MSP Minimum Selling Price

NIST National Institute Of Standards And Technology

NMR Nuclear Magnetic Resonance

NOx Nitrogen Oxides

NPV Net Present Value

PM Particulate Matter Mass

PN Particulate Number

SOT State-Of-Technology

SRDP Standard Reference Data Program

TBP True Boiling Point

TOFMS Time-Of-Flight Mass Spectrometry

UHC Unburnt Hydrocarbon

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus xxi

Acknowledgements

I have the greatest gratitude to my principal supervisor, Dr Thomas Rainey

for the invaluable support and mentorship. I can reasonably say that he has

believed in my abilities more than I have. His support has enabled me to push

through, starting as a Masters by Research student, articulating to a PhD, getting

awarded a scholarship, and finally completing this thesis. His patience throughout

the difficulties around the reactor, the lab, the fridge, and the elusive distillation

column has instilled in me an appreciation for a temperament that is necessary to

thrive in academia.

The help of Professor Richard Brown, my associate supervisor, is also much

appreciated. His encouragement and insights in experiments, articles and

conference abstracts were valuable.

Special thanks are needed for Dr Jana Adamovska, who took me under her

wing when I started this research. I was more than happy to help with her work and

she did not mind teaching me most of what I know about liquefaction. I could have

not hoped for a better colleague.

I would also like to thank the technical staff in the Chemistry laboratories

and CARF, especially Dr Lauren Butler, Peter Hegarty, Shane Russell, Joel Herring

and Michael Bongard, for helping me with learning and using the equipment I

needed for my analyses. Many thanks too to my colleagues Farah, Mutah, Maryam,

and Farhad for all the inputs and support. To everyone in I’ve been acquainted to

in QUT who never hesitated to share a smile, say hello and ask “how are you?”,

even if it was difficult to reciprocate during tougher times, thanks.

To my friends in the Philippines, and wherever else, thank you for sending

your encouragement through the aether. Thank you also to my family and my

Brisbane family for your love and encouragement. To my partner Kris, thank you

for being patient and supporting me even if I’m too grumpy to ask for it sometimes.

There isn’t enough success to match the joy you give me every single day.

Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus 23

Chapter 1: Introduction

1.1 Background

The increasing levels of anthropogenic carbon dioxide (CO2) affecting the climate

[1], declining fossil fuel supply [2], and the numerous socio-economic cruces around

energy [3] are compelling reasons to pursue various alternative energy technologies

and examine their suitability to substitute or augment conventional energy sources

currently in use.

There is a consensus in most scientific studies that human activity has been

determined as a major influence on the increasing amount of CO2 in the atmosphere

[4]. In particular, burning fossil fuels for transportation accounts for 23% of total CO2

emitted [5]. The use of renewable energy, particularly energy derived from biomass,

has been demonstrated as a key mitigating measure to reduce greenhouse gas (GHG)

emissions [6, 7].

Petroleum crude oil supply has been known to be sensitive to factors such as

political instability and declining production due to the industry’s heavy reliance on

international trade [8]. This presents vulnerabilities to long-term supply disruptions

and spikes in crude oil price. To mitigate these risks, it has been proposed that the

vulnerabilities be managed by decreasing dependency on oil and gas, and diversifying

the fuel mix in the transport sector, among others [9]. The development of domestic

renewable energy production sites and encouraging fuel diversity have been shown

to improve energy security [10]. One such measure is to shift liquid fuel production to

biofuels. For instance, other than a lower net energy required in production compared

with fossil fuels, the greenhouse gas emissions were also lower for bioethanol, and

could be further decreased with the development of processes that use cellulosic

feedstock [11]. The low biomass price compared to crude oil, increased employment,

and other positive macroeconomic effects have been suggested as economic benefits

of shifting from petroleum fuels to biofuels [12].

The use of biomass for energy benefits from its ubiquity in almost any region in the

world. Depending on climatic conditions, total plant biomass is distributed from

650 g/m2 in an Arctic tundra to 38,800 g/m2 in tropical forests [13]. The global

24 Modelling a Commercial-Scale Bagasse Liquefaction Plant Using ASPEN Plus

terrestrial plant net primary productivity was estimated to be 54.3 Gt/y, spread across

all continents [14]. In contrast, petroleum reserves are largely concentrated in a few

countries. Middle Eastern countries control 69.9% of reserves as of 2016, while their

consumption is only 6.7%. On the other hand, the Asia Pacific region has 2.8% of

global proved reserves but consume 42% of total fossil fuel supply [15]. Nonetheless,

despite the abundance of biomass resources and the advantages of using biofuels,

there is still a large dependence on fossil fuels to provide energy.

For instance, in Australia, in 2015-16, fossil fuel oil products contributed to 37% in

the total energy mix. Consumption of fossil fuels, including liquefied petroleum gas

and natural gas comprise 99.5% of road transport consumption [16]. Due to

increasing energy demand, Australia imported around 33 billion L of refined

petroleum products, which was 55% of the consumption in the same period [17]. This

purports a reliance on foreign supply and a greater sensitivity to changes in the global

market. In contrast, biofuels make up only 0.1% of the total energy mix, and while

solid biomass has a larger share of 3.3%, it is only used for electricity generation and

heating. Meanwhile, around 80 Mt/y of lignocellulosic feedstock is available for

biofuel production in order to substitute even a portion of fossil fuel consumption with

less carbon-intensive biofuels [18].

The use of biomass for energy is not an unfamiliar concept and solid biomass has

been widely used as a reliable fuel; however, fuels from petroleum have relatively

higher energy density and are easier to transport in liquid or gaseous forms. Despite

the ongoing preference for petroleum-based liquid and gaseous fuels, the energy

content of biomass has not been overlooked. Around 10% of the current global energy

supply comes from biomass [19], mostly as solid fuels for cooking and heating, but

only contributed 2.8% of the global transport fuel mix in 2015 [20], albeit growing at

an average of 9.7% annually [21]. For biomass to be viable as a transport fuel source,

the physical and chemical properties of biofuels should match conventional fuels

used by existing systems.

Another important consideration in bioenergy is that the biomass used in

producing biofuels are not in competition with food as not to affect supply [22]. Biofuel

research has evolved from production of liquid and gaseous fuels from food crops to

conversion of non-food lignocellulosic materials and microalgae to fuel. The selection


of feedstock depends heavily on availability in the region and transportation to

processing centres, while selection of an appropriate conversion technology will

depend on feedstock composition, moisture content, desired products, product yield,

and process efficiency, just to name a few.

Among various biofuel production technologies, thermochemical processes have

found its way in novel pathways to convert lignocellulosic materials to fuels, thereby

avoiding the food versus fuel debate. These processes operating in high temperatures

provide the energy to break polymeric structures of cellulose, hemicellulose and lignin

and carry out pyrolytic reactions. As biomass rapidly heats up in the process, light

components are vapourised, large molecules are cracked to smaller molecules, and

then react with each other to form components of various chemical moieties [23].

Higher temperatures produce permanent gases such as CO2, CO, H2 and CH4 and

heavy polyaromatic hydrocarbons that make up soot, while higher pressures promote

formation of liquid products [23]. The processes can be feedstock-agnostic, requiring

only minor modifications to accommodate varying feedstock conditions and produce

consistent output. Thermochemical processes also allow conversion of the whole

biomass rather than obtaining a fraction that would be appropriate for the conversion

process, such as in fermentation of sugars or oil extraction from plants or algae. Both

the indifference to the type and composition of the feedstock and the larger overall

conversions allow thermochemical processes to drive seamless integration of

biofuels with a high-demand fuel market.

Thermochemical processes are neither new nor esoteric, as they have been used

in various applications throughout history. Pyrolysis, for instance, has been used as

far back as the ancient Egyptian times [24]. Years of improvements and research

have enabled the development of various process mechanisms and configurations to

achieve the desired products. For instance, fast pyrolysis has been preferred over slow

pyrolysis to maximise the yield of liquid products by using a high heating rate [25].

This process requires pre-drying of the biomass feedstock to reduce the amount of

water in the product [24]. Hydrothermal liquefaction (HTL), on the other hand, was

developed to carry out pyrolytic reactions at higher pressures to further encourage

liquid production with better quality [26]. The process does not require pre-drying of

feedstock, rather, moisture was a desired component due to the solvolytic effects of

water at high temperatures and pressures that enable more effective conversion of


biomass to products [27]. This vital characteristic of HTL enabled production of a

thermochemical liquid product, biocrude, from virtually any biomass feedstock, or a

mixture of feedstocks with high throughput and energy efficiency [28].

A problem in the use of biocrude as a biofuel is its suitability for use in internal

combustion engines. Compared with fossil fuels such as diesel, biocrudes have higher

viscosity, lower energy content, higher oxygen content and can be less chemically-

stable [29]. Therefore there is a considerable amount of work required to develop

processes that upgrade biocrudes to match fossil fuel properties to facilitate direct

use of biofuels without modifications to existing engine design.

There is no shortage of laboratory scale investigations in liquefaction and research

in biocrude upgrading is increasing; however, the emerging challenge is to scale up

these processes and demonstrate its ability to reliably produce biofuels in the

commercial scale.

1.2 Research Aims

This research aims to develop a pathway to demonstrate a commercial-scale

biomass liquefaction plant that produces biofuels. Prospective biocrude upgrading

processes were reviewed to determine suitability of these processes to meet the

physical, chemical and fuel properties of conventional fuels. The use of ASPEN Plus

to model biocrude was investigated by using assay data (i.e. distillation curves and

density) to simulate biocrude and biocrude blends with petroleum crude oil. A process

model was developed in ASPEN Plus for a Queensland-located liquefaction plant

using sugarcane bagasse as feedstock and was compared with models of pyrolysis

and gasification plants with similar economic parameters. Both technical and

economic results were analysed and prospective opportunities were uncovered from

the sensitivity analyses of profitability.

The outcomes of this study can facilitate the modelling of biomass liquefaction

plants and expedite the development of business cases for investments in

commercialisation of the liquefaction process. Moreover, it presents information

about the critical technological, economic and policy areas that may hinder future

development of liquefaction plants.


1.3 Specific Study Objectives

The specific study objectives were the following:

1. Determination of prospective upgrading processes for liquefaction biocrudes

by reviewing its key physical and chemical properties, the effect of these

properties on engine performance and emissions, and technologies for

upgrading biocrudes to achieve desirable fuel properties.

2. Determination of the practicability of blending biocrude with petroleum crude

oil for co-processing by analyzing their miscibility and the effect of temperature

and varying blend ratios. Determination of the suitability of modelling biocrudes

and biocrude blends in ASPEN Plus to facilitate development of process

models. Determination of the suitability of using physical and chemical analysis

data such as gas chromatography-mass spectrometry (GC-MS) results and

simulated distillation (ASTM D 2887) distillation curves to model biocrudes.

3. Modelling of a biofuel production liquefaction plant set in Queensland,

Australia using ASPEN Plus. Locally-available sugarcane bagasse was used as

feedstock and ethanol was used as liquefaction solvent. Comparative analysis

of the liquefaction plant with pyrolysis and gasification plants using ASPEN

models, including the analysis of profitability and sensitivity to changing

parameters.


1.4 Research Methodology

The methods used in this research are briefly discussed in this section.

Materials and procedures are described with context and additional detail in the

succeeding chapters.

1.4.1 Review of Upgrading Processes

The properties of biocrude of different kinds were examined and related to their

effect to fuel properties and emissions. A variety of upgrading processes were looked

into drawing from petroleum refining and pyrolysis upgrading analogues. The recent

results of experimental studies in upgrading liquefaction biocrude and pyrolysis bio-

oil were discussed. Important issues such as economics and sustainability were also

considered. The discussion of biocrude and fuel properties and the upgrading

processes were summarised in a table that maps the processes with the properties

they upgrade in biocrude.

1.4.2 Liquefaction and Product Separation

A number of liquefaction tests were conducted in this research. All tests involved

the liquefaction of sugarcane bagasse sourced from the Wilmar Invicta sugar factory

in Townsville, Queensland, Australia in a 1.8 L Parr reactor. An initial 10 bar N2

pressure was used for all liquefaction runs to minimise the effect of oxygen in the

process. A solvent composed of 95% ethanol- 5% water was used in the reaction at a

1:19 biomass-to-solid ratio. The reactor was heated to 300 °C and maintained at that

temperature for 30 minutes. Following the reaction time, the reactor was then cooled

down by heat exchange with a cooling water coil. At room temperature, the reactor

headspace was purged and the solid and liquid contents of the reactor were removed

and weighed. Known amounts of ethanol was used to rinse the reactor to maximise

the removal of the products from the reactor.

Product separation occurs in two steps. First, the solid and liquid products were

separated through filtration. The product mixture was filtered through a pre-weighed

Whatman’s no. 5 filter paper on a Büchner funnel under vacuum to separate the solid

products. Known quantities of ethanol were used to rinse the wet solids to maximise

the separation of biocrude and solids. The wet solids were then weighed with the filter

paper, and the wet solid weight was calculated by difference. The collected filtrate

was also weighed. The wet solids were dried in an oven at 105 °C for 24 hours. The


dried solids were weighed and the liquid retained with the solids after filtration was

calculated by difference. The second separation step was to separate the solvent from

the biocrude. This was done in a rotary evaporator under vacuum at 40 °C, after pre-

weighing the empty flasks and samples. The evaporated ethanol was collected and

the remaining biocrude in the sample flask was weighed to determine the biocrude

yield. The recovered ethanol yield was also calculated. The mass balance of the

process was then calculated, accounting for additions of ethanol post-reaction, with

the difference assumed to be the gas phase, as is common in liquefaction studies.

1.4.3 Gas Chromatography-Mass Spectrometry (GCMS)

Biocrude composition was analysed through GCMS. This is a typical

qualitative characterisation method for liquefaction products. Petroleum crude

and biocrude-petroleum crude blends were also analysed through GCMS in this

research. The samples were prepared by pipetting around 10 µL of biocrude into

a headspace sampling bottle. The amount of sample in the bottle does not need

to be accurately measured, with only due consideration into ensuring the

headspace vapours sampled will not be oversaturated. The bottles were then

arranged on the vial tray and the testing sequence was programmed for

automated incubation and sampling by a TriPlus RSH Autosampler. For each

sample, the bottle was individually heated and agitated in the incubation oven to

ensure homogeneous mixing of vapours prior to headspace vapour sampling. The

headspace vapours were then injected with a 75:1 split ratio into the

ThermoScientific ISQ Trace 1310 GC equipped with a single quadrupole mass

selective detector. The incubation procedure and GC method used was

determined as optimum for the sample following several GC experiments informed

by previous work. The resulting spectra of peaks from the chromatogram were

electronically compared with spectra from the US National Institute of Standard

and Technology (NIST) Mass Spectral Search Program and Library System to

identify components.

1.4.4 Simulated Distillation

Simulated distillation of biocrude, petroleum crude and biocrude-petroleum

crude blends were conducted in this research. The method used follows ASTM D

2887 (Standard Test Method for Boiling Range Distribution of Petroleum Fractions

by Gas Chromatography). The samples were prepared by diluting in


dichloromethane with a dilution factor of 100 to reduce viscosity. Low viscosity

samples were able to be injected into the GC neat. A calibration curve was

prepared by running the ASTM D 2887 Calibration Mixture in the Agilent HP 6890

GC equipped with a flame ionisation detector (GC-FID). The method used for the

calibration mixture and subsequent sample analysis was chosen among several

tests to optimise the chromatograms generated by the GC and allow insightful

analysis. The calibration curve and the equations given in ASTM D 2887 to

interpolate points were used to generate the distillation curves of samples.

1.4.5 Blending

Blends of biocrudes and petroleum crude were prepared to assess the

viability of having a blend as input into the distillation process. Blends were

prepared by mixing biocrude and petroleum crude in a sealed vial using a vortex

stirrer. Blending temperatures above ambient were achieved by immersing the vial

in a water bath at the set temperature. The proportions of biocrude and petroleum

crude were also varied for some blends.

1.4.6 Process Modelling

Process models were developed using data from literature and stock unit

operation blocks in ASPEN Plus, which is a commercial process modelling software

that has wide application in various industries. The process model was developed

to generate mass and energy flows from specified relationships given by user-

defined product yields, stoichiometry, and equations-of-state, automatically

calculated by the software. The use of the software relies on the quality of data

entered into the stock “blocks” of unit operations and “streams” of mass and

energy, with the simulation machine facilitating the copious iterative calculations

within and around the modelled plant.

The process model was developed by first selecting material components

from the database that will compose the streams defined in the model. ASPEN

Plus usually contains adequate physical and thermodynamic data to accurately

calculate or estimate a derived quantity for use in calculations. These data are

drawn from National Institute of Standards and Technology (NIST) Standard

Reference Data Program (SRDP) [30]. In the absence of the required data, the

information was manually entered in ASPEN Plus from literature sources. Non-

conventional components, such as biomass, were defined using ultimate and


proximate analyses, as required in the software. Appropriate thermodynamic

methods were also selected for quantity estimation.

The next step in process modelling is building the process plant flowsheet.

This was done by selecting unit operation blocks and joining them with material

and energy streams. Input streams are then defined with material composition,

temperature, and pressure or other quantities such as vapour fraction, as

specified by the degrees of freedom, which ASPEN automatically determines. The

range of unit operation blocks varied from simple to complex in modelling the

processes. An appropriate level of complexity was selected for representing a unit

operation or process, in consideration of available data and the accuracy of

calculating a result. For instance, a separation can be simply modelled as a “Sep”

block where the ratio at which a component separates into two or more streams

can be specified by the modeller. A block with higher complexity such as “Flash2”

can calculate the separation by phase from specified temperature, pressure,

vapour fraction or heat duty using thermodynamic equations of state. For

reactions, a “RYield” block can represent a reactor simply, requiring only

specifications of temperature, pressure and product yields, which ASPEN uses to

calculate the amount of products, initial and final enthalpy, and the resulting heat

duty or temperature of outgoing streams. A more complex reactor block would be

“RStoic” where the stoichiometry of reactions occurring in the reactor is specified,

and the amount of products are determined based on the availability of reactants

participating in the specified reactions. For more established processes with a

wealth of literature supporting a complex model, the more sophisticated blocks

were chosen, while for the relatively novel processes, such as the liquefaction

reactor, simpler blocks were used.

Following the construction of the flowsheet, considerable effort was

undertaken to ensure the connectivity of the blocks were correct, and that

expected outcomes were achieved. For instance, for a pyrolysis vapour quench

process, the goal was to cool the vapours from the pyrolysis reactor using cooled

bio-oil. To achieve this, the contact tower (“RadFrac” block in ASPEN) was

configured to ensure the vapours have adequate contact with the cascading liquid

for cooling, and the amount of condensable vapours in the gas outlet on top of the

tower was minimised. Iterative calculations were conducted in ASPEN by varying

the settings until this was achieved. A similar approach was used in ensuring


maximum recovery of ethanol from the liquefaction product streams, or maximum

H2S and CO2 removal from syngas in the gasification model, among others. Some

straightforward calculations, such as biomass drying was modelled with a

“Calculator” block that enabled specification of a resulting property (in this case,

moisture content) by calculating the extent of drying to achieve the target.

ASPEN also calculated the convergence of the material and heat flows to

check the mass and energy balance of the model. The models were deemed

complete when the outputs for all streams and blocks were generated and the

model has “converged”. Heat recovery through a network of heat exchangers were

also included in the process models in order to optimise energy use and manage

operating costs. Furthermore, values in the process model was able to be varied

for sensitivity analyses using ASPEN’s “Sensitivity” function. A range of values with

fixed intervals or a set of user-defined values can be used to vary one or many

independent variables, and the resulting value one or many dependent variables

can be selected to be tabulated.

1.4.7 Economic Modelling

The results of the process model were used as inputs to the economic model.

Input flows determined operational costs and product flows were used to calculate

revenue by multiplying annual flows with unit price and the annual hours of

operation. Unit prices were taken from real-world values estimated as an average

value over 12 months when possible. Other operational costs such as trade waste

(waste water) handling and labour were also calculated using local rates. To

rigorously determine equipment size and cost, mass and energy flows were used

to size equipment using conventional chemical engineering methods. For example,

for a separation tank, both liquid and vapour volumes were calculated for a fixed

retention time. A factor of safety was also added in anticipation of varying

operating conditions. The resulting tank volume was then used to calculate cost.

Equipment costs were determined using the calculated equipment size using

Matche, which is an industry process equipment cost database, or cost estimation

figures in chemical engineering textbooks. Some vendor-sourced cost information

was also used. Equipment costs were scaled using a power law scaling factor if

the cost reference was for a different scale. Costs estimated using values from

previous years were converted to 2017 values using the Chemical Engineering


Plant Cost Index (CEPCI). Total direct capital costs were used to estimate installed

costs and indirect costs using factors for a solid-liquid plant [31]. Working capital

was also estimated similarly, as a factor of fixed capital investment. The cost

estimation techniques employed are appropriately rigorous for a feasibil ity study

stage and a variation of 30-50% can be expected.

The costs and revenue are entered into the cash flow spreadsheet where the

annual cash flow was calculated. The capital costs were entered as a lump sum

cost in Year 0. The earnings before interest, depreciation, taxes and amortisations

(EBITDA) was calculated by subtracting the operational costs from revenues. A

constant depreciation amount was calculated over the plant life of 20 years. This

was taken off from the EBITDA. After depreciation has been accounted for, the tax

was calculated using the corporate tax rate, and the cash flow after tax was

determined. The present value of the yearly taxed cash flows (ATCF) are summed

to calculate the net present value (NPV), given by Equation 1.1, where CAPEX is

the capital cost, r is the discount rate of 10% and n is the plant life of 20 years.

(Eq. 1.1)

The internal rate of return (IRR) and minimum selling price (MSP) were also

calculated when numerically possible. The IRR is defined as the value of the rate

r when NPV is zero in Equation 1. On the other hand, the minimum selling price is

the value of the product price when NPV is zero without changing the value of the

discount rate. Together with cost and revenue calculations, the economic

indicators of NPV, IRR and MSP describe the economic feasibility and investment

viability of developing a liquefaction biofuel plant, and the affordability of the

biocrude-based fuels.

1.4.8 Sensitivity Analysis

Using the process and economic models, the values of critical parameters

were varied to demonstrate the effect of these variations on the net present value

of the plant and the minimum selling price of the products. The range of values for

the parameters were ±10% for small changes and ±50% for large changes. The

small changes illustrate the range at which the operation of the plant varies, while

𝑁𝑃𝑉 = 𝐶𝐴𝑃𝐸𝑋 + ∑ 𝐴𝑇𝐶𝐹 (1 + 𝑟)−𝑖

𝑛

𝑖=1


the large changes are more for anticipating the variation in estimations and the

effect of some assumptions and simplifications.

1.5 Thesis Structure

The following presents the organisation of this dissertation by mapping the

objectives (in parentheses) with publications and main chapters that constitute this

work.

Figure 1.1. Diagrammatic representation of the thesis structure mapping the objectives of the

research to publications.

Chapter 1&2: Introduction and Literature Review

•Publication: A review of hydrothermal liquefaction biocrude properties and prospects for upgrading to transportation fuels

• Published in Energies, July 2015

Chapter 3: Determination of prospective upgrading

processes for liquefaction biocrudes (Obj 1)

•Publication: Liquefaction biocrudes and their petroleum crude blends for processing in conventional distillation units

• Published in Fuel Processing Technology, December 2017

Chapter 4: Determination of the practicability to blend biocrude with petroleum

crude oil and modelling of biocrudes and blends in

ASPEN Plus (Obj 2)

•Publication: Techno-economic analysis of the thermal liquefaction of sugarcane bagasse in ethanol to produce liquid fuels

•Published in Applied Energy, 2018

Chapter 5: Modelling of a biofuel production

liquefaction plant (Obj 3)

•Publication: Comparative techno-economic analysis of biofuel production through gasification, thermal liquefaction and pyrolysis of sugarcane bagasse

•Submitted to Bioresource Technology

Chapter 6: Modelling sugarcane bagasse pyrolysis

and gasification and comparing them with the liquefaction plant model

(Obj 3)

Chapter 7: Conclusions


Chapter 3 presents a review of the properties of biocrude and processes that

can potentially upgrade it. It is important to have an understanding of how the

physical and chemical properties of biocrude relate to fuel properties. The effects

of these properties on internal combustion engine performance and emissions

were discussed to appropriately discern the importance of upgrading biocrude.

The link of biocrude properties to fuel properties has not been articulated in other

studies. Biocrudes have properties that require upgrading to meet fuel standards.

A discussion of prospective upgrading pathways such as distillation and

hydrodeoxygenation were examined based on the results of applying these

processes in upgrading pyrolysis bio-oil, which have similar physical and chemical

properties. Studies of liquefaction biocrude upgrading were also included.

Challenges and future prospects in biocrude upgrading were discussed, and a

summary of upgrading processes linked to the properties they improve was

provided. This chapter was published in the journal Energies as a review article

titled “A review of hydrothermal liquefaction biocrude properties and prospects for

upgrading to transportation fuels”.

Chapter 4 describes the distillation behaviour of biocrudes, which was

expressed through the boiling point distribution curves. As one of the upgrading

processes discussed in Chapter 3, distillation was identified as a potential drop-in

point for co-processing biocrudes in conventional refineries. It was therefore

important to analyse the viability of blending biocrudes with petroleum crude oil.

The distillation curves of biocrudes were measured through simulated distillation.

These distillation curves are physical measurements and were used to compare

the biocrudes with petroleum crude oil and condensate. Blends of bagasse

biocrude with petroleum crude oil were prepared in different blending

temperatures and blend ratios and their miscibility was analysed by observing the

features of their Fourier transform infrared (FTIR) spectra. The distillation curves

of the blends were also measured and compared with the modelled curves in

ASPEN Plus to validate the models. It was determined that ASPEN can adequately

represent biocrudes when supplied with assay data and can sufficiently predict

the blend assay. This chapter was published in the journal Fuel Processing

Technology as an article titled “Liquefaction biocrudes and their petroleum crude

blends for processing in conventional distillation units”.


Chapter 5 presents a model of a thermal liquefaction plant using sugarcane

bagasse as feedstock and ethanol as solvent. It was determined that a 10 t/h

biomass (as received) liquefaction plant in Queensland, Australia can produce

gasoline and diesel-like fuels at an average price of US$ 0.99/L to achieve

minimum profitability (net present value is zero). This was in consonance with past

hydrothermal liquefaction studies. This suggests the feasibility of liquefaction

using ethanol as solvent. The net present value (NPV) was most sensitive to

product price, biocrude yield and hydrodeoxygenation (HDO) conversion ratio. The

minimum selling price of the product can be reduced if state-of-technology

biocrude yield and HDO conversion are improved. Chapter 5 was submitted to the

journal Applied Energy as an article titled “Techno-economic analysis of the

thermal liquefaction of sugarcane bagasse in ethanol to produce liquid fuels” and

is currently under review.

Chapter 6 situates the relatively novel liquefaction process among its

thermochemical peers. With the same techno-economic approach, the profitability

of a liquefaction plant was compared with that of gasification and pyrolysis.

Process models were developed for all three plants with the same bagasse

processing capacity of 10 t/h (as received). The three plants were designed to

produce a crude oil-like product from bagasse through the respective

thermochemical processes and their relevant refining and upgrading processes.

The NPV was calculated for the three cases and is ranked as follows:

pyrolysis>liquefaction>gasification. The advantages and drawbacks of each

process as it relates to product yield, operational and capital costs were discussed.

It was determined that the MSP for gasification was US$ 1.94/L, for liquefaction,

US$ 0.98/L, and for pyrolysis, US$ 1.19/L. The NPV of all plants were most

sensitive to product price and thermochemical conversion. Gasification, which

required more heating was sensitive to natural gas price. All plants were sensitive

to feedstock price at the same rate. The MSP of the products from the liquefaction

plant was least affected by varying process and economic parameters. Changes in

capital costs and tax rates affected the MSP only slightly; however,

thermochemical yield and Fischer-Tropsch conversion affected the gasification

products heavily. The study suggests incentives should target improvements in

conversion rates as well as efficiency in energy and chemical use. This chapter

was submitted to the journal Bioresource Technology as an article titled


“Comparative techno-economic analysis of biofuel production through

gasification, thermal liquefaction and pyrolysis of sugarcane bagasse” and is

currently pending review.

1.6 Novelty of this work

There are numerous studies in biocrude production, but only a few on

upgrading biocrude and the use of biofuels produced through liquefaction. In

contrast, research around process modelling and techno-economics has been very

sparse. This research has identified that gap and pursued a modelling pathway to

demonstrate the technical and economic viability of liquefaction.

Drawing from petroleum refining, distillation was identified as a unit

operation where biocrude can be potentially blended with petroleum crude oil or

processed on its own. Previously, there has been no research around representing

biocrudes in ASPEN Plus using simulated distillation curves. In this research, the

models of biocrude and petroleum crude blending in ASPEN were verified

experimentally. This leads the way to future modelling of biocrude upgrading

processes.

Compared with the few liquefaction models that use wood feedstock and

water as liquefying solvent, emerging liquefaction technologies such as the use of

sugarcane bagasse and ethanol, a renewable organic solvent, were incorporated

into the model of a novel liquefaction plant. This allowed for costs related to a

waste feedstock, solvent recovery, and recycle to be analysed. Moreover, the

comparative analysis of liquefaction, gasification and pyrolysis was done to

provide a holistic view of the viability of the liquefaction process. This research

purports that using the same basis provides a more meaningful comparison

between the three processes. The key differences in presented were based on

process models and enabled an analysis of the effect on profitability and biofuel

affordability of technical, economic, and policy parameters. This is in contrast to

other comparisons using mostly cost data from literature with some scaling and

adjustment. Prospective measures to make fuels from liquefaction more

commercially competitive were also examined.


1.7 References

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Recent Global CO2 2017 [cited 2017 September 8, 2017]; Available

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2. Chapman, I., The end of Peak Oil? Why this topic is still relevant despite

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3. Sovacool, B.K. and M.H. Dworkin, Energy justice: Conceptual insights and

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4. Cook, J., et al., Quantifying the consensus on anthropogenic global

warming in the scientific literature. Environmental Research Letters,

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5. International Energy Agency. CO2 Emissions from Fuel Combustion

Highlights. 2014: Paris, FR.

6. Siemers, W., Greenhouse gas balance for electricity production from

biomass resources in Thailand. Journal of Sustainable Energy &

Environment, 2010. 1: p. 65-70.

7. Cherubini, F., et al., Energy- and greenhouse gas-based LCA of biofuel and

bioenergy systems: Key issues, ranges and recommendations. Resources,

Conservation and Recycling, 2009. 53(8): p. 434-447.

8. Gupta, E., Oil vulnerability index of oil-importing countries. Energy Policy,

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9. Gnansounou, E., Assessing the energy vulnerability: Case of industrialised

countries. Energy Policy, 2008. 36(10): p. 3734-3744.

10. Glynn, J., A. Chiodi, and B. Ó Gallachóir, Energy security assessment

methods: Quantifying the security co-benefits of decarbonising the Irish

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11. Farrell, A.E., et al., Ethanol Can Contribute to Energy and Environmental

Goals. Science, 2006. 311(5760): p. 506-508.

12. Dixon, P.B., S. Osborne, and M.T. Rimmer, The Economy-Wide Effects in

the United States of Replacing Crude Petroleum with Biomass. Energy &

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13. Chapin, F.S., P.A. Matson, and P.M. Vitousek, Plant Carbon Budgets, in

Principles of Terrestrial Ecosystem Ecology. 2011, Springer New York:

New York, NY. p. 157-181.

14. Kucharik, C.J., et al., Testing the performance of a dynamic global

ecosystem model: Water balance, carbon balance, and vegetation

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consumption, by state, by industry and fuel type, energy units Canberra,

ACT, Australia:Department of the Environment and Energy; 2017 [cited

2017 Nov 13].

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18. Kosinkova, J., et al., Measuring the regional availability of biomass for

biofuels and the potential for microalgae. Renewable and Sustainable

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19. Chum, H., et al., Bioenergy, in IPCC Special Report on Renewable Energy

Sources and Climate Change Mitigation, O. Edenhofer, et al., Editors.

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York, NY, USA.

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France.

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Demand Projections: A working paper for REmap 2030 2014,

International Renewable Energy Agency: Abu Dhabi, UAE. p. 88.

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Trilemma. Science, 2009. 325(5938): p. 270-271.

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biomass. Energy & Fuels, 1987. 1(2): p. 123-137.

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McGraw-Hill.


Chapter 2: Literature Review

Previous work in and around this research have informed the experimental

and modelling considerations applied in meeting the study objectives. The body of

knowledge in literature has shaped the research question and provided the

appropriate context for the research. In this chapter, the pertinent literature is

presented and discussed in order to provide an understanding of where this

research is situated in the biofuel production field.

Figure 2.1 presents the number of publications related to liquefaction from

1981. Prior to that period, there has been very few publications devoted to thermal

liquefaction. In the 1980s, it became an emerging topic of interest for producing

alternative fuels at the same time when oil price was unstable. This can be shown

in the steadily increasing number of articles in that period. In the following decade,

oil prices have been fairly stable, and interest in liquefaction was maintained, but

the conditions did not merit enough attention for further research.

Figure 2.1. Number of publications with the keyword “liquefaction”, related to the context of this

research (Scopus, 24 Nov 2017) plotted with the crude oil price variations [1] from 1981-2017.

The research started to pick up in 2007, with steady increases in the number

of published works until 2013, when the number of publications grew sharply. This

coincides with oil price spikes in 2005-2007 and 2011-2013. The last five years

have produced more than half of all relevant publications of the last 37 years.

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Number of Articles Crude Oil Price (Brent)


Traditionally, geopolitical conflict and economic crises were the main drivers of oil

price changes and subsequently, the volume of research in alternative fuels. The

recent emissions reduction targets could have also driven the sharply increasing

amount of research in this field despite the decrease in oil prices.

Looking closely at the last 10 years in Figure 2.2, it can be seen that the

publications in the specific areas of interest in this research were also robust. The

number of liquefaction studies that use ethanol as solvent, sugarcane bagasse as

feedstock, use distillation to upgrade biocrude, or studies performing techno-

economics were also increasing. This reflects the increasing interest in developing

liquefaction as a sustainable biofuel production process through the use of less

expensive feedstock such as process residues, and renewable, bio-based

solvents. Exploring paths to commericalisation through refinery analogues and

economic analysis can be seen as areas that are starting to be researched. This

generally presents that the volume of research could be pointing in a direction

aligned to what this thesis aimed to achieve.

Figure 2.2. Trends in the number of publications with the keyword “liquefaction”, in specific areas

of interest. (Scopus, 24 Nov 2017)

2.1 Current Fuel Production and Consumption

In 1983, Edmonds and Reilly predicted that use of conventional oil and gas

will likely be below 10% in 2050 [2]. This was from the view that demand will

decrease from 2025 and that coal will be a dominant energy source. Their

projections were partially correct. After the highest energy consumption growth of

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Liquefaction Articles Ethanol as Solvent Studies Techno-economic Studies

Bagasse Studies Distillation Studies


around 2.5% annually occurred in 1995 to 2005, the rate of growth has been

declining in the last decade, due to the downturn of energy-intensive industries

[1]. Coal production on the other hand had its up and downs in the last 25 years,

but has been met with a steady decline since 2011 [1]. Coal consumption has

been flat in the same period, due to the emergence of natural gas and renewables,

while oil consumption has been increasing [3].

Figure 2.3 shows the final energy consumption in 2015. The energy mix was

dominated by fossil fuels, with more than two-thirds of the total [3]. Oil has the

largest share in the energy consumption mix and has been driven largely by

transportation [3]. On the other hand, biomass has only been used for 11% of

consumption. This is mostly in traditional biomass heating applications [3].

Figure 2.3. World final energy consumption by fuel [3].

The transport sector is an interesting focus area for energy research because

of its association to economic growth and large dependence on petroleum fuels.

Economic growth is related to how energy is used for transport, i.e. more

transportation of goods, more personal travel, traffic congestion, etc. Unavoidably,

as world gross domestic product (GDP) increases, GHG emissions will also

increase [4]. Since economic growth is not likely to be impeded, one of the factors

that the Intergovernmental Panel for Climate Change (IPCC) proposes for reducing

GHG emissions in transport is to lower fuel carbon intensity. A way to achieve this

is to use alternative fuels such as biofuels [4].

Electricity, 19%

Natural Gas,

15%

Biofuel and

Waste, 11%

Coal, 11%

Other/ Renewables, 3%

Transport, 27%

Non-energy, 7%

Industry, 3%

Residential, 2%

Other, 2%

Oil, 41%


In the last 10 years, biofuels have gained attention in augmenting fuel supply

as shown by its increasing production. Figure 2.4 presents the regional biofuel

production from 2006-2016.

Figure 2.4. Global biofuel production [1].

In this period, biofuel production has been increasing around 14% annually

[1]. In North, Central and South America, bioethanol is the major product, with USA

and Brazil being the leading producers. In Europe, Eurasia and the Asia Pacific

regions, biodiesel dominates, with Germany and Indonesia as the top producers

[1]. To illustrate the scale, the global total biofuel production in 2016 amounts to

82.3 million t/y oil equivalent, which is dwarfed by 4382 million t/y of petroleum

crude oil produced. The current production of biofuels is a mere 1.9% of the total

crude oil production, and cannot, at this point, conceivably replace petroleum

crudes as transportation fuels. Therefore, to even marginally improve net GHG

emissions from vehicles, the volume of biofuel production needs to be increased.

2.2 Biomass Feedstocks for Conversion to Biofuels

Current biofuel production relies heavily on ‘first-generation’ feedstocks such

as vegetable oil and edible sugars [5], which can compete with food supply and

land dedicated for food crops. It has been suggested that non-food biomass or

‘second-generation’ feedstocks such as perennial crops grown on degraded land,

crop residues, sustainable wood, forest residues, and wastes should be used for

biofuels [6]. The use of microalgae was also proposed as a ‘third-generation’

feedstock [7]. The use of non-food biomass not only avoids affecting food security,

0

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2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Bio

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North America South and Central America Europe and Eurasia

Middle East Africa Asia Pacific


but also presents a wider variety of products due to the heterogeneous

composition of lignocellulosic materials. With first-generation biofuels, conversion

of sugars and oils use relatively straightforward processes and simple separations

due to the chemical homogeneity of the starting materials and products [5]. On

the other hand, ‘second-generation’ feedstock can be relatively complex to

process due to their chemical make-up. Plant materials have varying amounts of

cellulose, hemicellulose, lignin, proteins, extractives, starch and ash [8], which can

result in different chemical compositions of products from conversion processes.

The composition and structure of lignocellulosic materials can also hinder or

inhibit conversion processes, requiring additional process steps. For instance, the

production of ethanol from lignocellulosic feedstock requires pre-treatment of the

biomass by removing lignin and hydrolysis of cellulose and hemicellulose, to allow

access to the fermentable sugars [9].

Available biomass feedstock for bioenergy in 2050 was estimated on a ‘food-

first basis’ to range from 64 to 161 EJ/y, contingent on changes in climate and

livestock feed requirements [10]. Waste biomass can be collected centrally as

process outputs such as in sugar factories or in systems such as sewers and

manure handling systems [11]. Developing dedicated non-food crops does not

compete with food supply, but poses effects on land use, water supply and

biodiversity [12]. The use of agricultural residues as feedstock does not change

land use but requires more extensive harvesting and may affect agricultural

productivity and introduce additional GHG emissions through soil erosion [13].

Microalgae feedstock can grow without affecting land and freshwater use, but has

a high cost and significant technological challenges to maintain high yield and

feedstock quality [14]. All potential feedstock has its own advantages and

disadvantages; nonetheless it’s worth exploring their utility for biofuel production.

A variety of feedstock sources can help attenuate the adverse impacts of intensive

feedstock production practices.

Despite these challenges, processes that use non-food feedstock is currently

being developed for biofuel production. As of 2014, second-generation biofuels

make up 0.2% of total global biofuel production [15]. The price of producing

second-generation biodiesel is between US$ 0.9-1.2 per litre of gasoline

equivalent, which is significantly higher than US$ 0.6 per litre for gasoline and

diesel [16]. Nonetheless, the International Energy Agency [16] forecasts an


increase of more than three-fold in biofuel consumption from 2010 to 2035, with

a decrease in production cost approaching US$ 0.8 per litre of gasoline equivalent.

In these projections, feedstock costs are not predicted to decrease significantly,

however, costs of production will decrease due to technological advances.

Among the technologies being developed to produce biofuels,

thermochemical processes have been promising since these can convert whole

feedstock into fluids that can be further processed to biofuels. This is

advantageous due to larger total yields and the use of an uncomplicated process

that typically does not involve microorganisms or enzymes. Pyrolysis, for instance,

has been developed to an industrial-scale process to liquefy biomass [17].

However, the process requires low moisture content of feedstock, high heating and

quenching rate and high temperatures [18]. Gasification, another thermochemical

process, employs more severe temperatures to produce syngas, which can be

used to generate methanol, among others [19]. In contrast, liquefaction operates

at a lower temperature but in high pressures to exploit the reduced mass transfer

resistances as the solvent in the process behaves in the supercritical regime [20].

Figure 2.5 shows the pressure and temperature requirements of the

thermochemical processes employed to convert biomass to biofuels.

Figure 2.5. Pressure and temperature regimes of thermochemical processes to convert biomass

to liquid biofuels. Adapted from Forde et al. [19]

1

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100

1000

0 200 400 600 800 1000 1200

Pre

ssu

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ar)

Temperature (°C)

Liq

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Pyrolysis

Gasification


2.3 Liquefaction

Liquefaction is a thermochemical process used to harness the energy

content of the constituent chemicals of biomass by converting solid feedstock to

liquid product. Compared with pyrolysis, liquefaction can handle a larger variety of

feedstock because it can handle materials with significant moisture content. This

reduces the energy consumption in conversion by eliminating the need for a

separate pre-drying process. The presence of water in the process (i.e.

hydrothermal liquefaction or HTL) partitions the oily organic products from the

water-soluble by-products after liquefaction [20]. This facilitates separation of the

main product phase from the aqueous by-product phase.

The liquefaction process operates at temperatures of 280-400 °C and

pressures of 5-20 MPa [21]. Aside from reduced mass transfer resistances of the

solvent at these conditions, the high pressure also causes better penetration of

solvent into the biomass to facilitate fragmentation of large biomass polymers

[22]. The lower temperature required makes liquefaction a less energy-intensive

process compared with gasification [23]. The unrestrictive nature of HTL with

moisture content of feedstock allows a wide-range of materials to be processed.

Lignocellulosic materials such as wood [24, 25], agricultural and forest residues

[22, 24, 26-30], as well as high nitrogen materials such as municipal wastes [31],

sewage sludge [32], manure [33], and algae [34-36] have been used to produce

biocrude through HTL. The choice of feedstock and the reaction conditions in HTL

such as temperature, solvent, biomass-to-solvent ratio, reaction atmosphere and

reaction time affect quality and yield of biocrude produced [37].

Most research around the liquefaction process has been limited to lab- and

pilot-scale experiments and characterisation of products. Upgrading processes to

improve physical and chemical properties have been initiated by a number of

research groups, but not as numerous as those upgrading products of pyrolysis

[38]. As a result, there has been very few studies that have tested liquefaction-

produced biofuels in engines [39, 40].

2.4 Biocrude

The products of HTL are usually a gaseous phase, an aqueous phase, solids,

and a semi-liquid, dark-coloured, high-viscosity oily phase with a smoke-like odour


[34]. The product stream of interest is the oily portion, which has been referred to

in this research as biocrude. This term differentiates the product of liquefaction

from the product of pyrolysis, which is usually termed bio-oil. Biocrude and bio-oil

share many similar properties and are occasionally used interchangeably.

However, the differences in the nature of liquefaction and pyrolysis processes

make biocrudes and bio-oils different. Pyrolysis bio-oil usually has a relatively lower

viscosity but higher acidity than biocrude [41]. Biocrude typically has higher

calorific value and hydrogen content, and lower moisture content than pyrolysis

bio-oil [42]. Furthermore, it has been suggested that biocrude can be more

thermally stable and can be hydrotreated directly in more severe conditions [43].

The chemical composition of biocrude depends heavily on the composition

of the feedstock in HTL. The composition of feedstock vary for each taxonomic

group [8] and these variations affect the chemical composition of biocrude

produced [29, 37, 44]. Lignocellulosic materials break down into a complex

assortment of chemicals consisting of carbon, hydrogen, and oxygen [21].

Biocrude from wastes, sludge, manure, and algae have nitrogen-containing

compounds from the deamination of amino acids in proteins [20]. Table 2.1 shows

the biocrude properties from different feedstock, with physical properties of

pyrolysis bio-oil, diesel and biodiesel included for comparison.


Table 2.1. Physical properties of biocrude from various feedstock and comparison with diesel and

biodiesel.

Liquefaction

Feedstock

Compositiona,b

(wt%)

Viscosity

(mPa-s)

Density

(kg/L)

Heating

Value

(MJ/kg)

Ref

Beech wood Cell: 44.2%

Hemi: 33.5%

Lig: 21.8%

-- 1.1 35 [24]

Bagasse Cell: 49.2%

Hemi: 25.8%

Lig: 19.5%

6.7 × 105 -- 31 [45]

Coconut husk Cell: 30.6%

Hemi: 25.9%

Lig: 38.8%

1.3 × 106 -- 30 [45]

Corn stalk Cell: 42.4%

Hemi: 25.8%

Lig: 21.7%

1.6 × 106 -- 30 [45]

Rice straw Cell: 41.3%

Hemi: 24.6%

Lig: 9.2%

1124, 50 °C 1.02 34 [44]

Garbagec Carb: 55%

Prot: 18.4%

Fat: 5.3%

53,000 -- 36 [31]

Sewage sludge Carb: 20.3%

Prot: 33.6%

Fat: 6.9%

818.3, 50 °C 0.91 36 [44]

Reference Substances

Beech pyrolysis

bio-oil

C: 41%

H: 6.5%

O: 52.8%

70, 40 °C 1.1 21 [41]

Diesel C: 87%

H: 13%

1.1–3.5, 40 °C 0.85 45 [46]

Biodiesel C: 77%

H: 12%

O: 11%

1.7–5.3, 40 °C 0.88 40 [46]

a. Cellulose, Hemicellulose, Lignin; Carbohydrate, Protein, Fat; Carbon, Hydrogen, Oxygen. b. Composition

of feedstock for most, composition of product for pyrolysis bio-oil, diesel, and biodiesel; c. Artificial garbage

mixed for the study

The properties of fuels predict their performance and emissions when used

in internal combustion engines. Matching the properties of biocrude to

conventional fuels in liquefaction and upgrading studies enables the production of

a drop-in biofuel that does not require engine modifications for its direct use.


Viscosity is a regulated property with an impact to energy requirements in

fuel pumping, engine deposits [47], combustion [48] and emissions [49]. As

shown in Table 2.1, biocrudes have viscosities up to the magnitude of 106, which

is at least 75 times higher than reported viscosities of bitumen [50]. The difference

in viscosity of biocrude and traditional fuels or commercially-available biofuels is

obvious in its appearance. Biocrude has been reported to have a viscosity 35-100

times higher than diesel or biodiesel [34]. Fuel density is another important

measure as it directly influences mass injection in delivering the appropriate

amount of fuel for complete combustion [51] and the quality of emissions [52-54].

Reported biocrude densities (Table 2.1) are usually above conventional fuels and

can negatively affect engine performance and emissions.

The heating value of biocrude is usually presented as higher heating value

(HHV), which includes the heat of vaporisation of water during combustion. Fuel

HHV has been correlated with chemical composition expressed in ultimate [55]

and proximate [56] analyses. Ultimate analysis correlations state that the HHV is

increased with higher carbon and hydrogen content and decreased with higher

oxygen and nitrogen content [57]. The heating value of biocrude is commonly

reported in liquefaction studies. A comparison between heating value of the

biocrude to that of the feedstock provides an indication of the energy conversion

efficiency of the HTL process. An improvement in the heating value suggests an

effective fuel production process. This same assessment can be found in

upgrading studies.

Figure 2.6 shows typical HHV of feedstock and biocrudes produced in HTL.

HHV is not regulated in fuels; however in development of alternative fuels, it is

desirable to produce fuels with HHV similar to conventional fuels for ease of use

with current engine fuel injection designs.


Figure 2.6 High heating value of feedstock compared with their corresponding biocrudes [22, 24,

27, 29-31, 34, 44, 58-60].

The abundance of polar compounds in biocrude was observed from its

miscibility with polar solvents, as shown in many HTL experiments. These solvents

are used to extract the maximum amount of biocrude in the separation of the

product mixture following liquefaction. Biocrude has been extracted using acetone

with around 22-59% yield [22, 24], and dichloromethane with yields from 18-50%

[59]. On the other hand, using a non-polar solvent, Chan et al. [61] obtained 16-

38% yield by extracting with toluene. The extracting solvent does affect the

production of biocrude, however, it contributes to the amount of the organic

fraction separated from the aqueous product fraction. Nabi et al. [39] directly

blended biocrude with diesel and obtained a blend containing 0.33 biocrude mass

fraction. Solubility in traditional non-polar fuels could serve as an initial screening

for the direct use of biocrude as fuel; however, other properties are important in

achieving good engine performance and emissions.

Biocrudes are composed mostly of carbon, but a substantial level of oxygen

can be expected from biocrudes due to the high oxygen content of the feedstock

[62]. Depolymerisation of cellulose, hemicellulose, and lignin in biomass, along

with dehydration and decarboxylation reactions during HTL produce biocrude with

lower oxygen content than the original feedstock (i.e. 40-60% in feedstock to 10-

20% in biocrude) [20]. Oxygen in biocrude is in form of any number of carboxylic

0 10 20 30 40

Beech

Aspen wood

Bagasse 1

Bagasse 2

Rice straw 2

Rice straw 1

Garbage

Arundo

Sorghum

Pineapple tops

Banana bunch stems

Miscanthus

Corncob

Spirulina platensis

High Heating Value (MJ/kg)Feedstock

Bio-crude


acids, alcohols, ketones, aldehydes, sugars, furans, phenols, guaiacols, syringols,

and other oxygenates [21]. Figure 2.7 demonstrates the difference in oxygen

content between biocrudes, upgraded biocrudes and reference fuels. In the figure,

oxygen content is expressed as a ratio to the carbon content and plotted against

the hydrogen-carbon ratio.

Figure 2.7 Van Krevelen plot of molar H/C and O/C ratios of HTL biocrudes from lignocellulosic

materials, compared with upgraded products and reference fuels [22, 24, 27, 30, 44, 58, 60, 63-

66].

In terms of elemental composition, biocrudes are significantly different with

diesel or biodiesel. In upgraded biocrudes, however, oxygen content is lower. In

the refining of crude oil, if required, oxygen is removed prior to reforming to prevent

catalyst poisoning [67]. Oxygenated fuels such as biodiesel have been observed

to burn with better emissions [68, 69]. Effects of oxygen content on physical

properties of fuels are not well elucidated; however, it can be observed from

various studies that high oxygen content is correlated with low HHV.

Nitrogen and sulphur are also present in small amounts in biocrude. Nitrogen

content of biocrude is around 2% [22, 24, 27], while sulphur is found in very small

amounts, with only 0.08-0.3% detected in lignocellulosic biocrudes [27, 66].

Higher nitrogen and sulphur contents can be expected from algae biocrudes due

to higher content of proteins [34]. The low levels of nitrogen and sulphur reduce

the desulphurisation and denitrogenation burden in upgrading. As sulphur content

of fuels is regulated in many jurisdictions because of its polluting potential [67],

0

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H/CLignocellulosic Biocrude Upgraded Biocrude Reference Fuels


upgraded biocrudes may be developed as blendstock fuels to reduce sulphur

content in conventional fuels. Unlike sulphur, nitrogen is not regulated; however,

low nitrogen content is necessary to minimise catalyst deactivation and improve

diesel stability [67] .

Biocrude is composed of a complex mixture of organic chemicals [21, 70]

unlike conventional fuels which have a narrower range of chemical moieties [71,

72]. The effects of various functional groups to fuel properties have not been

explained for biocrude, compared with numerous studies on biodiesel and

petroleum-based fuels [71, 73]. The presence of oxygenated compounds such as

acids, aldehydes and chars decrease the stability of pyrolysis bio-oils [74]. As these

functional groups are present in biocrude, similar effects can be expected. Phenol

and substituted phenols from the degradation of lignin were also observed to be

reactive components in biocrude [75]. The diversity of chemical species in

biocrude also affect thermal stability in upgrading processes [76] and certain

functional groups react at different temperatures [77]. Altering the chemical

composition with an objective to produce fuels with similar physical properties is

a key purpose of employing upgrading processes.

2.5 Upgrading

Research related to the development of various upgrading processes is

predicated on limitations of biocrude as a fuel. It is inferred that upgrading

processes improve biocrude properties to enable use as fuel. Figure 2.8 illustrates

changes in the heating value and oxygen content of duckweed, aspen wood and a

hardwood biocrude to upgraded biocrude. Bars represent heating values and

diamonds represent oxygen content. Orange figures are for biocrude and blue

figures are for upgraded biocrude.


Figure 2.8. Improvements in (a) HHV and (b) oxygen content by upgrading biocrude [65, 66, 78].

In biofuel research, the upgrading of biocrudes has been developed aligned

with conventional petroleum refining processes. This was conceived with a view of

co-processing in existing refineries to mitigate the adverse effects to profitability

of the massive capital costs required for a new biorefinery and facilitate the

introduction of biocrude-derived fuels into an established petroleum market [78].

This has been recognised as a pathway for pyrolysis bio-oils [79] thus, numerous

studies in upgrading pyrolysis bio-oil through refinery processes have been

developed. Due to similar chemical properties, this approach can also be taken for

exploring upgrading of biocrudes. This is discussed in more detail in Chapter 3.

2.6 Liquefaction Biofuels at the Larger Scale

Most liquefaction literature presents research of liquefaction and biocrudes

at the laboratory scale. Early pilot scale tests in the 1980s were conducted at the

Pittsburgh Energy Research Centre and the Pacific Northwest National Laboratory;

however the low yields from the HTL of wood slowed the further development of

the process at larger scales [80]. Up to 2011, it was believed that the facility in

Oregon, USA was the largest HTL facility, processing 100 kg/h of feedstock [80].

Since then, larger pilot plants have been built to adequately assess large scale

material and thermal efficiencies. Table 2.2 presents the currently operational

demonstration plants reported in the International Energy Agency (IEA) Task 39

Database. Other facilities not in the database could also be in operation but are

protected by investors to preserve their competitiveness.

0

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15

20

25

30

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45

Oxyg

en

Co

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nt (%

) [d

iam

on

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HH

V (M

J/k

g) [

ba

rs]

Upgraded Biocrude (HHV) Biocrude (HHV) Biocrude (O-content) Upgraded biocrude (O-content)


Table 2.2. Operational liquefaction demonstration plants [81].

Facility Country Feedstock Capacity

(t/y biocrude)

Aarhus University,

Center for Biorefining

Technologies

Denmark Lignocellulosic

crops

1

Licella Australia Radiata pine,

banna grass,

algae

350

Steeper Energy/

Aalborg University

Denmark Forest residues 24

The pilot plants allowed research into steady state operations and

parameters that could not typically be explored in laboratory-scale batch

experiments. Pedersen et al. [82] investigated the effect of recirculation of water

solvent and concluded that total organic carbon and ash accumulated over time

in their continuous reactor. A continuous HTL reactor in the University of Sydney

was able to pump 10% algae slurry and concluded that residence times to obtain

maximum yield could be shorter than what is usually reported in batch studies

[83]. Another key parameter explored is the solids loading or slurry concentration

of liquefaction feedstock. It is desirable to pump a slurry with the highest organic

content to maximise biocrude production but the slurry concentration is limited by

its ability to be pumped to the desired reaction pressure. Previous research have

achieved successful pumping of up to 14% dry solids in water [80]. Dãrãban et al.

[84] suggested mixing the slurry with recycled biocrude or pre-treating the slurry

with sodium hydroxide at 180 °C to obtain a pumpable slurry concentration of

20%.

To date, the literature review yielded no pilot- or large-scale liquefaction-

plants that involved upgrading of biocrude to drop-in fuels, although the

institutions with pilot-scale liquefaction facilities are involved in laboratory-scale

upgrading research.

2.7 Modelling Liquefaction in Different Scales

A number of studies have modelled liquefaction in different scales to predict

the mass and energy transfer and chemical reactions occurring within the process.

The models in the small scale assist process modelling by providing information


around the transport processes into and from feedstock particles, the chemical

changes occurring during liquefaction, and the rate at which these processes

occur. Ultimately, models can help predict product yields and compositions based

on the interactions of the reagents and variations in process parameters. Ammar

et al. [85] proposed a two-step mechanism of diffusion of the solvent into the

biomass fibre and the kinetics of liquefaction reactions of the biomass. The study

describes a “resistant core” in thicker pieces of biomass (i.e. as in slabs or chips)

due to several mass and heat transfer occurrences. This provides a simple basis

for modelling the effect of temperature, solvent properties, and biomass feed

particle size. Demirbaş [24] conducted liquefaction of beech wood, spruce wood,

hazelnut shells and tea waste, and calculated the correlation of liquid product

yields with temperature and lignin content. The linear equations developed from

the experimental data are limited to the conditions in which the experiments were

carried out; however, a similar approach can be implemented for more

sophisticated modelling using the increasingly large liquefaction data set.

Kinetic modelling for liquefaction has been explored because of the

necessity of kinetic data in sizing reactors. Due to the complexity of the starting

material and the end product, many different chemical reactions and other rate-

controlling mass and energy transfer processes occur simultaneously. This

presents significant challenges in coming up with a general model. The use of

model compounds enable isolated analysis of the reactions that can simplify the

kinetic models [20]. The varying feedstock composition also presents a hurdle in

modelling liquefaction. Most of the modelling studies have focused on deciphering

the reaction pathways to accurately predict the yield and composition of biocrudes

based on the different levels of cellulose, hemicellulose, and lignin in biomass [86-

89]. Liquefactions with real feedstock uncovered interactions between cellulose

and hemicellulose that affect product distribution of chemical species in biocrude

[87].

Another complexity in modelling liquefaction is representing biocrude in

mass and energy balances. Biomass feedstock, albeit its complex composition,

has been modelled in computer modelling systems using its proximate and

ultimate analysis [90, 91] to calculate physical properties or generate degradation

products. Depending on the modelling tool used and the availability of data, the

product yields can be calculated from the feedstock input [92], although this


approach is limited by the specification of possible products and the assumption

of reactions reaching equilibrium. A more complicated model considering more

potential products and stoichiometry can be implemented; however, the

computational requirements can be massive (i.e. finding the solution to a system

of a large number of interrelated equations) without a guarantee of more accurate

results. Thus, it is more expedient to use a range of yields for the different product

streams to simulate liquefaction in process models.

Another hindrance is the availability of physical and chemical data. Software

such as ASPEN1 Plus™ and CHEMCAD™ rely on the availability of data from its

databanks and the data available from the National Institute of Standards and

Technology (NIST) Standard Reference Data Program (SRDP) [93, 94]. That is not

to imply that modelling software are not powerful for liquefaction modelling;

however, some compounds identified in biocrude will not have complete datasets

for predicting chemical and phase equilibrium. These software can also estimate

certain properties using available data and correlations from literature [93].

Researchers have found great use for process modelling software to

simulate thermochemical processes such as liquefaction due to their built-in

computational abilities. Both ASPEN Plus and CHEMCAD have libraries of unit

operation blocks that can calculate reactions and separations as specified. In

liquefaction literature, ASPEN Plus is the software of choice for modelling the

liquefaction process [95-98] although some modelling studies in CHEMCAD were

also published [99].

In these liquefaction models, biocrude is typically represented as a mixture

of compounds identified in liquefaction studies. The data is usually a list of

components determined by gas chromatography-mass spectrometry (GC-MS) with

their corresponding relative quantities given as the percentage of the total

chromatogram area. The use of GC to separate biocrudes in analysis has received

some criticism due to the presence of non-volatile components that do not appear

in GC chromatograms [100]. Furthermore, data on the quantities of components

is in need of improvement using advanced analytical methods such as two-

dimensional GC time-of-flight mass spectrometry (GC x GC TOFMS), GC with a

1 ASPEN and CHEMCAD are brand names of commercial software and are not considered acronyms.


flame ionisation detector (GC-FID), and the use of standards to calibrate signals

with known quantities [101]. Advanced analysis of biocrude using GC-Fourier

Transform Ion Cyclotron Resonance-MS has identified thousands of compounds in

biocrude [100]. Some researchers have overcome some of these difficulties in

quantitative analysis by linear programming techniques to close the mass balance,

but still using a relatively small number of representative compounds [98].

Another way to potentially represent biocrudes is through its bulk properties.

Petroleum crudes have been traditionally characterised by its assay properties

[102] because quantification of each of the thousands of individual components

[103] is a gargantuan task. In consideration of the analytical difficulties around

identifying and quantifying each component in biocrude, representing biocrudes

using assay data could be useful, especially with a view of co-processing biocrudes

with petroleum crudes. However, applying the same method should be scrutinised

since biocrudes cannot be directly compared with petroleum crude oil. Biocrude

may have chemical compounds that have up to eight oxygen heteroatoms, as

phenols, methoxyphenols, furans, and other oxygenates, comprising up to 55% of

the biocrude [104]. This is in contrast with petroleum crude, which has four oxygen

heteroatoms at most. This difference contributes to the difference in polarity of

biocrude and petroleum crude. Furthermore, biocrude components have been

observed to have lower carbon numbers with a higher amount of double bonds

[104]. These differences between petroleum crude and biocrude may translate

into variations in the effectiveness of traditional petroleum assay methods and

numerical methods used in software to represent petroleum crude oils [105].

Due to the difficulties presented in modelling liquefaction and biocrude,

modelling biocrude upgrading processes have also been complicated. However,

since most biocrude upgrading processes are the same as in petroleum refining

or pyrolysis bio-oil upgrading, the chemistry is more elucidated [106], analytical

methods are established [107], and comparisons can be easily made [42]. The

use of model compounds in pyrolysis bio-oil upgrading studies [108] have similarly

enabled understanding of hydrodeoxygenation of biocrudes. A number of kinetic

studies have attempted to present the kinetics of hydrodeoxygenation and

hydrocracking of biocrude using different reaction conditions and catalysts. [109-

111]. Nevertheless, the use of model compounds and their yields to simulate

upgrading is still the prevalent method in process modelling studies [97, 98, 112].


2.8 Techno-economic Studies of Liquefaction Plants

As liquefaction technology matures, the research shifts to optimisation and

commercialisation. Even at an early stage, a techno-economic study can inform

future work by identifying improvement areas that have greater impact to

efficiencies, costs and profitability [113]. As discussed in Section 2.6, to date, only

a few liquefaction plants are in operation, and none at the commercial scale.

However, techno-economic analyses of liquefaction processes have been

conducted as early as 30 years ago, e.g. [114]. Subsequent analyses over the

years that use current state of technology to provide fresh perspectives on the

technical and economic feasibility of the process have been few and far between.

Figure 2.9 summarises the results of these techno-economic studies for the

liquefaction process. The large bubbles indicate capital cost (left axis), with bubble

size indicating plant size. The capital cost values were adjusted to 2017 values

using the Chemical Engineering Plant Cost Index (CEPCI)2 [115]. Dots indicate

product price (right axis). Red dots plants that use represent lignocellulosic

feedstock while green dots represent algal feedstock. Prices were adjusted to

2017 values using a consumer price index (CPI) [116].

Figure 2.9. Summary of literature estimates of capital costs and product prices from techno-

economic analyses of liquefaction plants [95-98, 117-120].

2 Adjusted cost = Base cost x (CEPCI2017/CEPCIbase year)

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

0

100

200

300

400

500

600

700

1980 1985 1990 1995 2000 2005 2010 2015 2020

Pro

du

ct

Pri

ce (

20

17

US

$/L)

Pla

nt

Co

st

(20

17

US

$ M

illio

ns)

Estimation Year

Large bubbles (blue) indicate capital cost (left axis),

bubble size indicate plant size

Dots (red/green) indicate product price (right axis)


In Figure 2.9, it can be observed that there is no trend in capital costs

changing across estimation years or even plant capacities. Similar capacities in

1987, 2005 and 2014 have similar adjusted costs, even though it was expected

that the learning rate for this technology have decreased capital costs over time

[121]. Potentially contributing to this could be the capital cost methodologies

employed in techno-economic studies, which have been critiqued around taking

estimation uncertainties in consideration [122].

The differences in product price are usually caused by different conditions

and cases used in the study. For instance, Magdeldin et al. [98] presented five

different plant configurations that resulted in variations in product price in the

range of US$ 0.85-2.33/L. Additionally, the liquefaction process and biocrude also

take many different forms and different locations will have major differences in

feedstock, distribution systems and costs. The hydrocarbon product price is

usually sensitive to feedstock price [96, 123] and conversion yields [97, 117,

119]. These techno-economic analyses usually discuss the effects of changes in

conversion rates and input costs, but rarely present the variability due to taxes and

incentives, which can drive further investment.

Comparative techno-economic analysis of different technologies have also

been conducted for biofuel productions, although the data is usually extracted

from literature. Due to the absence of the process models in these comparisons,

the effect of technical parameters to profitability and product price across the

different technologies are not usually elucidated [119, 122].

2.9 Conclusion

Research in liquefaction is a burgeoning field due to its potential utility in

producing high-yield, high-quality fuel. The literature demonstrates an increasing

understanding of the liquefaction process and biocrudes; however, the work is

limited to the laboratory- and pilot-scale.

In modelling liquefaction and biocrude upgrading processes, there are still

difficulties in properly representing biocrudes due to the variations in chemical

composition data, brought about by analytical difficulties. Therefore, a different

approach can be useful for representing biocrude in modelling software to


facilitate further techno-economic modelling and life cycle analyses, and lead

liquefaction into commercial production of biofuels.

The technical and economic viability of liquefaction plants are being

demonstrated in a number of studies. However, new techniques in liquefaction,

such as the use of waste feedstock, renewable solvents and upgrading processes

should be included in liquefaction models to properly assess their technical and

economic effects. A study including the technical aspects of liquefaction compared

with other thermochemical processes can also provide a more meaningful analysis

of profitability in the proper context.


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technologies. Energy Policy, 2001. 29(4): p. 255-261.

122. Brown, T.R., A critical analysis of thermochemical cellulosic biorefinery

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p. 412-421.

123. Beckman, D., et al., Techno-economic assessment of selected biomass

liquefaction processes. 1990.

http://www.chemengonline.com/pci-home

https://data.bls.gov/cgi-bin/cpicalc.pl


Chapter 3: A review of hydrothermal

liquefaction biocrude properties

and prospects for upgrading to

transportation fuels

Jerome A. Ramirez 1, Richard J. Brown 1,2 and Thomas J. Rainey 1,2

1 School of Chemistry, Physics and Mechanical Engineering, Science and

Engineering Faculty, Queensland University of Technology, 2 George St,

Brisbane, Queensland 4000, Australia;

E-Mails: [email protected] (J.A.R.); [email protected]

(R.J.B.)

2 Biofuel Engine Research Facility, Queensland University of Technology, 2

George St, Brisbane, Queensland 4000, Australia;

Published in Energies, 8 (2015), Pages 6765-6794


STATEMENT OF JOINT AUTHORSHIP

The authors listed below have certified that:

1. they meet the criteria for authorship in that they have participated in the

conception, execution, or interpretation, of at least that part of the

publication in their field of expertise;

2. they take public responsibility for their part of the publication, except for

the responsible author who accepts overall responsibility for the

publication;

3. there are no other authors of the publication according to these criteria;

4. potential conflicts of interest have been disclosed to (a) granting bodies,

(b) the editor or publisher of journals or other publications, and (c) the

head of the responsible academic unit; and

5. they agree to the use of the publication in the student’s thesis and its

publication on the QUT ePrints site consistent with any limitations set by

publisher requirements.

In the case of this chapter: Chapter 3

Title: A review of hydrothermal liquefaction biocrude properties and prospects for

upgrading to transportation fuels (2015, published)

Contributor Statement of Contribution

Jerome Ramirez Developed the outline and wrote the manuscript

Richard Brown Guided the conception of the review outline and provided

major editorial contributions

Thomas Rainey Guided the conception of the review outline and provided

major editorial contributions

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming

their certifying authorship.

Thomas Rainey 13 July 2018

Name Signature Date


A Review of Hydrothermal Liquefaction Biocrude

Properties and Prospects for Upgrading to

Transportation Fuels

Jerome A. Ramirez 1, Richard J. Brown 1,2, † and Thomas J. Rainey 1,2, †

1 School of Chemistry, Physics and Mechanical Engineering, Science and

Engineering Faculty, Queensland University of Technology, 2 George St,

Brisbane, Queensland 4000, Australia;

E-Mails: [email protected] (J.A.R.); [email protected]

(R.J.B.)

2 Biofuel Engine Research Facility, Queensland University of Technology, 2

George St, Brisbane, Queensland 4000, Australia;

Published in Energies, 8 (2015), Pages 6765-6794

† These authors contributed equally to this work.

* Author to whom correspondence should be addressed; E-Mail:

[email protected];

Tel.: +61-7-3138-1977; Fax: +61-7-3138-8381

Abstract: Hydrothermal liquefaction (HTL) presents a viable route for

converting a vast range of materials into liquid fuel, without the need for pre-

drying. Currently, HTL studies produce biocrude with properties that fall short

of diesel or biodiesel standards. Upgrading biocrude improves the physical

and chemical properties to produce a fuel corresponding to diesel or

biodiesel. Properties such as viscosity, density, heating value, oxygen,

nitrogen and sulphur content, and chemical composition can be modified

towards meeting fuel standards using strategies such as solvent extraction,

distillation, hydrodeoxygenation and catalytic cracking. This article presents

a review of the upgrading technologies available, and how they might be

used to make HTL biocrude into a transportation fuel that meets current fuel

property standards.

Keywords: upgrading; liquefaction; biofuels; hydrothermal liquefaction (HTL)


3.1 Introduction

In the first quarter of 2015, global atmospheric carbon dioxide levels have

reached a record high of 400 parts per million (ppm) [1]. In order to restrict global

temperature rise to 2 °C, greenhouse gas (GHG) emissions should be maintained

in the range of 445–490 ppm CO2-equivalent [2]. This target requires a reduction

in GHG emissions from energy production by shifting from a reliance on fossil fuels

to renewable energy sources such as biomass and biofuels. With a climate change

mitigation strategy that maintains CO2 levels at 450 ppm, it is projected the share

of biofuels in the energy mix will rise to up to 11% by 2030 [3].

Biomass is used as a sustainable solid fuel largely for cooking and heating

[3]. In recent years, however, energy from biomass has taken a different form.

From 1990 to 2008, the use of liquid fuels from biomass increased at an average

of 12.1% annually, taking biofuels’ share in the global transport fuel mix to 2% in

2008 [3]. Furthermore, the United Nations Sustainable Energy for All Strategy

aims to “double the share of renewable energy in the global energy mix” by 2030

[4]. This demonstrates a need to develop highly-productive and cost-effective

biofuel technologies not only to meet the growing energy demand, but also to

support climate mitigation strategies. These imperatives provide motivation to

make fuels from biomass viable for widespread use.

Converting biomass from its natural solid form to liquid fuels is not a

spontaneous process. The liquid fuels that humans have harnessed on a large

scale as fossil fuels took thousands of years of geochemical processing to convert

biomass to crude oil and gas. Unprocessed biomass, however, has lower energy

density, higher moisture content, and its physical form is not homogeneous and

free-flowing [5] making it a problem as a feedstock for reciprocating engines.

These issues have been partially addressed by a number of processing

technologies. For example, the controlled burning of wood in the absence of air to

produce charcoal results in a solid fuel with lower moisture content and a higher

energy density than wood [5]. However, since charcoal is still a solid, it cannot be

used in modern transportation applications.

In the 1940s, Berl [6] noted that the high conversion and thermal

efficiencies for converting carbohydrate-containing materials into liquid fuel

justified further research with a view to overcoming declining oil reserves.


In addition to addressing climate change and energy security, it can be

expected that wide use of biofuels may bring about benefits towards improving

overall health. Sulphur dioxide, among other pollutants, are significantly lower

when biodiesel is used instead of conventional diesel [4]. Moreover, reduction of

air pollution from fossil fuels is projected to cause a decline in mortalities and

health care costs quantified in the range of US$ 1.9–4.6 per gigajoule [7].

As of 2010, world biofuel production has been largely focused on first-

generation fuels producing ethanol and biodiesel from starch, sugars, and

vegetable oils. Advanced biofuels or biofuels produced from lignocellulosic

materials such as wood waste and straw made up only 0.2% of total biofuel

production [8]. In recent years, research on using biomass for liquid fuels has been

robust, ranging from studies of pyrolysis and hydrothermal liquefaction of

lignocellulosic materials, gasification and biomass-to-liquid technologies, to

upgrading processes.

Several technologies have been employed to harness the energy content of

biomass and make it more available for a variety of uses [9]. Of these,

thermochemical processes are of significant importance due to their ability to

transform biomass into fluids, increase heating value, and enable easier handling,

distribution and storage. Pyrolysis, initially developed to produce chemicals such

as methanol, acetic acid and acetone from wood [5] has been widely researched

and developed to an industrial-scale process to produce oils from biomass. Among

different pyrolysis processes, fast pyrolysis has been determined to maximise

liquid products [10]. However, fast pyrolysis is limited by its requirements for low

moisture content feedstocks, rapid heating and quenching rate, and high

temperatures [11]. Hydrothermal liquefaction (HTL) or solvolysis, on the other

hand, is preferred over pyrolysis for processing feedstock with significant moisture

content because the process does not consume energy in the removal of water,

either through pre-drying or in-process evaporation. Moreover, the reaction of

these substances with water or other hydrogen donor solvents facilitates

separation of the oily product stream from the more polar by-product stream [12].

Hydrothermal liquefaction (HTL) produces liquid biocrude through treatment

of biomass at high pressures of 50–200 atm and high temperatures of 250–

400 °C [13]. HTL exploits the properties of superheated fluids to reduce mass


transfer resistances [12]. The high pressure also enables higher penetration of

the solvent into the biomass structure to facilitate fragmentation of biomass

molecules [14]. The nature of the process allows for feedstock with high moisture

content, therefore a wide range of material can be subjected to HTL to produce

biocrude. Studies liquefying wood [15-18], forest residues [19-21], agricultural

residues [14, 16, 17, 20, 22, 23], municipal wastes [24, 25],sewage sludge [26,

27], manure [28, 29], and algae [30-35] have been published.

The choice of feedstock is contingent on many factors such as availability

[36] and ease of transportation; however from a processing perspective, it is

important to know the composition of the material. Lignocellulosic materials such

as wood, forest and agricultural residues contain varying levels of cellulose,

hemicellulose and lignin [12]. Under HTL, these complex biopolymers break into

a complex mixture of chemicals consisting mostly of carbon, hydrogen and oxygen

[13]. Municipal wastes and sewage sludge contain a significant amount of

nitrogen due to the protein content derived from human wastes [25, 27]. Algae

contain carbohydrates, lipids and proteins [31, 35], which break down to various

organic chemicals, some of which contain nitrogen from deamination of amino

acids from proteins [12].

HTL biocrudes are semi-liquid [6], viscous, dark-coloured and have a smoke-

like smell [35]. The typical viscosity of biocrudes is 10–10,000 times higher than

that of diesel and biodiesel [16, 21, 25, 30, 32, 35]. Moreover, heating values are

not comparable with conventional fuels and biodiesel. These properties make HTL

biocrude difficult to use as transportation fuels, apart from marine applications.

Nabi et al. [37] blended wood powder HTL biocrude with conventional diesel fuel

and studied fuel properties, emissions, and engine performance. The investigation

concluded that the blended fuel doesn’t cause significant changes in engine

performance. It was observed that particulate matter mass (PM) and particulate

number (PN) were lower while total unburnt hydrocarbon (UHC) and nitric oxide

emissions were higher. While this study demonstrated the feasibility of directly

using HTL biocrudes by blending with diesel, the blend was still predominantly

fossil fuel. Therefore, there is an opportunity to maximise the benefits of using a

totally-renewable fuel by improving the properties of the HTL biocrude through

upgrading.


Upgrading refers to processing oils in order to improve their physical and

chemical properties to values given in existing fuel standards. As shown in Figure

3.1, upgrading processes follow HTL, with a general objective to produce fuel with

standard properties.

Figure 3.1. Thermochemical process conceptual diagram and outline of the article.

Figure 3.1 shows the organisation of the topics discussed in this article. In

the next section, physical and chemical properties of biocrude are examined. In

Section 3.3, upgrading processes that have been investigated and prospective

processes that can be applied to upgrade HTL biocrude are reviewed. Finally, in

Section 3.4, a discussion on challenges of HTL biocrude upgrading and

considerations for prospective research are also discussed. In this article,

products of pyrolysis will be referred to as bio-oil, while HTL products will be

referred to as biocrude.

3.2 Biocrude Properties

Physical properties are indicative of the characteristics and interactions of

the mixture of chemicals that comprise biocrudes. Chemical composition of

biocrude depends on HTL reaction conditions such as temperature, solvent,

solvent density, reaction time, and gas used as reaction atmosphere, but the

composition of the biomass fed into the liquefaction process has the most

significant effect [38]. The use of different feedstocks greatly affects biocrude

properties. A comparison of physical and chemical properties of biocrudes

obtained from various HTL studies with diesel and biodiesel standards are

summarised in Table 3.1, showing findings for biocrude viscosity, density, heating

value, hydrogen-carbon (H/C) and oxygen carbon (O/C) ratios.


In this review, physical properties of biocrude will be compared with diesel

or biodiesel standards, since these have been well-studied and regulated in many

jurisdictions. Chemical properties, such as composition will be discussed

independently or where appropriate, compared to similar chemicals or substances

used as fuel.


Table 3.1. Biocrude produced from various feedstock and their properties.

Fe

ed

sto

ck

Typ

e

Feedstock Compositiona Viscosity,

mPa-s Density,

kg/L

Heating

Value, MJ/kg

H/Cb O/Cb Ref

Liquefaction biocrudes

Lig

no

cellu

losic

s

Beech wood

C: 44.2%;

H: 33.5%; L: 21.8%

-- 1.1 35 1.11 0.16 [16]

Bagasse

C: 49.2%;

H: 25.8%; L: 19.5%

6.7 × 105 -- 31 1.12 0.21 [21]

Bagasse/

black liquor

C: 41.3%;

H: 23.7%; L: 25.6%c

-- -- 28 1.35 0.39 [39]

Coconut husk

C: 30.6%; H: 25.9%;

L: 38.8%

1.3 × 106 -- 30 1.00 0.21 [21]

Corn stalk

C: 42.4%;

H: 25.8%; L: 21.7%

1.6 × 106 -- 30 1.01 0.21 [21]

Garbage

Carb: 55%;

Prot: 18.4%; Fat: 5.3%

53,000 -- 36 1.48 0.13 [25]

Mic

roa

lgae

Dunaliella

tertiolecta

Carb: 14.7%; Prot: 63.6%;

Fat: 20.5%

15–330,

50 °C -- 36 1.36 0.09 [30]

Botryococcus

braunii

98% organic content;

50% hexane soluble

64–160,

50 °C -- 48 2.42 0.02 [32]

Spirulina platensis

Carb: 30.2%; Prot: 48.4%;

Fat: 13.3%

189.80, 40 °C

0.97 34 1.44 0.1 [35]

Scenedesmus

sp. --

3.27–

3.75, 25 °C

0.97–

1.04d 30 1.60 0.1 [40]

Reference Fuels

Fuels Diesel

1.1–3.5,

40 °C 0.85 45.1 1.79 0 [41]

Biodiesel 1.7–5.3,

40 °C 0.88 40.5 1.87 0.11 [41]

Note: a. Cellulose, Hemicellulose, Lignin; Carbohydrate, Protein, Fat; b. Molar ratio; c. Bagasse; d. Calculated. (notes should be cited in order)


3.2.1 Physical Properties

3.2.1.1 Viscosity

Viscosity is a measure of flow behaviour of a fluid and an important quantity

in many fluid flow calculations. For an organic compound its viscosity is related to

its chemical structure. Boelhouwer et al. [42] concluded that straight chain

hydrocarbons have higher viscosities than branched hydrocarbons, and alcohol or

acid groups have more effect on viscosity compared to esters and ketones.

Kinematic viscosity is more commonly used for fuels. High-viscosity fuel will

not be well-atomised, leading to poor combustion [43], increased engine deposits,

and higher energy requirements for fuel pumping [44]. Moreover, higher fuel

viscosity has been observed to increase carbon monoxide (CO) and UHC [45]. In

contrast, very low fuel viscosity leads to poor lubrication of fuel injection pumps,

causing leaks and increased wear [46]. This results in biodiesel standards having

upper and lower limits in kinematic viscosity.

3.2.1.2 Density

In fuels, density is related to the energy content for a given volume. Since

the engine injection system measures the fuel by volume, a higher density fuel will

have a greater power output from combustion of a larger fuel mass [44]. Density

has also been correlated with increases in nitrogen oxides (NOx) [47, 48], PM [48],

CO, and UHC [49] in emissions. The heating value and cetane number are also

both related to density [50]. In literature and in legislated standards, specific

gravity is sometimes reported instead of density.

3.2.1.3 Heating Value

The fuel heating value is a common criterion for evaluating a liquefaction

process. The heating value is a quantitative representation of the biocrude’s

energy content [51], which can be used to evaluate efficiency of converting

feedstock to fuel. This quantity also gives the energy density of the fuel, which

dictates how much energy is released with each volume of fuel injected into the

combustion chamber. Heating value can be presented as a higher heating value

(HHV) or a lower heating value (LHV). The HHV includes the heat released after

condensation of water in the combustion products, while the LHV does not. In

fuels, HHV has been correlated with chemical composition given by ultimate [52]


and proximate [53] analyses. Recently, this approach has been applied for HTL

biocrudes. Correlations state that heating value is directly proportional with the

elemental composition, with carbon and hydrogen increasing heating value and

oxygen and nitrogen having a negative effect [54]. However, it is the experience of

the authors that traditional correlations do not closely match experimental data

for HTL biocrudes [39, 40] and so existing correlations should be modified. While

HHV quantity is not regulated, it is prudent to produce biofuels with heating values

similar to conventional fuels to ensure minimal modifications to engines,

particularly in injection technology.

3.2.2 Chemical Properties

3.2.2.1 Oxygen Content

Liquefaction biocrudes have significant oxygen content resulting from the

depolymerisation of biomass components (i.e., cellulose, hemicellulose and

lignin). These oxygenated compounds take the form of organic acids, alcohols,

ketones, aldehydes, sugars, furans, phenols, guaiacols, syringols, and other

oxygenates [13]. In crude oil refining, oxygen is removed to prevent poisoning of

catalysts in the reforming process [55]. Studies correlating oxygen content to fuel

properties, engine operation and performance have been done on biodiesel.

Lower CO emissions [56] and PM [57] have been observed for relatively highly

oxygenated fuels such as biodiesel.

3.2.2.2 Nitrogen Content

Nitrogen in fuel may interact with degradation products and form solid

deposits [58]. Nitrogen content is not regulated by diesel or biodiesel standards,

although in crude oil refining, nitrogen content is reduced through hydrotreatment

to minimise catalyst deactivation and improve diesel stability [55].

Biocrude from HTL of lignocellulosic materials usually has low levels of

nitrogen with a maximum of 2% [14, 16, 21, 22]. Higher levels of nitrogen have

been reported for biocrudes produced from garbage, wastewater sludge, and

algae (up to 10%) due to the protein content of the

feedstock [25, 27, 30-32].


3.2.2.3 Sulphur Content

The sulphur content of fuel is a regulated quantity as burning sulphur in fuel

produces sulphur oxides [55] and sulphate particles that contribute to PM

emissions [59]. Moreover, sulphur can cause increased cylinder wear and deposit

formation [59]. ASTM D975 [58] and D6751 [60] limits sulphur content in diesel

and biodiesel, respectively, to 15 ppm.

Lignocellulosic materials and algae have very minimal sulphur content.

Biocrude has been produced with only 0.1–1.3 wt% sulphur [21, 31, 33, 35].

Biochar, on the other hand, has a higher sulphur content [13, 61], which may

mean reactions in liquefaction favour sulphur binding into compounds in the solid

fraction.

3.2.2.4 Chemical Composition

Diesel is mainly composed of alkanes, alkenes and aromatics [62], while

biodiesel is more oxygenated, comprised of fatty acid methyl/ethyl esters [63].

HTL biocrude, on the other hand, is a complex mixture of oxygenated organic

chemicals [13, 64], aliphatics, sugars, oligomers, nitrogenous aliphatics, and

nitrogenous aromatics [65]. Table 3.2 shows the main chemical groups for

biocrude.

The chemical composition of biocrudes is usually determined through gas

chromatography-mass spectrometry (GCMS). However, the vast amount of

components and high complexity of the biocrude prevent effective

chromatographic separation, resulting in broad background signals [66]. More

recent studies have used nuclear magnetic resonance (NMR) spectroscopy [67]

and Fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) to

perform analyses with higher resolution and accuracy [68].

Table 3.2. Groups of chemicals of hydrothermal liquefaction biocrude.

Main Components Area%* Range References

Phenolics 6%–65% [14, 20, 39]

Esters 2%–44% [14, 27, 39] Aromatics and heterocyclics 6%–35% [14, 39]

Aldehydes 0–18% [14, 20]

Carboxylic acids 2%–40% [20, 27, 35] Ketones 0–38% [20, 27, 35, 40]

Alkanes 9%–13% [35, 40]

Nitrogenates 12%–23% [35, 40]

Note: *. Area % from gas chromatography-mass spectrometry results.


The effects of varying compositions on the physical properties of diesel and

biodiesel have been studied, while for HTL biocrudes these relationships have not

been elucidated. Table 3.3 shows the properties of various groups in diesel and

their effect on fuel properties. In biodiesels, chain length and unsaturation of fatty

acids are usually correlated to properties. Increasing chain length increases

cetane number (an indication of ignition quality; Section 3), heating value and

viscosity, while increasing unsaturation in fatty acids decreases viscosity and

cetane number, but increases density and volumetric heating value [69]. Although

these relationships are for diesel and biodiesel they provide an idea of the

potential effects chemical composition may have on the physical properties of HTL

biocrude.

Table 3.3. Properties of various chemical groups and their effect on diesel properties [62].

Group Ignition Quality Heating Value Density

n-Alkanes Good Low Low Isoalkanes Low Low Low

Alkenes Low Low Low

Cycloalkanes Moderate Moderate Moderate Aromatics Poor High High

3.2.3 Key Fuel Properties

These final fuel properties may not be directly influenced by upgrading

processes; however, some consideration should also be given to improving them

when processing biocrude. Brief discussions of some key fuel properties to be

considered are provided here.

3.2.3.1 Cetane Number

The Cetane Number (CN) is related to the fuel ignition delay time. Dorn et al.

[69] determined the relationship between fuel components and CN. Normal

alkanes increase cetane number the most, followed by branched alkanes, normal

alkenes, branched alkenes, cycloalkanes, and aromatics. A high CN signifies good

ignition quality, good cold start properties, minimal white smoke in exhaust [46],

and low UHC [45] and CO emissions [45, 48]. On the other hand, a low CN is

related to a longer ignition delay time, which leads to higher amounts of injected

fuel mixed prior to combustion. This then causes high rates of combustion and

pressure rise that manifests as diesel knock. This also brings about premixed

burning that leads to high combustion temperatures and increased NOx [45, 70].


3.2.3.2 Vapour Pressure

Total vapour pressure of the fuel is dependent on the interactions of

components within the mixture. Vapour pressure of a mixture can be estimated

through the use of activity coefficients and thermodynamic models [71]. These

models demonstrate the dependence of vapour pressure on fuel chemical

composition. As a fuel property, vapour pressure affects performance of fuels,

especially during cold start conditions [59]. However, a high vapour pressure is a

concern due to higher fuel evaporation that contributes to increased hydrocarbon

emissions [71].

3.2.3.3 Oxidation Stability

Oxidation stability describes the resistance to oxidation of fuel during

storage. Biodiesel is degraded more easily than diesel due to the presence of

double bonds in ester chains [46]. In HTL biocrude the oxidation stability of

upgraded fuels has not been investigated, however, stability of pyrolysis and HTL

products has been observed. This is further discussed in Subsection 3.2.

The physical and chemical properties dictate how appropriate the fuel is for

combustion in transportation engines. A number of studies, such as those referred

to earlier in this section have discussed effects of biodiesel properties to diesel

engine operation. Fundamentally, molecular weight and branching of organic

molecules affect intermolecular attractions and subsequently physical properties.

The presence of aromatic rings, nitrogen and oxygen also affect physical

properties. These properties inform the selection of pathways to upgrade biocrude

to transportation fuels.

3.3 Upgrading Processes

Due to the similarity of pyrolysis and HTL, recent research on HTL biocrude

upgrading has so far focused on upgrading technologies that had previously been

studied for pyrolysis bio-oils. The upgrading processes for pyrolysis bio-oils were

themselves inspired by petroleum refining technologies. Although the authors

draw on learnings from the pyrolysis literature, we have applied the understanding

to HTL conversion. Upgrading processes discussed in this section are shown in

Figure 3.2.


Figure 3.2. Block diagram of upgrading processes discussed in this section.

3.3.1 Separation

Products of HTL are usually a gas phase portion, a liquid oily fraction, a liquid

aqueous fraction and solid residue. Studies on HTL and pyrolysis of biomass use

a number of physical separation methods, mainly done as part of the work-up to

segregate product fractions for analysis. These methods can realistically carry out

separations to isolate high-value products or facilitate further processing to

produce fuel and high-value products. Removal of water content in the oil fraction

is specifically important for suitability for upgrading processes, as water may

cause catalyst inactivity [72].

3.3.1.1 Solvent Extraction

Addition of a solvent to the two-phase product can enhance separation and

extraction. The liquid product can be decanted to separate aqueous and oil

portions. This crude separation results in an oil fraction with a moisture content of

around 5% [12]. The choice of solvent will primarily be based on its immiscibility

with water to facilitate separation, and its efficiency to extract the organic

components and maximise yield. Selection of an appropriate solvent can be done

through a number of methods. One such method is the use of the Robbins’ chart

of solute-solvent interactions, which describes effect of functional groups on

solubility based on hydrogen bonding and electron donor-acceptor interactions

[73]. Use of this chart will be beneficial when targeting extraction of chemicals

with specific functional groups. However, due to the complexity of biocrude as a

mixture, many researchers have investigated various chemicals to determine the

most appropriate solvent. The most efficient solvent is somewhat dependent on

the composition of the biocrude and hence, the original feedstock.


Solvent polarity is a key parameter for consideration in choosing appropriate

solvents. With the abundance of polar compounds in biocrude and bio-oil, polar

solvents are often more appropriate for extraction. This was confirmed by Garcia-

Perez et al. [74] by performing successive extractions of bagasse pyrolysis bio-oil

using solvents of increasing polarity. The solvents used were, in this order,

pentane, benzene, dichloromethane, ethyl acetate, and methanol. The fractions

that were extracted in ethyl acetate and dichloromethane were the two largest,

owing to the high polarity of the bio-oil components. Another fractionation method,

performed by Chum et al. [75] used ethyl acetate to separate a phenol fraction

from pyrolysis bio-oil.

In liquefaction studies, oils for product characterisation are usually extracted

using polar solvents such as acetone [14, 16, 17, 20, 31], tetrahydrofuran (THF)

[22], ethanol [27], chloroform [76], or dichloromethane (DCM) [25, 29, 30, 32,

33, 40, 77].

On the other hand, microalgae biocrude contains significant amounts of

alkanes [35, 40], which means non-polar solvents may be more effective. Valdez

et al. [78] studied the use of nonpolar solvents hexadecane, decane, hexane, and

cyclohexane, and polar solvents methoxycyclopentane, dichloromethane, and

chloroform to separate biocrude from products of algae liquefaction. Higher yield

was obtained from nonpolar solvents due to the similarity of the long carbon chain

solvents to chemicals in biocrude, as confirmed by GCMS analysis. On the other

hand, polar solvents extracted oil with higher carbon content, with the researchers

posing that these solvents recovered carbon-rich compounds akin to resins and

asphaltenes. Table 3.4 (next page) shows the oil fraction yields of HTL of different

feedstock at the optimum condition reported, without use of catalysts or

pretreatment, where the biocrude was extracted from the liquid product with a

solvent.


Table 3.4. Yields of solvent extraction of HTL biocrude from the liquid fraction using polar and

non-polar solvents.

Feedstock Solvent Yield References

Acacia mangium wood Acetone 32% [21]

Ailanthus wood Acetone 29% [17]

Bagasse Acetone 59% [14]

Acetone 31% [21]

Bagasse pith Acetone 30% [21]

Banana stem Acetone 21% [21]

Beech wood Acetone 28% [16]

Acetone 34% [17]

Botryococcus braunii microalgae Dichloromethane 58%c [32]

Cattle manure Dichloromethane 49%a,b [29]

Chaetomorpha linum macroalgae Dichloromethane 17% [33]

Chlorella microalgae Dichloromethane 42% [77]

Cladophora coelothrix macroalgae Dichloromethane 20% [33]

Cladophora vagabunda macroalgae Dichloromethane 28% [33]

Coconut husk Acetone 28% [21]

Coconut shell Acetone 34% [21]

Corn stalk Acetone 28% [21]

Corncob Acetone 76% [17]

Cypress wood Diethyl ether 15% [18]

Derbesia tenuissima macroalgae Dichloromethane 33% [33]

Dunaliella tertiolecta microalgae Dichloromethane 37% [30]

Dunaliella tertiolecta cake Chloroform ~22% [76]

Garbage Dichloromethane ~22% [25]

Hazelnut seedcoat Acetone 22% [17]

Hazelnut shell Acetone 22% [16]

Acetone 28% [17]

Kenaf Acetone 28% [21]

Metroxylon sp. petioles Acetone 23% [21]

Metroxylon sp. stem Acetone 29% [21]

Nannochloropsis salina microalgae Acetone 46% [31]

Nannochloropsis sp. microalgae Chloroform 35% [78] Dichloromethane 30% [78]

Methoxycyclopentane 32% [78] Hexane 32% [78]

Hexadecane 38% [78] Decane 39% [78]

Cyclohexane 34% [78]

Oedogonium sp. macroalgae Dichloromethane 36% [33]

Oil-palm empty fruit bunch Acetone 33% [21]

Oil-palm fruit press fiber Diethyl ether 19% [23]

Oil-palm husk Acetone 27% [21]

Oil-palm petioles Acetone 23% [21]


Feedstock Solvent Yield References

Oil-palm shell Acetone 36% [21]

Olive husk Acetone 23% [17]

Pine bark Acetone 21% [17]

Pineapple leaf Acetone 24% [21]

Rice husk Diethyl ether 1.7% [20] Acetone 29% [21]

Rice straw Acetone 23% [21] Tetrahydrofuran 40% [22]

Rubber tree Acetone 31% [21]

Scenedesmus sp. microalgae Dichloromethane 34% [40] Hexane 31%

Sewage sludge Ethanol ~55% [27]

Spirulina platensis Acetone 38% [31]

Spruce wood Acetone 26% [16] Acetone 26% [17]

Tea waste Acetone 23% [16]

Ulva ohnoi macroalgae Dichloromethane 30% [33]

Note: a. NaOH was used as catalyst; no run without catalyst; b. Volatile content basis; c. Organic

content basis

There are very few studies that have used more than one solvent to

fractionate the oil product. Karagöz et al. [20] separated the liquid product of

liquefaction of sawdust and rice husk with diethyl ether and ethyl acetate. The

solid fraction was also washed with acetone to obtain adhering oils. The largest oil

fraction was obtained from the acetone wash, containing mostly phenolic

compounds. The fraction obtained from extraction with diethyl ether has the

second highest yield and is also comprised of mostly phenolics.

3.3.1.2 Distillation

Another separation process that can be applied is distillation. Its ubiquity in

petroleum refining makes it a likely candidate for industrial scale biocrude

fractionation. There are various methods of distillation, and process selection

depends on physical and chemical characteristics of the feed and the range of its

components. Fractional distillation separates components using differences in

boiling points. Vacuum distillation operates at reduced pressures, lowering the

boiling point and enabling separation of components in less severe temperatures,

preventing cracking or decomposition of components. Steam distillation uses

steam to lower the partial pressure of the mixture, reducing the boiling point of


components. Molecular distillation employs pressures below 1 Pa to separate

components without the pressure exerted by the gaseous phase [51], thus the

separation relies on differences of mean free paths of each component [79]. A

typical continuous industrial scale distillation set-up is shown in Figure 3.3.

Distillation has been used in studies that characterise pyrolysis bio-oils. The

temperatures used ranged from 100–250 °C for atmospheric distillation and 80–

230 °C for vacuum distillation [79-82]. The ranges can be attributed to the varying

tendency of bio-oil for cracking and polymerisation [83] when it reaches a certain

temperature. Vacuum and molecular distillation allow for separation at lower

temperatures to minimise thermal degradation. Removal of moisture is also a key

result of distillation, to a resulting moisture content of 0.49% to 6.46% in middle

and heavy fractions [79-81].

Figure 3.3. Continuous Binary Fractional Distillation. [84]

Fractional distillation of corn stover pyrolysis bio-oil was performed by

Capunitan and Capareda [80] at both atmospheric and reduced pressures. The

bio-oil was separated into three fractions. In atmospheric distillation, 84% of the

bio-oil was recovered and at 500 mbar, 73% was recovered. Dramatic reduction

of moisture was reported for the middle and heavy fractions, as well as a reduction

of total acid number of the heavy fraction. These results were attributed to

separation of water to the light fraction and of acidic components to the middle

fraction.

Vacuum distillation and two stages of molecular distillation of pyrolysis bio-

oil in series were conducted by Guo et al. [79]. Vacuum distillation was performed


only as a pre-treatment step to remove water and light hydrocarbons. The product

of vacuum distillation was fed into the first molecular distillation at 1600 Pa and

the process yielded 26% bio-oil. The product of the first molecular distillation was

fed into the second molecular distillation at 340 Pa and yielded 23%. It was

observed that viscosity of the light fractions was less than the original bio-oil since

lower molecular weight compounds were separated into these fractions.

Furthermore, the separations conducted and a detailed analysis of chemicals in

each fraction using GCMS led the researchers to conclude that acids and ketones

are easier to separate, compared with aldehydes and phenols, while diphenols

and sugars cannot be removed by distillation.

Steam distillation was used by Murwanashyaka et al. [82] to obtain a phenol-

rich fraction and separate syringol from wood pyrolysis bio-oil at 105 °C. While

this study was geared towards isolation and purification of valuable chemicals,

this might also be a pathway to remove unwanted compounds or concentrate

compounds with heteroatoms for more focused processing.

At this time, there are limited studies on distillation of HTL products before

hydrotreatment, since separation is not necessary to analyse products.

3.3.2 Hydrogenation

As discussed in the previous section, hydrogen and oxygen content is directly

correlated to the biocrude’s heating value. A way to improve heating value is to

increase hydrogen content and remove oxygen content [13]. Hydrogenation is a

process used in petroleum refining to increase saturation of hydrocarbons and

remove sulphur, nitrogen, and oxygen. This is done to prevent catalyst deactivation

in further processing, to minimise coking, and to improve fuel characteristics [55].

Another issue is the unstable nature of pyrolysis and liquefaction products

due to polymerisation or degradation of components. Adjaye et al. [61] studied the

stability of wood HTL bio-oil and observed an increase in viscosity and the amount

of residue collected after distillation. Jena et al. [35] observed an increase of 73%

in viscosity of algae HTL biocrude over 90 days. This suggests reactions of

aldehydes and organic acids and an increase in the amount of higher molecular

weight compounds due to polymerisation and condensation [85]. These potential

reactions in biocrude can be addressed by hydrogenation. Several hydrogenation


methods and processes that have been investigated are discussed in this section.

Typical reactions are shown in Figure 3.4.

Figure 3.4. Typical reactions in hydrogenation and cracking processes [86].

3.3.2.1 Hydrogen-Donor Solvents

Addition of a hydrogen-donor solvent improved stability of pyrolysis and

liquefaction oils and prepared the bio-oil for further upgrading processes. Studies

in catalytic hydrodeoxygenation (HDO) observed thermal decomposition,

production of coke and decrease in catalyst activity [87, 88] when processing

pyrolysis bio-oils.

Solvents such as tetralin, methanol, and ethanol [85] have been used to

arrest free radical polymerisation. Adjaye et al. [61] added tetralin to wood HTL

biocrude and observed stable viscosities over 31 days. It was also important to

note that the amount of oil decreased and the amount of residue increased in

samples without tetralin, compared to a stable composition in samples treated

with tetralin. Rezzoug and Capart [89] added tetralin to wood liquefaction biocrude

prior to catalytic hydrogenation and obtained increasing light fractions with

increasing tetralin/oil ratio. Diebold and Czernik [90] studied ethyl acetate,

ethanol, acetone, methanol, a mixture of methyl isobutyl ketone and a mixture of

methanol and acetone to wood pyrolysis bio-oil and observed the effect of each

additive over accelerated aging at 90 °C. Of all the solvents, the sample with 10%

methanol had the slowest aging rate, measured as change in viscosity over time.

It was further inferred that the low aging rate was caused by molecular dilution

and formation of intermediate products that hinder polymerisation.


3.3.2.2 Mild Hydrogenation

Mild hydrogenation prior to a more severe hydrogenation process was

proposed in several studies of pyrolysis bio-oils due to reports of coking during the

severe HDO step [85]. This initial step uses catalysts similar to a typical

hydrogenation process, but with less severe conditions. Processes are run with

temperatures below 300 °C, and lower hydrogen pressure is required. This

process aims to stabilise the bio-oil and reduce reactive oxygen sites that can

produce char [72]. In several studies, mild hydrogenation resulted in higher

thermal stability and reduced char formation [91] due to hydrogenation of

aldehydes and ketones in the initial step [72]. It can then be inferred that lower

coking has decreased catalyst inactivity and resulted in a higher oil yield [88].

A review of mild hydrogenation studies by Diebold [85] revealed reduction in

oxygen content but an increase in viscosity of bio-oil. Furthermore, a study by Elliot

and Baker [91] observed that HTL bio-oil can be directly subjected to HDO, unlike

pyrolysis bio-oils which need to undergo low temperature treatment. This posits

that mild hydrogenation prior to a more severe hydrogenation is an option for HTL

biocrude that has properties similar to pyrolysis bio-oil, such as high oxygen

content and high amounts of carbonyl-containing groups.

3.3.2.3 Hydrodeoxygenation (HDO) with Metal Catalysts

Hydrodeoxygenation, which consists of hydrogenation and oxygen-removal

processes, is done to improve the properties of the HTL product and bring it as

close as possible to petroleum fuels or biodiesel. HDO of biocrude involves high

temperature, high hydrogen pressure, and the use of a catalyst to provide the right

conditions for the hydrogenation process to proceed. Oxygenated components

mentioned in Section 2 are the target chemicals in this process. Ease of oxygen

removal depends on bonding of the heteroatom and steric effects [86]. A study of

HDO of model compounds in a cobalt-molybdenum (CoMo) catalyst proposed a

low-temperature reactivity ranking of the various categories of components in

pyrolysis bio-oil. It was concluded that ketones have high reactivity, followed by

carboxylic acids, phenols and furans. It was also postulated that saturation of

double bonds and HDO of alcohols and ethers will occur at a lower temperature

than what is required for a ketone [92]. These conclusions were based on

observed activation energies, temperature of identical conversions and hydrogen


consumption of the HDO of the model chemicals, which are shown in Table 5. A

study by Laurent and Delmon [93] also observed a similar trend in activity of

groups in HDO of pyrolysis bio-oil. They further observed that decarboxylation

occurs alongside the HDO of carboxylic groups, and carbon is being converted to

CO2.

Table 3.5. Activation energies, iso-reactive temperature and hydrogen consumption of

hydrodeoxygenation of model compounds with a CoMo catalyst, presented by Grange et al. [92].

Chemical/Group Activation Energy

(KJ/mol)

Iso-Reactive

Temperature (°C)

Hydrogen

Consumption

Ketone 50 203 2 H2/group

Carboxylic acid 109 283 3 H2/group

Methoxyphenol 113 301 ~6 H2/molecule

4-Methylphenol 141 340 ~4 H2/molecule

2-Ethylphenol 150 367 ~4 H2/molecule

Dibenzofuran 143 417 ~8 H2/molecule

The use of CoMo and nickel catalysts is common for hydroprocessing in oil

refineries. The selection of appropriate catalysts depended on the properties of

crude oil, including metal, nitrogen, and sulphur content [94]. Therefore,

application of oil refining processes and selection of appropriate catalysts for the

hydrotreatment of biocrude will have different considerations, some of which have

been investigated in various studies.

An early study by the Pacific Northwest National Laboratory [91] investigated

catalytic hydrotreatment of HTL biocrude by using model chemicals, biocrude

distillates, and whole biocrude. The study concluded that sulphided CoMo and Ni

catalysts are the most effective for HDO since these two catalysts have high

specificity, compared to other metallic catalysts tested. Furthermore, when

sulphided CoMo was used, there was less saturation of aromatic compounds,

which was desired for producing fuel similar to gasoline.

A study of 12 catalysts and eight hydrogen donors in hydrotreatment of wood

HTL biocrude was conducted by Grlic et al. [95]. It was observed that use of

sulphided NiMo/Al2O3 catalysts resulted in the highest yield, lowest viscosity and

high gross calorific value. The highest gross calorific value was obtained using an

oxide form of the NiMo/Al2O3 catalyst. Of the solvents, use of tetralin contributed

to a high product calorific value and low amounts of residue. It should be noted,


however, that products of HDO with oxided NiMo/Al2O3 had very high viscosity,

with oils sticking to reactor parts. In another study of the same group [96], effects

of process conditions were observed in upgrading wood HTL biocrude over a

NiMo/Al2O3 catalyst. Temperature was the most influential factor: an increase in

the heating value with an increase in the reaction temperature.

Several HDO studies observed that products from processes that use

sulphided catalysts have some amount of sulphur [91, 97]. To prevent adding

sulphur into the HDO product, the use of noble metal catalysts can be an

alternative to the more conventional catalysts already discussed. Noble metals

such as platinum and rhenium were used in reforming processes to increase the

octane number of fuels. However, the presence of sulphur in the feed poisons the

catalyst [55]. On the other hand, use of noble metals to upgrade biocrude is

possible because of its low sulphur. A study using Ru/C and Pd/C in

hydrotreatment of corn stover bio-oil observed 25.5% deoxygenation and a

product H/C ratio of 1.47 at 300 °C using Ru/C [98].

Metal catalysts containing Rh, Rh-Co, Ni and Ni-Cu and using SiO2, Al2O3,

ZiO2, CeO2, and CeO2-ZrO2 supports were investigated by Yakolev, et al. [99].

Anisole and biodiesel (esters) were used as model reactants. In this study, it was

concluded that the Ni-Cu catalysts were most effective in HDO, having a degree of

deoxygenation of 60%–100%. Using CeO2 as support achieved 100% HDO with Ni-

Cu and 94.6% HDO with Rh, while using ZrO2 achieved 60% HDO with Ni-Cu and

90.8% HDO with Rh. The oxide forms provided adequate oxygen vacancy on the

support surface, allowing more oxygen removal.

Subcritical water with metallic catalysts in HDO was also studied. Zhang, et

al [100] upgraded duckweed HTL biocrude in subcritical water at 350 °C using

Ru/C, Pd/C, Pt/C, Pt/γ-Al2O3, Pt/C-sulfide, Rh/γ-Al2O3, activated carbon, MoS2,

Mo2C, Co–Mo/γ-Al2O3, and zeolite. Since the duckweed biocrude had small

amounts of sulphur and nitrogen, removal of sulphur and nitrogen was also

considered in upgrading. The researchers observed that Ru/C had activity for HDO,

desulphurisation, and denitrogenation, resulting to a product with the lowest

sulphur, highest hydrocarbon content, and highest heating value. Conversely, Pt/C

was observed to have the best HDO performance.


Another pathway for hydrogenation and reduction of carbonyl groups into

methylene groups is the use of zero valent metals such as Fe, Zn, Al and Mg. Liu

et al. [101] used Zn to upgrade pyrolysis bio-oil at ambient temperature and

pressure. The experiment resulted in a minor change in % C, % H and % O; however

it was observed through NMR spectroscopy that alcohols and ethers (C–O)

increased, while ketones, aldehydes and carboxyls (C=O) decreased. GCMS

results also confirmed a reduction in carboxylic acids and aldehydes.

3.3.3 Catalytic Cracking

While hydrodeoxygenation aims to remove oxygen atoms from the biocrude,

cracking processes aim to produce lighter products with improved properties.

Thermal cracking, which was used in the early 1900s to produce gasoline from

gas oil [55], is not considered to be a viable alternative for cracking biocrudes. The

highly-oxygenated biocrudes have high coking potential [102]. Catalytic cracking,

on the other hand, has better selectivity and can be performed with milder

conditions, decreasing production of undesirable side-products like gases and

coke [102]. Using hydrogen in cracking processes is termed hydrocracking, which

is a hydrogen addition process with more severe conditions, compared to

hydrotreatment [55]. Catalysts used in cracking are natural clay materials,

synthetic amorphous silica-alumina, and synthetic crystalline zeolites [55]. One

example of a synthetic zeolite is the ZSM-5, shown in Figure 3.5. The catalytic

cracking process carries out dehydration, decarbonylation, dehydrogenation,

hydrogenation, and hydrogen-transfer reactions [103]. There have been several

studies on cracking pyrolysis bio-oils with zeolites [104, 105] but only a few for

HTL biocrudes [106-109].

Figure 3.5. Microporous molecular structure of ZSM-5.


A continuous, downflow, fixed bed reactor with HZSM-5 at 330–410 °C and

atmospheric pressure was used by Adjaye and Bakhshi [107] to upgrade wood

powder HTL biocrude. This process used cracking catalysts to upgrade bio-oil

without the use of H2. The study compared results of using HZSM-5, H-mordenite,

H-Y, silicalite, and silica-alumina. Volatile “organic distillate” fraction yields ranged

from 40%–67%, with optimum yield obtained from using H-mordenite and H-Y at

370 °C. However, the organic fraction still contained 10.7%–36.5% oxygenated

compounds. Furthermore, 4.4%–20.5% weight of coke was produced. The

researchers also studied reactions of biocrude model compounds with the HZSM-

5 catalyst. The study observed minimal coke formation and increase in cracking

of non-volatile components in low feed concentration and low temperature.

However, low conversion was also observed [106, 108]. Furthermore, cracking,

deoxygenation, aromatisation and polymerisation were proposed as the main

reactions with this catalyst, which is similar to the proposal of Corma et al. [103].

Thermal cracking and catalytic cracking were compared by Gevert and

Otterstedt [109] using alpha alumina, EKZ-4 (containing rare earth zeolite Y), and

EKZ-2 (commercial equilibrium catalyst). Better yield was achieved at 500 °C for

thermal cracking, compared to a higher temperature, owing to the sensitivity to

thermal cracking of components of the hydroprocessed biocrude. Catalytic

cracking achieved better liquid product yield when compared to thermal cracking

at the same temperature. Furthermore, it was observed that lower catalyst to oil

ratios resulted in better oil yield and lower gas and coke production.

3.3.4 Esterification

Adding alcohols to biocrude is another method for changing chemical

composition and improving physical properties. The added alcohol reacts with the

organic acids to form esters, similar to chemicals that comprise biodiesel. The

esterification reaction is shown in Figure 3.6. Using ethanol to upgrade pyrolysis

bio-oils has been investigated as an alternative to hydrogen. Zhang et al. [110]

reacted rice husk pyrolysis bio-oil with ethanol over solid acid 40SiO2/TiO2-SO42−

and solid base 30K2CO3/Al2O3-NaOH catalysts. It was observed that esterified bio-

oils had vast improvements in viscosity, density and calorific value. The amount of

esters increased 20-fold with the acidic catalyst, while also producing acetals.


Figure 3.6. Esterification reaction.

Use of these alcohols in the supercritical regime has also been considered,

exploiting superior fluid properties, the same rationale for hydrothermal

liquefaction. Peng et al. [111] compared the subcritical and supercritical

upgrading of rice husk pyrolysis bio-oil with ethanol and HZSM-5 and reported the

supercritical process as being more effective. The researchers observed that

residue after vacuum distillation of unprocessed bio-oil and upgraded product was

reduced from 38% to 15%.

The use of supercritical ethanol with Pt/C, Pd/C, Ru/C and Ru/HZSM

catalysts to upgrade rice husk pyrolysis bio-oil was studied by Chen et al. [112].

The heating value of the bio-oil increased from 21.45 to 30 MJ/kg. It was also

observed that the relative amount of desired products was achieved when Ru/C

was used. The amount of acids and methyl esters decreased, while ethyl acetate

increased. The amount of phenols also decreased, while cyclic ketones and

alcohols increased.

3.3.5 Hybrid Processes

Due to the resulting low yields and high levels of coking in some of the

upgrading processes discussed in previous subsections, there has also been

research around combining features of multiple processes into a single process.

These hybrid processes aim to encourage desired reactions and inhibit undesired

reactions.

A combined reaction-distillation process can be used to simultaneously alter

and separate biocrude components. A reactive distillation process was studied by

Mahfud et al. [113] by mixing pyrolysis bio-oil with high boiling alcohols and an

acid catalyst. Organic acids and aldehydes reacted with alcohols to form esters

and acetals, improving physical properties of the bio-oil. The reactions used a

number of alcohols and acid catalysts at 50–80 °C, while distilling at a reduced

pressure of 5 kPa. The study compared alcohols and observed that n-butanol and

ethylene glycol perform similarly to carry out esterification of the organic acids.

Liquid sulphuric acid was the best catalyst; however the solid acid catalyst used


had significant results as well. Reduction of moisture content to up to a sixth of

the initial value was achieved in reduced-pressure distillation. Different solid

catalysts and solvents were used by Xu et al. [114]. Zirconium-containing

mesoporous molecular sieve SO42−/Zr-MCM-41 as a solid acid catalyst, and

ethanol and hydrogen peroxide as solvents were used in the reactive rectification

study. The process resulted in a yield of 21% of product with improved density,

water content, heating value and pH.

A one-step upgrading process involving hydrotreatment, esterification and

cracking at the same time was proposed by Tang et al. [115]. The process used

supercritical ethanol, a hydrogen atmosphere and a Pd/SO42−/ZrO2/SBA-15

catalyst to upgrade rice husk pyrolysis bio-oil. It was observed that properties of

the bio-oil, such as viscosity, density, pH and heating values improved, with the

process generating trace amounts of tar. This was attributed to conversion of large

molecular weight compounds and esterification of acids.

Another “one-pot” conversion process using Ni/ZrO2 in supercritical

cyclohexane at 300 °C was conducted by Shi et al. [116] to upgrade cornstalk

HTL biocrude, obtaining 81.6% carbon yield with 90% diesel and jet fuel

hydrocarbons. The upgraded oil had 0.75% oxygen, from 26.79% in the feed

biocrude, and a HHV of 46.86 MJ/kg.

Each upgrading process aims to improve the properties of the biocrude to

make it an acceptable fuel. Table 3.6 (next page) summarises the information

discussed in this section and shows the effect of each process to biocrude

properties.


Table 3.6. Upgrading processes and their effect on physical and chemical properties. Direct

influence of processes to biocrude property towards standard values.

Upgrading

Process

Upgrading

Mechanism

Vis

co

sit

y

De

nsit

y

He

ati

ng

Va

lue

O-C

on

ten

t

N-C

on

ten

t

S-C

on

ten

t

Ch

em

ica

l

Co

mpo

sit

ion

Ref

Solvent

Extraction

Separation from water;

increasing organic

yield.

√ √ √

[14, 16, 17, 20-

22, 25, 27, 29-

33, 35, 40, 76,

78]

Distillation

Removal of water;

separation of light from

heavy components.

√ √ √ [79-82, 112,

113]

Addition of

hydrogen-donor

solvents

Provision of hydrogen

in liquid phase for

stability.

√ √ √ √ √ [61, 85, 89, 90]

Mild

hydrogenation

Provision of hydrogen

in gas, hydrogenation

reaction in mild

conditions.

√ √ [72, 85,

91, 92]

HDO/HDN/ HDS

Hydrogenation in

severe conditions to

remove heteroatoms.

√ √ √ √ √ [86, 92-101,

115]

Cracking

Cleavage of high

molecular weight

compounds.

√ √ √ √ √

[102-

109, 115]

Esterification Conversion of organic

acids to esters. √ √ √

[110-115]

3.4 Challenges and Future Research Prospects

As remarked earlier, there is a far larger quantum of research on upgrading

pyrolysis bio-oils compared to HTL biocrude. This has been demonstrated in the


range of technologies that have been investigated for pyrolysis bio-oil upgrading.

Taking into account the advantages of liquefaction over pyrolysis, there is

adequate imperative to advance research in upgrading HTL biocrude to

transportation fuels. However, there are still major hurdles in development of a

commercially-competitive integrated process, starting from the significant capital

needed for the HTL process alone [116]. Challenges in upgrading HTL biocrude

can be translated to opportunities for researchers to develop cost-efficient and

sustainable technologies.

3.4.1 Economic Considerations

Production of second-generation fuels uses low-cost feedstock but incurs

significant capital costs. For liquefaction, a study estimated that using the current

state of technology (SOT), the minimum selling price of woody biomass HTL fuel is

US$ 4.44/ gallon gasoline equivalent (GGE) [117]. This is assuming a two-stage

hydrotreatment process. In 2050, it is projected that the cost of production will be

around US$ 3.03–3.79/GGE (0.80–1.00 per litre), depending on development of

more cost-effective technology and process improvements [9]. In the same

techno-economic study, Zhu et al. [117] obtained the goal case minimum selling

price at US$ 2.52/GGE, assuming less organics lost to the water phase and a

hydrocracking unit added to a single-step hydrotreatment process. The HTL and

upgrading units comprise 61% of total installed costs for the SOT case and 49%

in the goal case. Other costs such as those related to consumption of hydrogen

and catalysts, waste treatment and disposal also differ according to efficiency of

processes. Adding a hydrocracker for the goal case and increasing product yield

eliminates costs associated with selling the heavy oil produced in HTL as a by-

product, and offsets capital costs in installing hydrocracking equipment.

In another techno-economic study by another group led by Zhu [118], the

minimum fuel selling price of products of HTL of lipid-extracted algae (LEA) was

between US$ 2.07–7.11/GGE. The study included an HTL process, a

hydrotreatment unit, a hydrocracking unit, and three separation columns. The

highest capital cost was determined to be the hydrotreatment process, which was

39% of the total installed cost. The feedstock price affects price sensitivity the

most, followed by product yield and upgrading equipment cost.


In these studies, it can be observed clearly that upgrading processes impact

immensely on product price. As technologies improve, product yield and quality

improves, translating to better revenues, offsetting installation costs. Moreover,

cheaper and more efficient technologies can also reduce installation and

operating costs. Lastly, hydrogen requirements and waste treatment and disposal

costs may also be reduced.

3.4.2 Sustainability

Research in biofuels has been advanced under the banner of sustainable

energy. Biofuels are sustainable in that they are produced from renewable sources

[119]. Furthermore, production of fuels through HTL emits less greenhouse gases

than production from fossil fuels [120, 121]. Liu et al. [121] determined that the

energy return on investment (EROI) of producing fuels through HTL of algae has a

value of approximately 1, i.e., the energy output of a fuel produced by HTL is almost

equal to the energy input, using current state-of-the-art technologies. This is due

to the relatively simple heat recapture and efficiency measures in the HTL process.

Opportunities to improve EROI are identified in production of upstream nutrients;

however, upgrading will also be a significant energy burden [121]. There is a

possibility to use upgrading technologies that use less energy than conventional

refining as algae biocrude has lower sulphur and heavy metal content, reducing

the need for additional heteroatom removal processes [121]. In a life cycle

analysis by Frank et al. [122], energy use in upgrading is significantly affected by

hydrogen gas consumption in deoxygenation and denitrogenation, which was

determined using the Greenhouse Gases, Regulated Emissions, and Energy Use

in Transportation (GREET) model. These findings emphasise that energy

consumption in upgrading is affected by the quality of the biocrude being

upgraded, or the efficiency of hydrogenation processes.

Other emissions of the biofuel production process are wastewater and

residues. Zhu et al. [117] proposed a scenario of effective separation of organics

from water after HTL that reduces wastewater treatment costs by 50%.

While offgas from HTL and upgrading units can be processed to be used in

hydrogenation processes, the hydrogen consumption will depend on oxygen

content of biocrude, and effectiveness of hydrogenation. Deficit in H2 from these


processes can be supplemented by a hydrogen plant, which uses natural gas as

feed [117]. This demonstrates a process which is still dependent on a fossil fuel.

Further research on HTL and upgrading technologies can be directed

towards improving process and separation efficiencies to minimise wastes and

emissions, decrease hydrogenation burden, and improve conversion and use of

by-products through a biorefinery approach that advances sustainable biofuel

production [123].

3.4.3 Oxygenated Biofuels

As mentioned in Section 3.2.4, high oxygen fuels have been linked to better

emissions. There have been numerous studies on emissions of biodiesels and

other oxygenated fuels. However, research on the effects of fuels from HTL or

pyrolysis on emissions and engine performance is just emerging. Zhou et al. [124]

studied the overall combustion performance of cellulose and lignin derivatives di -

n-butyl ether and anisole, as blended with diesel. The study observed a decrease

in PM; however, the concentration of anisole was not beneficial to overall

performance and emissions. Results seem promising and further research can be

geared towards understanding the effects of upgraded biocrudes on performance

and emissions. Consequently, this presents an opportunity for different

technologies for upgrading to be investigated, focusing less on reducing oxygen

content, and more on other properties.

3.5 Author Contributions

This work was developed and written by J.A. Ramirez. It was conceived with

T.J. Rainey and R.J. Brown who both provided major editorial contributions and

guidance.

3.6 Conflicts of Interest

The authors declare no conflict of interest.


3.7 References and Notes

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Chapter 4: Liquefaction biocrudes and their

petroleum crude blends for

processing in conventional

distillation units

Jerome A. Ramirez, Richard Brown and Thomas J. Rainey

Biofuel Engine Research Facility, School of Chemistry, Physics and Mechanical

Engineering, Science and Engineering Faculty, Queensland University of

Technology, 2 George St, Brisbane, Queensland 4000, Australia

Published in Fuel Processing Technology, 167 (2017), Pages 674-683









publication;









Title: Liquefaction biocrudes and their petroleum crude blends for processing in

conventional distillation units (2017, published)



Richard Brown Provided editorial contributions

Thomas Rainey Assisted with preparing the manuscript and experimental

design; Provided editorial contributions.





Name Signature Date


Liquefaction biocrudes and their petroleum crude

blends for processing in conventional distillation units

Jerome A. Ramirez, Richard Brown and Thomas J. Rainey*




*corresponding author

Abstract: Various biocrudes were characterised by their distillation curves using

simulated distillation. Biocrude and petroleum crude blends were generated and

analysed to assess their compatibility for processing in conventional petroleum

refineries. Distillation curves of biocrudes varied widely but were comparable to

petroleum crude oil in shape and distillation range. Blending biocrude with

petroleum crudes in the laboratory presented some challenges in miscibility;

however, blending at higher than ambient temperatures, which more closely

represents industrial practice, improved miscibility as shown in the FTIR spectra

of blends, and close matching of the warmer blends to models. The distillation

curve of the biocrude-petroleum crude blends followed the modelled blends in

ASPEN Plus. Petroleum analysis and modelling methods were also demonstrated

as reliable tools to analyse biocrudes and their blends with petroleum crude. The

study verifies ASPEN’s utility in modelling biocrude distillation for processing in

refinery distillation columns either as a blend or separately.

Keywords: biocrude, distillation, ASPEN, liquefaction, biocrude upgrading


4.1 Introduction

Biomass remains a reliable source of energy despite the changing

requirements to meet environmental and supply demands. Its potential to be

converted to liquid fuels with a higher energy density enables its use for

transportation and heating. Liquid fuels from biomass are also considered

sustainable alternatives to fossil fuels due to the renewable nature of biomass

and the carbon dioxide uptake of cultivating plants or algae [1]. Deng et al. [2]

projected a likely global biofuel potential of 10 GJ per capita per year in 2070,

which when compared with the projected demand of up to 17 GJ per capita per

year, falls short. This is in consideration of sustainability and technical limitations

known at this time.

The constraints of biofuel production and the proven technical and financial

ease of using fossil fuels would mean that measures to reduce CO2 emissions may

involve blending of biofuels with conventional fuels instead of a complete phase-

out. This has been implemented in many jurisdictions by mandating biofuel blends

of ethanol in gasoline and biodiesel with petroleum diesel. Emissions from the use

of oxygenated alcohol or biodiesel blends present environmental and health

benefits and challenges [3, 4].

Untapped potential biofuel streams are from novel biofuels produced from

second- and third-generation biomass [5]. In particular, pyrolysis bio-oils and

liquefaction biocrudes have not made their way into transportation vehicles due

to the undesirable effects of their direct use. These products of thermochemical

conversions have lower heating value, higher oxygen content, higher viscosity, and

higher concentration of chemical species that produce nozzle-blocking coke than

conventional fuels [6, 7]. Furthermore, pyrolysis bio-oil, when compared with

liquefaction biocrudes, is less stable due to its reactive chemical composition [7].

Therefore, further processing is suggested to upgrade the properties of these

biofuels.

One pathway to produce biofuels is through a biorefinery, which was

conceived as an analogue of the petroleum refinery that produces many different

commodity and specialty product streams [8]. A biorefinery can produce chemicals

derived from cellulose, hemicellulose and lignin by employing technologies such

as chemical and thermal pre-treatment, and thermochemical and biochemical


conversion that break down the polymeric composition of biomass. The

intermediates can then be further refined to platform chemicals, biopolymer

precursors, or fuel [9]. Several studies have estimated the economic viability of

such biorefineries. A biomass liquefaction fuel plant in the United States was

estimated to cost US$1 million per MWh of biomass feedstock input (lower heating

value basis, 2016 dollars), with a minimum fuel selling price (MFSP) of US$2.52

per gallon of gasoline equivalent (GGE), while a similar plant in Finland costs

US$1.87 million per MWh of biomass feedstock input (lower heating value basis,

2016 dollars) and a breakeven price of US$7.11 per GGE [10, 11]. This is fairly

comparable with other biorefinery scenarios [12]. While financial viability was

demonstrated, these studies are in consonance that the technologies are not well -

developed to make these biorefineries competitive with conventional petroleum

refineries.

Potentially, biocrudes may be co-processed in existing petroleum refineries

to reduce the capital cost of converting biocrudes into fuels. Co-processing could

allow biocrudes to be integrated into fuel and chemical supply and distribution

lines. Moreover, augmenting crude oil inputs with biocrude allows for some

stability by managing fluctuations to supply, as well as taking advantage of

economies-of-scale in biocrude processing. This has been proposed for other

types of bio-based crude oils [13, 14].

To enable co-processing, the behaviour of biocrudes in complex refinery

operations must be understood. Of particular interest is fractional distillation,

which is a major unit operation in a refinery. Several studies have demonstrated

the vacuum batch distillation of biocrude [15, 16] and blending with petroleum

crude oil [17]. It is therefore important to determine where in the refinery process

it is most appropriate for biocrude to be integrated. Blending biocrude to

petroleum crude prior to distillation is a potential drop-in point; however, it is

important to understand the characteristics of biocrudes and their blends with

petroleum crude. Biocrude and petroleum crude, with components having

different polarities, could have poor miscibility. While most laboratory

investigations focused on the properties of biocrudes at room temperature,

distillation feed in refineries is preheated and so the effect of temperature on

miscibility was explored. Developing a deeper understanding of biocrude


distillation properties facilitates scaling up distillation experiments to large-scale

applications.

In this study, the distillation curves of biocrude, petroleum crude and their

blends were analysed using simulated distillation (ASTM D 2887). This method

provides the boiling point distribution of distilled mixtures, equivalent to the results

obtained by true boiling point (TBP) distillation (ASTM D 2892) [18]. The

compositions have also been analysed using gas chromatography mass

spectrometry (GC-MS) to determine volatile components of the substances.

Blending the biocrude with petroleum crude was also modelled in ASPEN Plus to

predict the resulting mixture’s distillation curve. Biocrude and petroleum crude

blends were also analysed to determine the compatibility of biocrudes with

petroleum crude distillation processes with regard to miscibility and distillation

properties. Coupling experimentally-determined distillation curves with ASPEN

Plus models has not been conducted in previous studies because investigators

tend to focus on either characterising the biocrudes or modelling biocrude plants,

but not both. The approach taken in this study aims to verify the utility of ASPEN

Plus as a biocrude process modelling tool.

4.2 Materials and Methods

4.2.1 Materials and equipment

Biocrude was obtained through liquefaction of a variety of feedstock using a

1.8 L Parr reactor. For all liquefaction experiments, an initial 10 bar N2 pressure

was used to minimise the effect of oxygen on liquefaction products. The reactor

was held at target temperature for 30 min, which was determined as the optimum

reaction time for the feedstock. Bagasse obtained from the Wilmar Invicta sugar

factory in Townsville, Queensland, Australia was liquefied at 300 °C, with a solvent

of 95% ethanol and 5% water. The biomass to solvent ratio was 1:19, to ensure

adequate contact of the solvent with biomass in the course of the reaction to

prevent untoward burning or pyrolysis without solvent. These conditions were

found to be favourable in earlier work [19, 20]. Due to the reactor producing only

20-40 g of biocrude per run, products from several runs were composited to

produce one sample. Aged bagasse biocrude was also analysed to compare with

freshly-prepared bagasse biocrude. The aged sample was obtained from previous


bagasse liquefactions, prepared using the same method described for the fresh

bagasse biocrude, and stored at 4 °C for 18 months.

Biocrudes produced from microalgae and mixed feedstock were also

included in the study to compare their distillation properties with the bagasse

biocrude. The microalgae (Scenedesmus sp.) biocrude was obtained from

previous liquefaction experiments at 25% biomass concentration and 350 °C

[21]. The mixed feedstock biocrude was a composite biocrude from liquefying

Arundo donax, forage sorghum, sugarcane bagasse, banana bunch stems and

pineapple tops in acetone, with a biomass-solvent ratio of 1:15 [22].

Mixed feedstock biocrude and aged bagasse biocrude were included in the

study to consider different supply scenarios. Combining biocrude could be an

option in regions with various types of lignocellulosic feedstock or to manage

seasonal and economic variations in feedstock availability. Long-term storage of

biocrude is important for biocrude production plants that may be located in the

outer reaches of the supply network. Furthermore, long-term storage can be

employed to mitigate sensitivities to seasonal supply of feedstock, changing

biofuel demand, and refinery capacities. Microalgae biocrude was considered in

the study for comparison as an emerging biomass feedstock. Biocrude and crude

oil descriptions are shown in Table 4.1.

Table 4.1. Description of biocrudes and petroleum crudes

ID Sample Description

BB Bagasse biocrude Biocrude sample from liquefaction of

sugarcane bagasse at 300 °C with ethanol.

BBA Aged bagasse

biocrude

Biocrude sample from liquefaction of

sugarcane bagasse at 300 °C with ethanol,

stored at 4 °C for 18 months [23].

MFB Mixed feedstock

biocrude

Composite biocrude sample from

liquefaction of Arundo donax, forage

sorghum, sugarcane bagasse, banana

bunch stems and pineapple tops at 300 °C

with acetone as solvent [22].


ID Sample Description

MB Microalgae biocrude Biocrude produced from liquefying

Scenedesmus sp. at 350 °C [21].

PCON Petroleum condensate Petroleum liquid condensate

PCRU Petroleum crude oil Petroleum crude oil

Petroleum crude oils were obtained from Caltex Australia Petroleum Pty Ltd.

The crude oil is a Chao Sim sample, while the petroleum condensate is a PHK-

Maui Mix sample. The ASTM D 2887 Calibration Mixture was purchased from

Sigma Aldrich. Biocrude and petroleum crude were analysed using a gas

chromatograph with a mass spectrometer detector (GC-MS) to determine

components, and flame ionisation detector (GC-FID) to generate distillation

curves. The GC-MS used was a ThermoScientific ISQ Trace 1310 equipped with a

single quadrupole mass selective detector, a TG5ms column (length 30 m, ID 0.25

mm, 0.25 μm film), and a TriPlus RSH Autosampler. The GC-FID used was an

Agilent HP 6890 equipped with a flame ionisation detector and an HP-5ms

column (length 30 m, ID 0.32 mm, 0.25 μm film).

4.2.2 Blending

Blends were prepared by mixing biocrude with crude oil for 10 min. The

components were added into a dry, weighed plastic vial, and blended in a vortex

stirrer. Some blends were heated prior to commencing blending. The vials were

capped to avoid loss of volatiles while stirring and heating. A petroleum blend was

also prepared for comparison. The blending formulations are shown in Table 4.2.

Table 4.2. Description of blends

Blend Blend Composition (% v/v) Blending

Tempera

ture (°C)

Biocrude Crude Oil Condensate

BIOBL-COOL 50% BBA 50% 0% 25

BIOBL-WARM/

BIOBL-5050

50% BBA 50% 0% 50

BIOBL-HOT 50% BBA 50% 0% 75

BIOBL-2575 25% BBA 75% 0% 50

PETBL 0% 50% 50% 25


4.2.3 Analysis

In the GC-MS analysis, static headspace extraction was employed to sample

volatile components in biocrudes and prevent potential fouling of

chromatographic columns with non-volatile components in the biocrude matrix.

Samples were heated to 100 °C and agitated in the incubation oven for 10 min

to ensure thermal equilibrium and a homogeneous mixture of vapours prior to

injection of 250 μL of the headspace vapours with a split ratio of 75:1. The carrier

gas was helium at 1.2 ml/min. The oven was programmed at an initial

temperature of 50 °C, held for 1 min before a 20 °C/min ramp to 325 °C, where

the temperature was held for 5 min. The MS transfer line temperature was set to

280 °C while the ion source temperature was set to 300 °C and the MS scanned

in the m/z range of 40-400 amu, with 1.4 min solvent cut and a scan time of 0.1

s. The spectra of peaks from the chromatogram were electronically compared with

spectra from the US National Institute of Standard and Technology (NIST) Mass

Spectral Search Program and Library System to identify components.

The distillation curves of the samples were obtained using procedures in

ASTM D 2887. Compared with ASTM D 2892, this method was selected for its

expediency and minute sample requirement. The use of ASTM D 86 was also

considered, however, this method required a 100 ml sample for each run, while

ASTM D 2887 required much less, even with replicates. The results of ASTM D 86

also represent only one theoretical plate distillation, and the results have to be

converted to TBP distillation values to be useful for mass balance calculations.

Furthermore, the use of ASTM D 2887 to generate the TBP distillation curve has

been demonstrated for a petroleum-like substance with significant oxygen and

aromatic content, which may be similar to biocrude [24].

The column and analysis sequence protocol for the distillation curve

determination were selected based on previous studies analysing similar samples

[23] and GC experiments to determine the protocol that results in maximum

repeatability. A calibration curve was prepared using the ASTM D 2887 Calibration

Mixture to relate retention time to boiling temperature. Prior to the analysis, the

samples were diluted with dichloromethane at a dilution factor of 100 to decrease

viscosity. Four microliters (4 µL) of the diluted mixture were then injected directly

into the column. PCON and PETBL were deemed to have a viscosity low enough to


be analysed neat, and thus required 1 µL to be injected. Samples were injected at

350 °C with a split ratio of 75:1. The carrier gas was helium at 1.2 ml/min. The

oven was programmed at an initial temperature of 50 °C, held for 1 min before a

15 °C/min ramp to 325 °C, where the temperature was held for 15 min. The FID

detector temperature was set at 370 °C with a sampling rate of 50 Hz. The

chromatographic data was then processed using the formulae in ASTM D 2887 to

generate the distillation curves of each sample.

Fourier transform infrared (FTIR) spectra for biocrude, petroleum crude and

blends were obtained in the spectral range of 4000-400 cm-1 using a Bruker-Alpha

spectrometer. The spectra generated were averages of 24 scans per sample

calculated by the Opus software. All samples were taken immediately after

shaking the sample vials to maximise homogeneity.

4.2.4 Distillation curve models

Biocrude-petroleum crude blends were simulated using a commercial

process modelling software, ASPEN Plus (version 8.4) to compare the physical

measurement of distillation curves to theoretical distillation curves. ASPEN Plus

was deemed as an appropriate tool for modelling liquefaction biocrudes due to its

powerful property estimation feature that allows modelling of complex and novel

substances. Furthermore, its ubiquity in similar liquefaction modelling studies

shows its suitability to handle the complexity these processes present [10, 11,

25]. With the measured distillation curves of the individual components as input,

ASPEN generated the distillation curves of the blends using its Assay Data Analysis

(ADA) feature. This allows ASPEN to calculate the properties of theoretical

distillation fractions or pseudocomponents from bulk measurements such as

distillation curves and density or API gravity measurements. It is important to verify

if the ASPEN-generated curves match the measured distillation curves. As the ADA

feature was designed with petroleum in mind, it was therefore necessary to use

petroleum samples as a basis to which biocrude data can be compared.

The crudes are modelled in ASPEN as an Assay component using the ASTM

D 2887 distillation curves and specific gravity. The values of specific gravity for

the components modelled are 0.78 for PCRU, 0.73 for PCON and 1.13 for BBA.

The RK-SOAVE property method, which is suitable for hydrocarbon processing, was

used for estimation of thermodynamic properties. Blending was modelled using a


MIXER block, with the petroleum crude oil in one stream and the biocrude as

another stream as inputs, and the blend coming out of the block as the output.

4.3 Results and Discussion

4.3.1 Volatile components of biocrudes and petroleum crudes

The composition of biocrudes varies with the liquefaction conditions in which

they are produced. Biomass composition, solvent and reaction temperature affect

product compositions the most [26]. Biocrude chemicals are products of

hydrolysis, depolymerisation, dehydration, dehydrogenation, deoxygenation,

decarboxylation, condensation, cyclisation, and re-polymerisation [27].

Oxygenated compounds such as esters, ketones and phenols are common

products in liquefaction of lignocellulosics, while nitrogenated compounds are

found in liquefied microalgae. On the other hand, petroleum crude oil is usually

composed of aliphatic and aromatic compounds, with traces of ions from salts

[28]. The volatile components of biocrudes and the blends are shown in Table

4.3a and b. The GC traces for BB, PCRU and BIOBL-COOL are included in

Supplemental Information (Appendix A).

Table 4.3a. Composition of biocrudes, listed in relative abundance within the group

Group BB BBA [23]

Aliphatics n.d. 1-hexane, 4,5-dimethyl-

Aromatics n.d ethylbenzene

Esters,

anhydrides

and carboxylic

acids

2-hydroxybutanoic acid ethyl ester;

2-hydroxypropanoic acid ethyl ester;

butanoic anhydride;

glycolic acid ethyl ester;

α-hydroxyisobutyric acid;

acetic acid;

acetic acid ethenyl ester;

4-oxo-pentanoic acid ethyl ester;

glycolic acid ethyl ester;

ethyl-2-(2-oxocyclopentyl)-

propionate;

2-furancarboxylic acid ethyl ester;

butanoic acid propyl ester

3-(1-acetyl-2,2-dimethyl cyclopentyl)-

2-propenoic acid methyl ester;

5-oxotetrahydrofuran-2-carboxylic

acid ethyl ester

Phenolics guaiacol;

phenol;

4-ethylphenol;

4-ethylguaiacol

2,6-dimethoxy-4-(2-propenyl)-

phenol;

2-(1,1-dimethylethyl)-1,4-

benzenediol;

4-ethylphenol


Group BB BBA [23]

Ketones and

aldehydes

2,3-dimethyl-2-cyclopenten-1-one;

3-hydroxy-3-methyl-2-butanone;

2-hydroxy-3-pentanone;

methylglyoxal;

2-cyclopenten-1-one;


3-ethylcyclopentanone;

2-methylcyclopentanone;

cyclopentanone;

3-methyl-2-cyclopenten-1-one

9-methyl-7-oxabicyclo(4.2.1) nona-

2,4-dien-8-one;

5,5-diethoxy-2-pentanone;

butyrolactone;

2-hydroxy-3-methyl-2-cyclopenten-1-

one;

1-hydroxy-2-butanone;

3-ethyl-2-hydroxy-2-cyclopenten-1-

one

Furans and

alcohols

2-ethoxyethanol;

2-methyl-3-pentanol;

2-furanmethanol;

2-ethyl-1,3-dioxolane;

3-methyl-1-butanol;

2-ethoxytetrahydrofuran;

2-acetyl-2-methyltetrahydrofuran

2-methyl-2-pentanol;

2-furanmethanol;

2,5-diethoxytetrahydrofuran;

2,3-dihydrobenzofuran;

D-mannose;

tetrahydro-2-furanmethanol

Nitrogenated

compounds

n.d. n.d.

n.d. not detected

Group MFB [22] MB [21]

Aliphatics 4-methyl-2-pentene;

2,7-dimethyl-3,5-octadiene;

1,2-dimethylpropylcyclopropane;

4-methyl-2-hexene

n.d.

Aromatics 1,2,5-trimethylbenzene 1,4-dimethylbenzene;

ethylbenzene;

1,3-dimethylbenzene

Esters,

anhydrides

and carboxylic

acids

4-hydroxy-2-methylenebutanoic acid;

2-methylpropanoic anhydride;

4-hydroxybutanoic acid

phthalic acid, di(2-propylpentyl)

ester;

1-acetate-1,2,3-propanetriol

Phenolics 4-ethylguaiacol;

4-ethylphenol;

p-cresol;

phenol


Group MFB [22] MB [21]

guaiacol;

syringol;

phenol;

3-ethoxyphenol;

m-cresol

Ketones and

aldehydes

4-methylpenten-2-one;

4-methyl-3-penten-2-one;

3,6-heptanedione;

2,5-hexanedione;

3,5,5-trimethyl-2-cyclohexen-1-one;

2,6-dimethyl-2,5-heptadien-4-one;

2-(1-hydroxy-1-methyl-2-oxopropyl)-2,5-

dimethyl-3(2H)-furanone;

2-acetonylcyclopentanone;

3-methyl-2-cyclopenten-1-one;

3,5-dimethyl-2-cyclohexen-1-one;

4-(1-methylethyl)-2-cyclohexen-1-one;


4-(2-furanyl)-3-buten-2-one;

3,3-dimethyl-hepta-4,5-dien-2-one


4-hydroxy-4-methyl-2-pentanone;

3-methyl-2-cyclopenten-1-one

Furans and

alcohols

2-furanmethanol;

2,2-dimethyl-(S)-1,3-dioxolane-4-

methanol;

2-methyl-3-penten-1-ol;

1-(1,2-propadienyl)cyclohexanol;

3-hydroxybenzene ethanol

n.d.

Nitrogenated

compounds

n.d. 1-butyl-2-pyrrolidinone;

1-pentylpiperidine;

1-methyl-2-pyrrolidinone;

1-butyl-2-pyrrolidinone;

1-ethyl-2-pyrrolidinone;

trimethylpyrazine;

1-propyl-2-pyrrolidinone;

2-ethyl-6-methylpyrazine


Table 4.3b. Composition of petroleum crude and biocrude-petroleum crude blend, listed in

relative abundance within the group

PCRU BIOBL-COOL

Aliphatics methylcyclohexane;

1,3,5-cycloheptatriene;

decane;

octane;

nonane;

heptane;

2-methylpentane;

hexane;

methylcyclopentane;

2-methylheptane;

1,3-dimethylcyclohexane;

undecane;

3-methylhexane;

1,2-dimethylcyclopentane;

ethylcyclohexane;

3-methylpentane;

dodecane;

2-methyldecane;

1-ethyl-4-methylcyclohexane;

pentane;

1,2,4-trimethylcyclohexane;

2,6-dimethyl-heptane;

decane, 4-methyl-;

methylcycloheptane;

tetradecane;


1-methyl-2-propylcyclohexane;

1,2,4-trimethylcyclopentane;

2,2-dimethylbutane;

ethylcyclopentane;

2,6-dimethylundecane;

2-cyclohexyl-decane;

2,6,10-trimethyltetradecane

decane;

nonane;

undecane;

octane;

2-propenylidenecyclobutene;

methylcyclohexane;


dodecane;

heptane;

3-ethyl-2,5-dimethylhexane;

ethylcyclohexane;

2-methyloctane;

2-methylheptane;

1,1,2-trimethylcyclohexane;

tridecane;

2-methyldecane;

2,6-dimethylnonane;

3-methylhexane;

2,6-dimethylheptane;

hexane;

cis-1,3-dimethylcyclopentane;

cis-1-ethyl-3-methylcyclohexane;

3-methyldecane;

2-methylpentane;

methylcyclopentane;

tetradecane;

butylcyclohexane;

4-ethyloctane;

2,3,7-trimethyloctane;

methylcycloheptane;


cyclopentylcyclohexane

Aromatics o-xylene;

m-ethyl-toluene;

mesitylene

m-xylene;

m-ethyl-toluene;

mesitylene


PCRU BIOBL-COOL

Esters,

anhydrides and

carboxylic acids

dichloroacetic acid dodecyl ester;

dichloroacetic acid tridecyl ester

2-hydroxypropanoic acid ethyl

ester;

2-hydroxybutanoic acid ethyl

ester;

2-methylpropanoic anhydride

Furans and

alcohols

2-hexen-1-ol;

2-tridecen-1-ol;

limonen-6-ol, pivalate

2-methyl-1-octanol;

2-ethoxyethanol

Halogenated

compounds

2-chloro-2-methylpentane 2-chloro-2-methylpentane

n.d. not detected

The composition of the biocrude-petroleum crude blend was more congruent

with petroleum crude than biocrude. This could be attributed to imperfect mixing

of the two components, causing a larger volatile portion from petroleum crude

sampled, or a proportionally small amount of biocrude components volatilised

during pre-GCMS sampling incubation. Immiscibility of the components is

reasonable considering the polarities of the compounds that make up petroleum

crude and biocrude. There is an opportunity to characterise the blend composition

with more advanced methods; however, there should also be consideration of the

amount of effort against the practical use of the information obtained. Due to the

complexity of a petroleum mixture, crude oils are usually characterised using

distillation curves [29]. It follows that biocrudes be characterised the same way,

with a view to co-processing in refineries.

4.3.2 Distillation curves of crude oils

The chromatograms from the GC-FID were processed following ASTM D 2887

protocols to generate distillation curves for each sample. Figure 4.1 shows the

distillation curves of the biocrudes and petroleum crudes. The crude oil sample

(PCRU) had the widest boiling range among all the samples. PCRU also boiled at

a higher temperature than all other samples. As expected, petroleum condensate

had the lowest distillation curve due to its nature as a light hydrocarbon mixture.

Microalgae biocrude had the lowest boiling range, which might present challenges

in separation. Among the bagasse biocrudes, the aged biocrude (BBA) generally

boiled higher than the fresher one (BB). Although both had the same boiling range,


and BB boiled higher in the lower distilled percent range, the boiling temperature

of BBA increased more steeply compared to BB. This could be attributed to the

presence of heavier components through polymerisation [23, 30].

Figure 4.1. Distillation curves of biocrudes and petroleum crudes, with typical petroleum

fractional distillation temperature ranges shown based on Behrenbruch [29].

The distillation curves of the biocrudes are within the range of the distillation

curve of the crude oil. This signifies that it is possible to process biocrudes in the

same distillation column designed for the crude oil analysed in this study.

However, components of biocrudes with a narrow boiling range might distil to only

the middle stages in the column, therefore concentrating the biocrude

components in certain cuts. The distillation curves on Figure 4.1 show that while

only around 10% of crude oil distilled, around 32% of BBA, 50% of BB and MB and

up to 70% of MFB distilled in the gasoline and kerosene range. In contrast, around

50% of crude oil distilled in the heavy gas oil range. While not necessarily

detrimental to the functioning of the distillation column, additional load on specific

cut sections (i.e. pumps, condensers) where components of biocrude are distilled

to, need to be considered. Conversely, it can be inferred that the biocrudes in this

0

50

100

150

200

250

300

350

400

450

500

550

0% 20% 40% 60% 80% 100%

Te

mp

era

ture

, °C

Percent Distilled, %

PCON PCRU BB MFB MB BBA

Gasoline

Kerosene

Diesel

Heavy Gas Oil

Lubricating Oil

Residuum


study might be better suited to a refinery intended for gasoline and kerosene

production.

4.3.3 Biocrude and petroleum blends

Following blending, the miscibility of biocrude with petroleum crude was not

easily ascertained due to the colour and opacity of the two liquids. In order to

determine homogeneity of the mixture, FTIR analysis of BCHC and its components

was conducted. A well-mixed blend will have spectral features of both biocrude

and petroleum crude, while a blend that did not completely mix will have spectra

that are similar to either of the components’ spectra. Immiscibility of the

components will cause the components to separate and thus, a sample may be

taken in either of the single component phases instead of a mixed component

phase.

The FTIR spectra show the functional groups present in the blends and the

composing oils. In Figure 4.2, it is shown that BBA (green) and PCRU (red) have

distinctly different spectra. BBA has a wide peak at 3400 cm-1 and strong peaks

between 1000 and 1300 cm-1 due to the presence of O-H and C-O groups,

respectively, suggesting alcohols. The C-O peaks, together with a carbonyl peak

between 1600 and 1900 cm-1 suggest esters. The carbonyl peak at 1700 cm-1

also points to the presence of ketones. Peaks between 1450 and 1620 cm -1

suggest aromatics, which when coupled with the O-H peaks suggest phenols. On

the other hand, the spectra of PCRU were much different. The main features are

the strong peaks between 2800 and 3000 cm-1, which suggest C-H stretching in

alkyl groups. Furthermore, other peaks at 1350 and 1450 cm-1 suggest CH2 and

CH3 bending vibrations. The FTIR spectra corroborate the results of the GC-MS.


Figure 4.2. FTIR spectra of BBA (green), PCRU (red) and BIOBL-COOL (blue)

It is reasonable to expect that the spectra of a well-mixed blend will exhibit

the peaks of both components. However, it was apparent that the BIOBL-COOL

blend spectra (blue) followed PCRU closely. Aside from very slight differences at

3400 cm-1 that suggest partial miscibility of O-H containing components, the blend

sample analysed could have been taken from a PCRU-rich section. The existence

of these sections of varying compositions of BBA and PCRU may further support

that hypothesis that biocrude and petroleum crude are not readily miscible.

To further elucidate this, more samples were taken from BIOBL-COOL to

observe if their spectra would be consistent. A sufficiently homogeneous mixture

should generate similar spectra regardless of sampling. Figure 4.3 illustrates two

samples taken from different sections of the blend, immediately after blending.

Since it was expected that BBA and PCRU will sort by density, the samples were

taken from two depths of the container. The green line represents the sample

taken from near the liquid level (top), while the blue line represents the sample

from the bottom of the container (bottom). The spectra were similar in certain

wavenumbers, although the strength of the peaks particularly at 3400 cm -1, 2800-

3000 cm-1 and 1000-1700 cm-1 varied.

3347

2919

2851

1462

1377

2920

2851

1459

1379

3359

1719

16071515

1374

1211

1041

30

40

50

60

70

80

90

100

300800130018002300280033003800

Ab

so

rba

nc

e, %

Wavenumber, cm-1

BIOBL-COOL

PCRU

BBA


Figure 4.3. FTIR spectra of BIOBL-COOL sampled from two sections: top (green) and bottom

(blue)

In contrast, the FTIR spectra of BIOBL-WARM were more positive. In Figure

4.4, the FTIR spectra of the blend show spectral features of both PCRU and BBA.

The two lines represent sampling at various cooling times. The red line

represented sampling after five minutes of cooling, while the blue line represented

sampling after ten minutes of cooling. Although similar in shape, the BIOBL-COOL

sample had stronger PCRU peaks (2800-3000 cm-1) and the warm sample had

stronger BBA peaks (3400 cm-1 and 1000-1700 cm-1).

TOP

BOTTOM

30

40

50

60

70

80

90

100

300800130018002300280033003800

Ab

so

rban

ce

, %

Wavenumber, cm-1

TOP

BOTTOM

3400 cm-1 3000 1000-1700 cm-1


Figure 4.4. FTIR spectra of BIOBL-WARM sampled after two different cooling times

The similarities in spectra and the presence of spectral features from both

components demonstrate that the immiscibility of biocrude with petroleum crude

can be overcome by blending at a higher temperature. Mixing at 50 °C allowed

the components to blend more vigorously due to the lower viscosity. Blending

biocrude and petroleum crude can be done after the preheat step prior to feeding

into a distillation column, which means there is no need for an additional

preheating step. Another factor that ensured homogeneity in the samples taken

was that they were taken immediately after mixing. It can be therefore inferred

that adequate mixing can maintain the homogeneity of the biocrude-petroleum

crude blend. A more detailed analysis regarding mixing speeds could be useful for

process design.

4.3.4 Distillation curves of blends

The effect of blending temperature was further investigated by analysing the

distillation curves of the blends. Figure 4.5 illustrates the effect of blending

temperature on the boiling point distribution of the sampled blends. The blending

model in ASPEN predicts the ideal result of blending petroleum crude and

biocrude as the whole blend is represented in the ASPEN distillation curve. It was

30

40

50

60

70

80

90

100

300800130018002300280033003800

Ab

so

rba

nc

e, %

Wavenumber, cm-1

5 MIN COOLING

10 MIN COOLING

CRUDE

BIOCRUDE


therefore suggested that a homogeneous sample should generate the same

distillation curve as the model. Deviations from the modelled distillation curves

can result from immiscibility of the components, particularly in blends composed

at ambient temperature.

In this analysis, it was observed that blending temperature affected the

boiling point distribution of the samples. BIOBL-COOL, which was blended at 25

°C, was furthest away from the ASPEN-generated curve. This demonstrates a

variance to the ideal blending modelled in ASPEN. The blends BIOBL-WARM (50

°C) and BIOBL-HOT (75 °C) had closer distillation curves to the model. Between

the three blends, only the temperature was varied, and thus it can be surmised

that blending at higher temperatures results in a more homogeneous blend. This

result can be influenced by higher mass transfer rates caused by the decrease in

viscosities of the crudes at higher temperatures. Between BIOBL-WARM and

BIOBL-HOT, the distillation curves did not present large differences, possibly

attributable to a diminishing effect of temperature. Other effects such as chemical

or polarity changes, which were not investigated in this study, may have also

contributed to the result.


Figure 4.5. Distillation curves of biocrude-petroleum crude blends composed at different

temperatures

Ensuring homogeneity by comparing blends composed at 50 °C, the

distillation curves of petroleum crude and biocrude blends with different

formulations were generated to observe the effects of blending and to compare

the suitability of the blends to be fed into a distillation column. Figure 4.6 shows

the modelled and measured distillation curves of blends and their components.

0

50

100

150

200

250

300

350

400

450

500

550

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tem

pera

ture

, °C


PCRU BBA

BIOBL-WARM BIOBL-HOT

BIOBL-COOL ASPEN Blend


(a)

(b)

Figure 4.6. Distillation curves of (a) the petroleum crude blend and (b) biocrude-petroleum crude

blends with varying blend ratio

0

50

100

150

200

250

300

350

400

450

500

550

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tem

pera

ture

, °C


PCRU PCON PETBL ASPEN PETBL

0

50

100

150

200

250

300

350

400

450

500

550

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tem

pera

ture

, °C


PCRU BBA BIOBL-5050BIOBL-2575 ASPEN BIOBL2575 ASPEN 5050


A condensate-crude oil blend (PETBL) was analysed for benchmarking. The

distillation curve of the blend shown in Figure 4.6a followed the shape of the

distillation curve of the petroleum condensate (PCON) both as modelled in ASPEN

Plus and as measured. The distillation temperature range was still within the range

of petroleum crude; however the difference in shape manifests difference in

distillation rate across the range. The petroleum crude curve has a quadratic

shape, with a lower distillation rate at the lower temperatures, and increasing after

250 °C before it distils at a constant rate. Both the blend and the petroleum

condensate distillation curves also have a quadratic shape, however, the

distillation rates were higher at lower temperatures and decreased after 300 °C.

The change in the distillation curve of petroleum crude resulting from blending

should be taken into account in designing the temperature and pressure profile of

the column. Otherwise, a different blending ratio could be considered to reduce

the effect of blending to distillation rates, while retaining the viscosity-reduction

benefits of blending.

In contrast with that of the petroleum blend, the distillation curves of the

petroleum crude and biocrude blends (BIOBL-5050 and BIOBL-2575) shown in

Figure 4.6b followed the shape of the distillation curve of petroleum crude oil

(PCRU). The shape of the distillation curve of biocrude was similar to that of

petroleum crude up to 230 °C, where it had an inflection point and the distillation

required a higher rate of temperature increase to proceed. This was not

unexpected considering the polar character of biocrude, since polar components

have higher boiling points than non-polar components of similar molecular weight.

The higher boiling components of both biocrude and petroleum crude contributed

to keeping the distillation curve high despite having low boiling components that

distilled below 100 °C. As the curves showed, the blends distilled at lower

temperatures than pure petroleum crude, affected by the addition of light

components in biocrude.

The two biocrude-petroleum crude blends with the varying ratios presented

expected distillation curve results. As BIOBL-5050 had more biocrude than BIOBL-

2575, its boiling point distribution was closer to that of biocrude. The distillation

curve of BIOBL-2575 sits in between BIOBL-5050 and PCRU. The shapes of both

curves resembled the PCRU distillation curve, although both blend curves


resembled a flat section of BBA at around 120 °C, which was less pronounced

with decreasing biocrude composition. The inflection point on the distillation curve

of BBA at 250 °C did not manifest on any of the blend curves, as they both

continued to follow the general curvature of PCRU. Comparing these results with

the ASPEN Plus-modelled distillation curves, the BIOBL-2575 ASPEN curve also

followed the PCRU curve, while the BIOBL-5050 ASPEN curve predicts an inflection

point which is similar but less pronounced than the one in BBA. The measured

BIOBL-5050 curve was close to the modelled curve, however, it did not have the

aforementioned inflection point. Between all measured and modelled curves, the

difference ranges from 2 to 52 °C, which was significantly greater than the

repeatability of the ASTM D 2887 method. This can be attributed to an

overreaching estimation by ASPEN Plus brought about by the truncated distillation

curve for BBA.

4.3.5 Considerations for processing in conventional refineries

The literature on co-processing biocrudes with petroleum crudes have been

focused on processing biocrude using technologies similar to those used in oil

refineries. However, blending of biocrudes with petroleum crudes prior to

processing has not been studied adequately. Lavanya et al. [17] blended 10%

algal biocrude with crude oil and demonstrated distillation of the blend. The

miscibility of the algal biocrude with crude oil was not discussed, but it was

mentioned that since the biocrudes were “light”, a light crude oil was appropriate

for blending. Hoffman et al. [16] distilled wood biocrude under vacuum and

proposes light hydroprocessing to remove oxygen prior to co-processing in

refineries to mitigate potential corrosion issues. The immiscibility of biocrude to

petroleum crude was not addressed in either of these studies.

The immiscibility of biocrude with crude oil could result in inconsistent

distillation feed composition at the feed stage, which affects the concentration-

sensitive operation of fractionating columns. However, it is possible to ensure that

the blend is well-mixed as it enters the column. The analyses above demonstrate

mixing at an elevated temperature improves homogeneity of the blend, as the

viscosity of both biocrude and petroleum crude was reduced, allowing for better

mixing. As the feed is usually preheated prior to the fractionating column, ensuring

adequate turbulence for mixing could sustain the consistency of the blend


composition. Another potential solution is to match the properties of biocrude to

the properties of the crude oil from a certain stage and introduce the biocrude

feed to the matching stage. Since the properties of biocrude depend heavily on

feedstock, the distillation curves of biocrudes may vary widely. This is not a novel

concept as different petroleum refineries were designed to process a particular

variety of petroleum crude. In a similar way, biocrudes can be matched with

petroleum refineries processing petroleum crude that has comparable properties

with biocrudes.

Furthermore, other processing configurations could be considered. There

can be two different distillation columns for biocrude and petroleum crude and

fractions with similar properties can be blended as feed for hydroprocessing. The

high density and viscosity of biocrude has also made it comparable to the heavier

fractions and atmospheric distillation residue, which can be processed in vacuum

distillation units. Vacuum distillation of biocrude has been demonstrated in some

studies [15, 16]. Hydrotreatment of the biocrude prior to blending with petroleum

crude oil has also been proposed. While this requires the addition of a processing

unit, it can be viewed as a necessary investment to facilitate co-processing

seamlessly by eliminating issues in incompatible properties of biocrude and

petroleum crude oil. The integration of biocrude to petroleum crude as well as the

comparison of various processing configurations would be interesting for further

investigations.

4.4 Conclusion

Biocrudes were successfully characterised by distillation curves generated

from simulated distillation. The compositions of biocrudes were broadly different;

consequently, their distillation properties varied widely. Biocrude and petroleum

crude blends were produced and the miscibility of the biocrude to the petroleum

crude oil was observed using FTIR. Distillation curves of the blends were measured

and matched to the blends modelled in ASPEN, ensuring blends are well -mixed

through heating to reduce viscosity and turbulent mixing patterns to increase the

consistency of the blend. This work confirms that ASPEN Plus can be used for the

simulation of the integration of biocrude into a petroleum refinery in terms of

distillation. The difference in distillation curves of biocrude-petroleum crude

blends with petroleum crude may affect operation of conventional distillation


units, and these should be considered in co-processing. Hydroprocessing to

decrease the oxygen content to match the polarity of the petroleum crude oil could

be beneficial to producing a consistent blend as a feed to the fractionating column.

4.5 Acknowledgments

This research was financially supported through a PhD scholarship from the

Australian Government. The authors would like to thank Caltex Australia Lytton

Refinery (Caltex Australia Petroleum Pty Ltd) for their generous donation of the

petroleum crude samples used in this study. We also express gratitude to the

Central Analytical Research Facility (CARF) operated by the Institute for Future

Environments (QUT) and the School of Chemistry, Physics and Mechanical

Engineering (CPME) analytical laboratories.


4.6 References

1. Beer, T., et al., Fuel-cycle greenhouse gas emissions from alternative

fuels in Australian heavy vehicles. Atmospheric Environment, 2002.

36(4): p. 753-763.

2. Deng, Y.Y., et al., Country-level assessment of long-term global bioenergy

potential. Biomass and Bioenergy, 2015. 74: p. 253-267.

3. Rahman, M.M., et al., Particle emissions from biodiesels with different

physical properties and chemical composition. Fuel, 2014. 134: p. 201-

208.

4. Hedayat, F., et al., Influence of oxygen content of the certain types of

biodiesels on particulate oxidative potential. Science of The Total

Environment, 2016. 545–546: p. 381-388.



Energy Reviews, 2015. 49: p. 1271-1285.

6. Xu, X., et al., Two-step catalytic hydrodeoxygenation of fast pyrolysis oil to

hydrocarbon liquid fuels. Chemosphere, 2013. 93(4): p. 652-660.




8. Ragauskas, A.J., et al., The Path Forward for Biofuels and Biomaterials.

Science, 2006. 311(5760): p. 484-489.

9. O'Hara, I., et al., Prospects for the development of sugarcane

biorefineries, in Proceedings of the 28th International Society of Sugar

Cane Technologists Conference, D.M. Hogarth, Editor. 2013: Sao Paulo,

Brazil.



Applied Energy, 2014. 129: p. 384-394.




12. Klein-Marcuschamer, D., et al., Technoeconomic analysis of renewable

aviation fuel from microalgae, Pongamia pinnata, and sugarcane.

Biofuels, Bioproducts and Biorefining, 2013. 7(4): p. 416-428.

13. Sarma, A.K. and D. Konwer, Feasibility Studies for Conventional Refinery

Distillation with a (1:1) w/w of a Biocrude Blend with Petroleum Crude Oil.

Energy & Fuels, 2005. 19(4): p. 1755-1758.

14. Agblevor, F.A., et al., Co-processing of standard gas oil and biocrude oil to

hydrocarbon fuels. Biomass and Bioenergy, 2012. 45: p. 130-137.

15. Eboibi, B.E.-O., et al., Hydrothermal liquefaction of microalgae for

biocrude production: Improving the biocrude properties with vacuum

distillation. Bioresource Technology, 2014. 174: p. 212-221.




17. Lavanya, M., et al., Hydrothermal liquefaction of freshwater and marine

algal biomass: A novel approach to produce distillate fuel fractions


through blending and co-processing of biocrude with petrocrude.

Bioresource Technology, 2016. 203: p. 228-235.

18. ASTM International Standard Test Method for Boiling Range Distribution

of Petroleum Fractions by Gas Chromatography, 2016. ASTM

International.



9(6): p. 630-638.



2077.

21. Hossain, F.M., et al., The chemical-physical properties of bio-crude

derived from the hydrothermal liquefaction of algae, in Algal Biomass,

Biofuels & Bioproducts. 2015, Elsevier: San Diego, CA, USA.



Advances, 2017. 7(44): p. 27707-27717.

23. Kosinkova, J., et al., Physical and Chemical Stability of Bagasse Biocrude

from Liquefaction Stored in Real Conditions. Energy & Fuels, 2016.

30(12): p. 10499-10504.

24. Kiser, M.D. and D.P. Malone, Comparison of simulated distillation to true

boiling point distillation of H-Coal distillates. Journal Name: Prepr. Pap. -

Am. Chem. Soc., Div. Fuel Chem.; (United States); Journal Volume: 27:3-4;

Conference: National meeting of the American Chemical Society, Kansas

City, MO, USA, 1 Sep 1982. 1982: ; None. Medium: X; Size: Pages: 114-

119.



Research, 2013. 2(4): p. 455-464.




27. Demirbaş, A., Mechanisms of liquefaction and pyrolysis reactions of

biomass. Energy Conversion and Management, 2000. 41(6): p. 633-646.

28. Elvers, B., Handbook of fuels: energy sources for transportation. 2008.

29. Behrenbruch, P. and T. Dedigama, Classification and characterisation of

crude oils based on distillation properties. Journal of Petroleum Science

and Engineering, 2007. 57(1–2): p. 166-180.

30. Adjaye, J.D., R.K. Sharma, and N.N. Bakhshi, Characterization and

stability analysis of wood-derived bio-oil. Fuel Processing Technology,

1992. 31(3): p. 241-256.


Chapter 5: Techno-economic analysis of the

thermal liquefaction of

sugarcane bagasse in ethanol to

produce liquid fuels





Published in Applied Energy, 224 (2018), Pages 184-193









publication;









Title: Techno-economic analysis of the thermal liquefaction of sugarcane bagasse

in ethanol to produce liquid fuels (2018, published)



Richard Brown Provided editorial contributions

Thomas Rainey Assisted with preparing the manuscript; Provided

editorial contributions.





Name Signature Date


Techno-economic analysis of the thermal liquefaction

of sugarcane bagasse in ethanol to produce liquid

fuels





Abstract: A plant converting sugarcane bagasse to liquid fuels through thermal

liquefaction in an Australian setting was modelled in ASPEN Plus. Ethanol was

investigated as a liquefaction solvent due to its effect of higher yields and higher

biocrude heating value compared to water (i.e. hydrothermal liquefaction). The

plant produced 0.67 kg biocrude per kg dry feed, which was further processed to

0.46 kg liquid fuels per kg of dry feed for a total of 25.8 million L/y of biofuel

product. Ethanol losses incurred the highest share in operating costs, although

there are opportunities for cost reduction around lower solvent to biomass ratio.

Over the plant life and with a corporate tax rate of 30%, it was determined that the

minimum selling price for the fuel products is US$ 0.99/L, which was comparable

to other liquefaction studies using water as solvent. It was demonstrated that

continuous operation mode was economically more advantageous than semi-

batch production. Product price, hydrodeoxygenation conversion efficiency and

plant capacity were determined to be the factors to which NPV is most sensitive,

while biocrude yield and hydrodeoxygenation conversion efficiency were the key

factors in decreasing the minimum selling price of the product to a level that can

be competitive.

Keywords: liquefaction, biocrude, techno-economic analysis, bagasse, biofuels


5.1 Introduction

The development of biofuel production technologies has been increasingly

important as a means of reducing greenhouse gases emissions due to the lower

net CO2 emitted over the fuel’s life cycle [1]. Aside from carbon sequestration,

biofuels also bring about socio-economic benefits, particularly in developing

countries and areas with limited fossil fuel supply [2].

Compared with plant oil-based biodiesel or bioethanol produced from food

sugars, technologies that convert non-food feedstock are preferred [3]. This is

where thermochemical processes are useful since these do not discriminate

against the nature of feedstock [4]. More versatile among the range of

thermochemical processes is thermal liquefaction, which has been demonstrated

to be effective in converting biomass of considerable water content to liquid

products. The liquefaction process is carried out at temperatures of 200-370 °C

and pressures of 4-40 MPa [5] producing gaseous, liquid and solid products from

the decomposition of biochemical polymeric substances in biomass. The product

of particular interest is the liquid organic product (i.e. biocrude), which consists of

saturated and unsaturated hydrocarbons, and oxygenated and nitrogenated

compounds of varying amounts, affected heavily by the composition of the

feedstock [6]. The variety in composition and the presence of heteroatoms make

biocrude less than satisfactory for direct use in internal combustion engines.

Biocrude has higher viscosity and lower heating value, compared to petroleum-

based fuels [7]. Therefore, aside from separation of the different liquefaction

product streams, further processing of biocrude is necessary to be able to use it

as a potential transportation fuel.

In the liquefaction of biomass, the use of a solvent is essential to take advantage

of its decreased heat and mass transfer resistances and the improved properties

at supercritical regimes [5]. Water is the most common solvent in liquefaction

studies, owing to its abundance, low cost and low environmental impact [8];

however its high critical point and boiling temperature (Tc = 374 °C, Pc = 22 MPa,

Tb = 100 °C) requires large amounts of energy in liquefaction and subsequent

separations. On the other hand, ethanol has a lower critical point and boiling

temperature (Tc = 241 °C, Pc = 6.3 MPa, Tb = 78 °C) that facilitates liquefaction

and separation of solvent using less energy. Ethanol can also be sourced from


renewable bioethanol processes, such as sugar industry-aligned molasses or

cellulosic sugar fermentation [9], supporting CO2 emissions reduction [10].

Moreover, in hydrothermal liquefactions (i.e. using water as solvent), the

liquefaction yield is partitioned between water-soluble and water-insoluble

fractions. This decreases the amount of water-insoluble, lower-oxygen-content

biocrude that can be recovered [11]. From a comparison of water, acetone and

ethanol used as solvents in liquefaction it was demonstrated that using ethanol

resulted in the highest biocrude yield [12]. Ethanol can also convert organic acids

formed intermediately to esters, which can reduce viscosity and increase stability

of product oils [13]. Other studies have also tested the use of ethanol and water

as co-solvents that have shown a synergistic effect in obtaining a higher biocrude

yield and heating value [11, 14-16].

Among several methods to upgrade biocrudes, petroleum refining analogues

are ubiquitous. Since the expected upgrading product is fuel, it is reasonable to

process biocrudes the same way as petroleum crudes. In petroleum refining

hydroprocessing units, hydrodeoxygenation (HDO), hydrodesulphurisation (HDS),

hydrodenitrogenation (HDN), and hydrogenation occur simultaneously [17], so

while sulphur and nitrogen are the main heteroatoms that need removal in

petroleum crudes, and oxygen needs to be removed in biocrudes, these units may

be appropriate to handle both feeds with modification. Distillation of biocrudes

has also been proposed either as an alternative to hydroprocessing [18] or for

potential co-processing of biocrudes with petroleum crudes in conventional

distillation equipment [19].

In order to facilitate commercialisation of biomass liquefaction technologies,

analysis of technical and economic feasibility of processing biomass to produce

biofuels is necessary. Biomass hydrothermal liquefaction has now been

demonstrated at the pilot scale, as shown in Table 5.1. Commercial scale

modelling will be essential to determine gaps in data and challenges in technical

feasibility, feedstock supply, and enable life-cycle analysis, among others.

Furthermore, converting product yields, energy and material balances and

equipment requirements to cash flows enables a pragmatic analysis of the

liquefaction process’ position in the energy industry.

Table 5.1. Pilot-scale biomass liquefaction plants in operation.


Feedstock

Type

Feedstock Operating Entity and

Location

Capacity Ref

Lignocellulosic Wood chips,

etc.

Chevron and Iowa

State University, Iowa,

USA

0.5-1 kg/h

biomass

[20]

Organic matter Licella P/L, New

South Wales,

Australia

893 kg/h

biomass

[21]

Biomass slurry Aarhus University,

Foulum, Denmark

20-60 L/h

slurry

[22]

Sugarcane

bagasse,

prickly acacia

Northern Oil,

Queensland, Australia

40 L/h drop-

in fuels

[23]

Algae Microalgae University of Sydney,

New South Wales,

Australia

90 L/h algae

slurry (1-10%

wt)

[24]

Algae Sapphire Energy, New

Mexico, USA

662 L/h

biocrude

[25]

Thermal liquefaction is projected to be a significant pathway to generate

drop-in fuels. Techno-economic studies of the HTL process have been conducted

for virgin and residual wood, and algae liquefied using water as solvent in the USA

[26-28] and Finland [29]. Zhu et al. [28] concluded that a woody biomass HTL,

upgrading and in-house hydrogen plant modelled in 2007 can produce a gasoline-

equivalent fuel that can be sold for at least US$ 1.17/L for the plant to be

profitable (i.e. minimum selling price). Using lipid-extracted microalgae, a similar

plant but with hydrocracking can be sold for US$ 0.70/L, which is still above

gasoline price [27]. A similar plant using whole algae and additionally,

hydrothermal gasification to process the aqueous phase of the HTL product

resulted in a minimum selling price of US$ 1.19/L [26]. These values were 1.5-

2.5 times more expensive than the prevailing wholesale price for gasoline at the

time. Only the lipid-extracted microalgae was seen as competitive, although its

viability is dependent on a lipid extraction biofuel plant and the profitability of the

plant is considered to be only incremental. A more recent study by Magdeldin et

al. [29] in Finland using cheaper biomass to produce a gasoline-like fuel through

HTL and upgrading, the minimum selling price was US$ 1.95/L. With a hydrogen

plant, the price improves to US$ 1.81/L, but with an additional char combined

heat and power (CHP) plant the price soars to US$ 2.28/L. An additional water

gasification plant to augment hydrogen production lowers the price to US$ 1.20/L

and the same plant without the char CHP, the price drops to US$ 0.83/L.


These studies in the USA and Finland support conversion of biomass that

are specific to their local feedstock supply. Conditions such as natural

environment, markets, and government policies that affect biofuel projects also

differ from country to country. The development of local economic models can

demonstrate the viability of a similar plant for a different feedstock or blend of

feedstock, if necessary.

In the Australian setting, Queensland is a prime location for the development

of commercial biomass liquefaction plants. This is predicated on the state’s

Biofutures Roadmap and Action Plan, which is based on its strong agricultural

sector and mature transport market [30]. This setting was chosen because it also

represents a potential location with favourable policy conditions complementing the

availability of different kinds of lignocellulosic feedstock in the region [31]. Among the

variety of feedstock suitable for biocrude production, sugarcane bagasse is a

sustainable choice due to its abundance and availability as a waste by-product of

sugar manufacturing. Up to 35 million t of sugarcane is produced in Australia

annually, most of it coming from Queensland, and although the crops are spread

over 380,000 ha, the cane is aggregated at sugar mills located along major

transport hubs. Following sugar manufacture, 11 million t of sugarcane bagasse

are produced [32]. Due to bagasse being produced centrally in sugar mills,

collection and transportation costs are minimised [31]. The proposed bagasse

liquefaction plant is hinged on the successful demonstration of biocrude

production from bagasse using ethanol as solvent in small and large laboratory

scale studies [11, 15].

In this study, a novel thermal liquefaction plant using bagasse as feedstock

and ethanol as solvent was modelled. The feasibility of using a solvent other than

water to produce biocrude in liquefaction, and subsequently, fuel products that

are similar to gasoline and diesel was explored in this techno-economic study. The

key difference from previous models using water as solvent was in obtaining

liquefaction products in one biocrude phase, rather than two phases that split the

total organic yield. A biofuel plant set in Queensland, Australia was used to

represent an agro-industrial area where lignocellulosic biomass is abundant for

feedstock [31]. The effect to capital expenditure and operational cost of using

ethanol in liquefaction and its recovery for recycle is also of interest since this has

not been explored in past studies. ASPEN Plus has been chosen to take advantage


of its built-in property estimation tools and ubiquitous utility in modelling solid

processing and petroleum processes [33]. The suitability of ASPEN for modelling

liquefaction processes has been demonstrated in a number of studies [28, 29,

34]. The resulting mass and energy balances were then integrated into an

economic model and economic indicators were calculated to provide insight to the

economic feasibility of the plant. Critical design and operational conditions were

identified and their effects to technical and economic feasibility were analysed

and sensitivity to operational and economic variables are also presented.

5.2 Methodology

The plant was sized at 84000 t/y, which is only 0.76% of the total bagasse

production in Australia; however considerations around seasonal bagasse

availability, competition with its other uses [35] and the proximity of mills in

Queensland that can supply bagasse [36] justify this basis. The plant battery limits

were defined to include feed slurry preparation, thermal liquefaction reactor,

ethanol recycling, liquefaction product separation, fractional distillation,

hydrodeoxygenation, catalyst regeneration and final product separation. Values

and assumptions throughout the modelling were derived from literature. Physical

and chemical properties, including values for thermodynamic and fluid behaviour

were obtained from databases or estimated in ASPEN Plus or CHEMBIO3D Ultra,

which is a chemical modelling and computational software. Sources of other data

required to run the model are specified in this section.

5.2.1 Process Description

Figure 5.1 shows the block diagram of the process modelled. Bagasse at

48% moisture at 10 t/h enters the liquefaction section, which includes slurry

preparation, the liquefaction reactor, heat recovery from hot liquefaction product

streams, phase separation and filtration. The particle size of the bagasse ranges

from greater than 12.5 mm to less than 4 mm. Rainey reports a bagasse size

profile of 25% particles above 12.5 mm, 35% between 4.0-12.5 mm and 40%

below 4.0 mm [37] for Queensland bagasse. It is expected that the size profile

changes from mill to mill; however, as biomass particle size does not have a

significant effect on biocrude yield [38] the bagasse in its ‘as received’ form

directly proceeds to the slurry preparation tank where it is mixed with ethanol at a

biomass-solvent ratio of 1:19. This ratio was chosen in consideration of the low


solids concentration required for pumping the slurry and the use of similar ratios

in literature [15]. The ethanol used is a combination of recycled ethanol from the

liquefaction section and make-up ethanol. The slurry is assumed to have

consistent 5% solids loading as it proceeds to the liquefaction reactor.

Figure 5.1. Block diagram of the liquefaction plant modelled in this study.

The slurry enters the liquefaction reactor preheated from the recycle heat

from liquefaction product cooling. The hot slurry is further heated to 300 °C and

gets converted to liquefaction products in the reactor. The retention time is set at

0.5 h, which dictates reactor size. The reaction occurs at 165 bar. The liquefaction

product is defined as a gas-liquid-solid mixture. Ethanol losses from this process

was taken to be insignificant. Product yields are commonly reported as a ratio of

product mass to biomass feed mass, with a total of 100%, thus ethanol has been

considered not to contribute to the total product mass [11, 15]. The product

mixture is cooled in heat exchangers to recover heat and separated in flash

separators where the gas is separated from the liquid and solid fraction. The

heavier fraction proceeds to a filter to separate the solid residue from the liquid

product. The filtrate proceeds to the distillation section, while the filter cake is

removed from the process as a char by-product. The liquid retention on the filter

cake used in the model was calculated from the moisture content of filter cakes

similar to the solid products in this process [39]. Considering the nature of the

liquid products, a higher value of 2% of the total liquid products was used. The gas

product is further cooled to ambient temperature (25 °C) to separate light gases

and recover the ethanol.

The liquid products proceed to the solvent recovery section for separation

and recovery of the unreacted ethanol. The solvent recovery section is comprised


of distillation columns, flash separators and condensers, which concentrate the

biocrude and recover ethanol for recycle. The ethanol-biocrude mixture enters a

distillation column with five stages, reflux ratio of 1.5 and distillate to feed ratio of

0.78, mainly to remove very light volatiles and dissolved gases. Steam is used to

facilitate the separation of ethanol from biocrude and reduce cracking potential.

The distillate, which is mostly ethanol by mass, proceeds to the recycle stream.

The distillation bottom stream proceeds to a separator where water is removed,

then to a second distillation to further reduce the ethanol content of the biocrude

and enhance ethanol recovery for reuse. The second distillation feed is heated to

flash 45% of the feed as it enters the second column with 15 stages, reflux ratio

of 1.5 and distillate to feed ratio of 0.7. The flashed feed facilitates the separation

of the light components with the heavy components. The second column distillate

is directed to the recycle stream and the second distillation bottoms with very low

ethanol content proceeds to the hydrodeoxygenation section.

As it enters the HDO section, the biocrude stream is pressurised and pre-

heated before it enters the HDO reactor with pressurised hydrogen. The

hydrodeoxygenation reactions proceed at 300 °C and 80 bar as described by Grilc

et al. [40]. In the HDO reactor, the biocrude is partially hydrogenated and

deoxygenated. The products of the reaction are the hydrodeoxygenated

components, water and coke. Some biocrude components cannot be fully

deoxygenated but are converted into cyclic alcohols, polyols and glycols. The

hydrogenated product is then cooled and depressurised before gases and water

are separated from the organic phase. The oil is then preheated to 75 °C and

directed to the product distillation column with 15 stages and a reflux ratio of 5,

where the feed is equally separated into a light fraction and a heavy fraction.

Ethanol vapours from the solvent recovery section are cooled and degassed.

Laboratory-scale experiments have reported losses in recovering solvents in

product separations [41]. However, the equipment in this plant can be considered

to have been designed to minimise ethanol losses (i.e. process operates in a

closed system with seals and fittings). Processes that have similar separation

schemes have reported a distillation efficiency of 99%, so a 1% ethanol loss in

recovery has been used here [42]. Moreover, since the liquefaction reactor was

modelled using the yield from a single-pass, no recycle process, the biocrude


components entrained in the recovered solvent were considered to be removed in

a process (e.g. adsorption) outside the scope of this study. The removed

components can then be recovered (i.e. through desorption) and added to the

biocrude product, maximising the yield from the process.

Effluent solids and non-condensable vapours are brought to the furnace

where they are burned to supply heat to the plant. Coke from the

hydrodeoxygenation reactor is burnt to regenerate the catalyst. A 20% surplus of

air was modelled to simulate complete combustion. Circulating water is used for

cooling and heating in moderate temperatures.

5.2.2 Modelling

The model of the liquefaction plant was based on the stock ASPEN blocks

and data from literature. The feedstock and biocrude considered were deemed to

be representative of typical materials and the processes employed were selected

on the basis of simplicity and effectiveness to deliver expected outcomes.

The liquefaction process is modelled using a RYield block. Specific reactions

and kinetics of the reactions that occur in liquefaction haven’t been fully

determined, thus modelling the products of liquefaction by yield is a common

approach. The biocrude, gas and solid yields, as well as biocrude composition

used in the model were gleaned from Kosinkova et al. [15]. The solid fraction was

modelled as carbon and silicon that make up biochar and ash yields. Cellobiose

was used to represent heavy components of liquefied bagasse. Gas composition

was obtained from a similar HTL study using similar feedstock [43]. A considerable

effort was made to use the exact chemical species from Kosinkova et al., however,

some compounds that cannot be modelled in ASPEN have been substituted with

compounds available in ASPEN that have similar boiling point, molecular weight

and polarity as shown in a previous study [44]. The list of compounds used in the

model is provided in Appendix C.

The separation process blocks aimed to maximise the separation of

biocrude components from solid and gas phases, and the recovery of ethanol

solvent for recycle. The hydrodeoxygenation reactor was modelled using an RStoic

block with chemical equations representing the hydrogenation or

deoxyhydrogenation of the biocrude components as described in literature [7, 17,

45-65]. The compounds to which the biocrude components were converted are


listed in Appendix C. Several components were deemed to react in the HDO reactor

to become part of coke. Optimum conversion factors as reported by Grlic, et al.

[40, 53] were modelled in the base case. Higher heating values (HHV) of products

were calculated by combusting the product streams in an RStoic reactor set to

model combustion reactions and cool the flue gases to 25 °C, following the

definition of HHV. The ASPEN process flow diagrams are shown in Appendix B1.

A deterministic economic model was built based on the results of the

process model, with mass and energy balances as inputs to calculate operational

and capital costs, and revenues. The plant is modelled to be located in the South

East Queensland region in Australia and rates relevant to the location were used

in the economic model. Capital costs were estimated using cost databases [66,

67] and were adjusted to 2017 prices using the Chemical Engineering Plant Cost

Index (CEPCI) as needed. Other direct and indirect costs were estimated using

ratio factors based on delivered-equipment costs for a solid liquid plant [68].

Economic indicators such as internal rate of return (IRR) and net present value

(NPV) were also calculated, using a tax rate of 30% and a straight line depreciation

method recommended by the Australian Taxation Office [69]. To facilitate

calculations of economic cases, a single price derived from their average was used

for the products. Table 5.2 presents the values of quantities used for the base

case model.


Table 5.2. Quantities used in base case model of the liquefaction plant.

Quantity Value Units Source

Bagasse Properties

Proximate Analysis

Ash Content 6.7 % db [15]

Volatile Matter 76.5 % db [15]

Fixed Carbon 16.8 % db [15]

Moisture 48 % as received

Ultimate Analysis

Carbon 43.16 % db [15]

Hydrogen 5.47 % db [15]

Nitrogen 0.51 % db [15]

Sulfur 2.15 % db [15]

Oxygen 42.57 % db [15]

Liquefaction Yields

Gas 34.7 % of feed as received [15]

Solid 16.1 % of feed as received [15]

Biocrude 49.2 % of feed as received [15]

Ethanol recovered 99 % of input ethanol [42]

Liquid retention on filter cake 2 % of liquid

HDO stoichiometric

conversion

100 % reactive components

Product distillation

Light fraction (distillate) rate 0.5 Mass proportion of

product distillation feed

Heavy fraction (bottoms)

rate

0.5 Mass proportion of

product distillation feed

Economic quantitiesa

Plant size 84000 tonnes per year feed as

received

Operating days per year 350 days

Exchange rate 0.78 US$/AU$ [70]

Power law scaling factor 0.7 [68]

CEPCI for study year 553 [71]

Location factor, Australia 1.4 [72]

Gasoline market price 0.4797 US$/L [73]

Diesel market price 0.4666 US$/L [73]

Feedstock price 46.80 US$/t dry feed [9]

Hydrogen price 1.56 US$/kg [74]

Ethanol price 0.64 US$/L [75]

Catalyst priceb 17.33 US$/kg

Natural gas price 4.63 US$/GJ [76]

Electricity price 38.10 US$/MWh [77]

Water price 2.20 US$/kL [78]

Trade waste handling price 0.76 US$/kL [78]

Maintenance rate 2 % FCIc

Discount rate 10%

Company tax rate 30% [69] a: Values as of July 2017 b: Vendor information c: FCI: Fixed Capital Investment (Direct Costs +

Indirect Costs)



5.3.1 Process Model

Table 5.3 presents the results of the key mass and energy streams

determined in the process model. The plant produced 0.47 kg of

hydrodeoxygenated product per kg of dry feedstock. This is comparable with other

liquefaction plant models. Separation of the hydrodeoxygenated oil yields 0.23

kg/kg dry feed of gasoline-like fraction and 0.23 kg/kg dry feed of diesel-like

fraction.

Table 5.3. Mass and energy balance results from the liquefaction process model.

Stream Production

(per kg feed dry basis)

Units

Biocrude 0.67 kg

HDO Biocrude 0.47 kg

Light Product 0.23 kg

Heavy Product 0.23 kg

CO2 Process

Emissions

1.81 kg

Waste Water 2.23 kg

Stream Consumption

(per kg feed dry basis)

Units

Ethanol Replacement 0.12 kg

Hydrogen 0.03 kg

Natural Gas Heating 0.006 GJ

Electricity 0.18 kWh

Steam 1.92 kg

Combustion Air 6.54 kg

Aside from biomass, the major inputs to the process are make-up ethanol,

hydrogen, natural gas for heating, electricity to drive pumps and compressors,

process steam and combustion air. The process generates 1.81 kg CO2 per kg dry

feed, and 2.23 kg waste water per kg dry feed. The waste water can be used in

steam reforming to produce hydrogen, which may ameliorate the costs associated

with purchasing hydrogen.

The comparison of key stream H/C and O/C values are shown in Figure 5.2.

The grey lines show progression across major processes. The line from the blue to

red point represents liquefaction, while the line from red to green represents HDO.


Figure 5.2. Van Krevelen diagram of bagasse, biocrude, HDO biocrude and light and heavy

fractions presented in molar O/C and H/C ratios.

As expected, the elemental composition between bagasse feedstock,

solvent-free liquefaction biocrude, and hydrodeoxygenated biocrude changes

after each step. In liquefaction with ethanol, oxygen is removed through

dehydration and decarboxylation [5] and separated as water and carbon dioxide

in the gaseous phase or in an aqueous phase immiscible with biocrude.

Experimental measurements by Chumpoo, et al. [11] and Kosinkova et al. [15]

present comparable elemental composition with the modelled biocrude, with the

difference in molar O/C attributed to the presence of heavier oxygenated

components, which may not appear in the gas chromatography data used in the

model, but manifest in the elemental analysis as it is not affected by volatility. In

the HDO process, oxygen is further removed by decarbonylation,

hydrodeoxygenation, decarboxylation and hydrogen is added by hydrogenation

and hydrocracking reactions [79]. In Figure 2, a reduction of oxygen and a more

significant increase in hydrogen can be observed. Distillation of the HDO product

results in two fractions shown as points equidistant to the distillation feed of HDO

0.00

0.20

0.40

0.60

0.80

1.00 1.40 1.80 2.20 2.60

Mo

lar

O/C

Molar H/C

Bagasse Model Biocrude Chumpoo Biocrude Kosinkova BiocrudeHDO Light Heavy Diesel


biocrude, with the heavier fraction having a lower O/C and higher H/C and the light

fraction having a the opposite.

The calculated HHV of the product streams are comparable with expected

values of 45-46 MJ/kg for gasoline and diesel [80]; however, the lower values of

the modelled products cannot be disregarded. The selling price for the products

were therefore scaled using the ratio of the energy content of the modelled

products and conventional fuels to derive realistic litres gasoline or diesel

equivalent (LGE or LDE) values. This adjustment is calculated as shown in

Equation 5.1. The adjustments in price are summarised in Table 4.

𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑝𝑟𝑖𝑐𝑒 (𝑈𝑆$/𝐿) = 𝑀𝑎𝑟𝑘𝑒𝑡 𝑝𝑟𝑖𝑐𝑒 (𝑈𝑆$/𝐿) × 𝐻𝐻𝑉𝑚

𝐻𝐻𝑉𝑝 (Eq. 5.1)

The adjustments in price are summarised in Table 5.4.

Table 5.4. Adjusted price for products modelled in this study.

Product Stream HHV of

petroleum

analogues,

HHVp

(MJ/kg)

HHV of

products

from

model,

HHVm

(MJ/kg)

Market

price

(US$/L)

[73]

Adjusted

price

(US$/LGE

or

US$/LDE)

Light

Product/Gasoline

Equivalent

45.43 43.36 0.4797 0.4578

Heavy

Product/Diesel

Equivalent

45.57 37.64 0.4666 0.3854

5.3.2 Economic Model

The results of the cost estimation and economic modelling described in

Section 5.2.2 is shown on Table 5.5. These values were obtained for the base

case and presented in 2017 US$. The total capital cost for this plant was

determined to be US$ 73.9 million. This is comparable with the scaled and

adjusted capital cost estimated by Zhu et al. [28], although the plant in this study

included more extensive solvent recovery equipment but not a hydrogen

production facility.


Table 5.5. Economic model results.

Quantity Value

Plant capacity, t/y feed as received 84000

Capital cost estimates, million US$

Liquefaction 3.68

Solvent Recovery 1.47

HDO 1.09

Tanks, utility and auxiliary 7.07

Total Purchased Cost 13.05

Location-adjusted Direct Cost 45.14

Total Indirect Costs 16.45

Working Capital 12.32

Total Capital Cost 73.90

Operating Costs, million US$/y

Feedstock Cost 2.04

Electricity 0.30

Heating 1.12

Ethanol Make-up 3.37

HT Catalyst replacement 0.12

Hydrogen 1.77

Steam Supply 2.07

Trade Waste Handling 0.06

Water 1.46

Labour 1.17

Maintenance (2% FCI) 1.23

Total Operating Costs 14.71

Revenue, million US$/y

Light Product 6.67

Heavy Product 4.31

Total Revenue 10.98

Base Economic Indicators

Annual Cash Flow, Million US$ -3.73

NPV, Million US$ -96.1

The operating costs involved amounted to US$ 14.7 million annually or US$

0.57/L product. This unit production cost is around 21% higher than the market

price for gasoline or diesel. The breakdown of the operating costs is shown in

Figure 5.3. The major costs are highlighted in the figure.


Figure 5.3. Breakdown of the liquefaction plant operating costs.

The largest cost is borne from supplying make-up ethanol for the liquefaction

process. This can be potentially reduced by operating with lower losses in ethanol

during recycling. In the process model, it was assumed that 1% of the ethanol is

lost in recovery, as typical in ethanol distillation [42]. Furthermore, a higher

biomass to solvent ratio will reduce the total ethanol used in the plant. For this

plant the biomass to solvent ratio is 1:19, with the slurry solid concentration at

5%. With consideration of slurry pumping requirements, the slurry solid

concentration can further be adjusted to up to 18% [29], which reduces ethanol

intake four-fold. However, the effects on product yield, biocrude composition, and

energy requirements should be accounted for. Lower slurry concentrations tend

to have better liquid yields [38], but lower solvent use also decreases energy

requirements for heating and pumping. Heating the slurry in liquefaction was

significant in this study considering half of the heating cost was for liquefaction

heating. Steam and hydrogen were also major costs in the plant, but are deemed

materially essential in critical processes. Feedstock cost was also significant,

which is in consonance with other studies [28, 29, 34]. Sugarcane bagasse has a

moderate price compared to virgin wood feedstock or low-grade residues. This is

due to its nature as a process residue of sugar production. Sugarcane bagasse is

readily available in bulk as a waste material, with minimal collection and

transportation costs [81]. However, its potential as an energy feedstock or fibre

source has elevated its value. Sugar producers use sugarcane bagasse as fuel for

heat and power [82], with its value varying depending on the factory’s heat and

power needs [9]. Sugarcane bagasse has also found its way as a feedstock for

Feedstock

cost

12%

Heating

15%

Ethanol

20%Hydrogen

15%

Steam

12%

Feedstock cost

Electricity

Heating

Ethanol

HT Catalyst replacement

Hydrogen

Steam

Trade Waste

Water

Labour

Maintenance (2% FCI)


pulp and paper production [83], which is further increasing its value and the

complexity of sugarcane bagasse pricing and demand.

The products from the plant generated two co-dependent revenue streams.

The process split the HDO biocrude into two equal mass flow rate product streams;

however due to the lower high heating value of the products relative to their

petroleum analogues, the revenue share was calculated to be 61% from the light

product and 39% from the heavy product. There were no other product and

revenue streams as solid and gaseous byproducts were used to generate heat.

Other outgoing streams were gaseous emissions and waste water, which were

deemed not to have marketable value.

At the adjusted product price based on market data [73], the annual revenue

was only 75% of the operating costs. Therefore, the cash flow before tax calculated

was negative, resulting in a large negative NPV. The MSP was calculated as US$

0.99/L, which was more than twice the average product price of US$ 0.42/L. Zhu

et al. [28] reports US$ 1.17/L from the base case and US$ 0.67/L from a

projected high production efficiency plant. Magdeldin et al. [29] calculated an MSP

of US$ 2.09 for a plant similar to this study and US$ 1.94/L for a plant with an in-

house hydrogen production facility similar to the base case model in Zhu et al.[28].

It should be noted that the values for product prices here were adjusted for heating

value discrepancies and represent price of petroleum fuel equivalent.

5.3.3 Continuous versus semi-batch

The base case was modelled on a continuous liquefaction mode of operation

as is typical of commercial-scale plants. However, a semi-batch mode for

liquefaction was considered to reflect the nature of how laboratory-scale

experiments are carried out. Each mode has its own advantages and

disadvantages. A continuous plug-flow type reactor ensures a seamless operation

without need for controls to switch between several semi-batch reactors. A

temperature profile within an acceptable range for the entire length of the reactor

is less complicated to sustain for a continuous reactor, while a stirred tank in semi-

batch mode may have zones of varying temperatures [29], due to the reliance on

fluid convection within the tank to distribute the heat from the walls to the rest of

the reactor. Other heating configurations such as coiled tubes can ameliorate heat

distribution but can hinder agitation and prevent consistent composition and


adequate contact of reagents. This can affect completion of reactions and

consistency of product. Semi-batch equipment will also be larger and connections

need to be able to withstand the high pressures in which the reaction is operated.

On the other hand, continuous reactors can involve racks of smaller tubes;

however, since the reaction involves solids in slurry and viscous products, plugging

of equipment can be an issue. Compared with semi-batch modes, there is little

room for redundancy in continuous mode, in case of process upsets. Nonetheless,

there is adequate interest to examine the economic implications of designing a

semi-batch process to compare it with continuous mode.

The mass and energy balances of semi-batch and continuous modes are

identical, with the exception of energy requirements around pumping and

agitation. The differences are presented in Table 5.6.

Table 5.6. Comparison of semi-batch and continuous operating modes.

Property Semi-batch Continuous

Slurry preparation None Slurry is prepared in a

mixing tank

Pumping into the reactor Centrifugal pump, low

pressure

Diaphragm pump, high

pressure

Agitation in reactor Turbine impeller None

Reactor operation Staggered operation of

four separate reactor

vessels to maintain

mass flow rate

Slurry runs through

several tubes in

parallel continuously

Pertinent equipment in

liquefaction section

Ethanol pump

Liquefaction reactor

vessels

Agitation drivers

Slurry preparation tank

Slurry diaphragm

pump

Continuous plug flow

reactors

Energy required in

liquefaction section

Ethanol pump: 13.4

kW

Agitation: 20 kW

Slurry pump: 683.6 kW

Capital cost, Million US$ 94.4 73.9

Operational cost, Million

US$/y

14.5 14.7

NPV -113.1 -96.1

MSP, US$/L 1.11 0.99

As shown in Table 5.6, continuous mode required more energy compared to

semi-batch mode due to the pumping of the slurry to allow for the reaction to occur


at liquefaction pressure. This resulted in a slightly higher operational cost for the

continuous mode. However, the capital cost for a semi-batch plant was

significantly higher from the purchase of large high pressure-rated vessels. This

adversely affected the NPV. Consequently, the MSP is higher for semi-batch in

order to recover the large capital expenditure with a larger revenue. The

continuous mode of operation was demonstrated to be incrementally better.

5.3.4 Sensitivity analysis

Several key parameters were varied to determine their impact on the

profitability and commercial viability of the liquefaction plant through analysis of

the changes in economic indicators. The parameters varied were plant capacity,

liquefaction yield, and HDO conversion in the technical model, and feedstock

price, product price, ethanol price, natural gas price, and discount rate in the

economic model. The tornado diagram illustrating the effect of these parameters

on the NPV is shown in Figure 5.4. The parameters were changed 50% each way

and the NPV was calculated for each change. The difference of the new NPV from

the base NPV was calculated and used in the tornado diagram.


Figure 5.4. Sensitivity of plant NPV to changes in technical and economic parameters within a ± 50% base case value range. Base NPV is US$ -96 Million. Delta NPV is the difference of the NPV

calculated using the range of values for each parameter to the base NPV. Red bars indicate result from low (-50%) values and blue bars represent result from high (+50%) values. White lines

indicate result from ±10% values.

The NPV was most sensitive to product price. This was due to the direct

impact of product price on revenue and consequently, annual cash flow. The range

between high and low values was US$ 85 Million, with only a US$ 0.42/L product

price difference. In contrast, the feedstock price did not affect NPV as much,

decreasing US$ 8 million from a US$ 46.8/t feedstock price increase between low

and high states. This change in feedstock price translates to a US$ 0.15/L

increase in unit production costs, but its effect on the NPV is not proportional to

the effect of product price due to the dilution of the effect of feedstock price with

the conversions in the process.

HDO conversion was the process parameter that affected profitability the

most. The change in HDO conversion from 50% to 100% changed the NPV by US$

40 million. The effect of varying the HDO conversion was manifested in both the

quantity and quality of products. A lower conversion generated decreased product

quantities. The quality, measured by the calculated HHV also changed. The new

HHV values were used to adjust the selling price. The change in product output

0.210

50

5000

23.68

0.32

23.4

2.31

0.631

100

15000

71.04

0.96

70.2

6.94

-60 -40 -20 0 20 40 60

Product Price (US$)

HDO Conversion (%)

Plant Capacity (t/y)

Biocrude Yield (%)

Ethanol Price (US$/L)

Feedstock Price (US$/dry t)

Natural Gas Price (US$/GJ)

Delta NPV [Base Value = 0 (Million USD)]


(kg/h) and adjusted price (US$/LGE and US$/LDE) both affected revenue. The

cost associated with lower hydrogen consumption for lower conversions was also

accounted for as a change in operational costs.

Plant capacity also significantly affected the plant economics. This was a

parameter that affected revenue, operational costs and capital expenditure. As

the plant capacity was varied, the capital costs were changed using a capacity

scaling factor of 0.7. The operational costs were adjusted for the major cost

streams and the revenue was changed with the varying product rates. With higher

plant capacity, the capital costs increased more steeply than the changes in

operational costs and revenue, thereby having a sizable negative effect on NPV.

Revenue increased linearly, as with operational costs, due to the uniform scaling

of the rates of raw materials, chemicals, energy use and products. Biocrude yield

affected revenue directly due to the changing rate of final product, and only

affected operational costs slightly with relatively smaller changes in pumping and

hydrogen costs.

Variation of costs of other inputs such as ethanol and natural gas for heating

also presented significant changes to the NPV, however, due to their less

significant roles in the process the effects are less pronounced compared to the

other parameters.

5.3.5 Minimum selling price analysis

As shown in the sensitivity analysis, the product price will be a critical factor

in the profitability of the plant not only by its direct effect on cash flow, but also

due to how much the price of petroleum analogues change. The 12-month data of

gasoline and diesel products obtained for this study showed a variation of 8-11%

from the average price. The price fluctuations are expected from multiple factors

in the petroleum market, and large market events such as oil crises can affect

prices beyond seasonal variations. However, at the moment the more pertinent

issue for fuels derived through the liquefaction pathway is the product price

reaching a level where it can compete with conventional fuels.

As discussed in Section 3.2, the average MSP for the two product streams is

US$ 0.99/L. As parameters were varied in the sensitivity analysis, the new MSP

was also calculated alongside NPVs. Figure 5.5 shows the variation of MSP values


with the varying process parameters and Figure 5.6 shows the variation with

varying economic parameters.

Figure 5.5. MSP values for varying liquefaction process parameters.

0 10 20 30 40 50 60 70 80 90 100

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

0 1600 3200 4800 6400 8000 9600 11200 12800 14400 16000

Biocrude Yield, %

HDO Conversion, %

MS

P, U

S$

/L

Plant Capacity, kg/h


Figure 5.6. MSP values for varying economic parameters.

The product price increases to US$ 1.14/L for a 5000 kg/h plant, and goes

down to US$ 0.92/L for a 15000 kg/hr plant. Even as operational and capital

costs increase with a higher plant capacity, the production volume was adequate

to lower the MSP, instead of increasing it. Higher biocrude yields, on the other

hand, decreases the MSP down to US$ 0.70/L due to the increased availability of

biocrude for conversion to fuel products. Conversely, the decrease in yield

increased the MSP. Lower HDO conversion increases the MSP steeply, to US$

2.1/L for a 50% HDO conversion scenario. Among the three process parameters,

it was apparent that HDO conversion can significantly affect the profitability of the

plant.

Figure 5.6 shows the effect of varying two economic parameters that change

through different mechanisms. The change in ethanol price is usually market-

driven while changes in tax rate is policy-driven. From the sensitivity analysis, it

has been determined that ethanol price has a moderate effect to NPV. This was

further reflected on the change to the minimum selling price. While the ethanol

price varies from US$ 0.32/L to US$ 0.96/L the MSP changes from US$ 0.92 to

US$ 1.06. This effect is similar to the effect of changing the tax rate. An Australian

corporate tax rate of 30% was used in the base case, and ±10% and ±50%

changes were modelled into the discounted cash flow analysis to determine the

changes to the MSP. When tax rate was lowered to 15%, the MSP decreased to

$0.00 $0.20 $0.40 $0.60 $0.80 $1.00

$0.80

$0.85

$0.90

$0.95

$1.00

$1.05

$1.10

0% 10% 20% 30% 40% 50%

Ethanol price, US$/kg

MS

P, U

S$

/L

Tax Rate, %


US$ 0.94/L. A high tax rate of 45% increased the MSP to US$ 1.07/L. MSP at

lower tax rates (0-10%) were also determined to analyse the effect of tax holidays

and low tax rates for biofuels and renewable energy projects that are currently

employed in a number of countries [84].

Figures 5.5 and 5.6 also illustrated the profitable price boundaries when the

plant is operating with the parameters in the graph. For example, when looking at

HDO conversion, shown in Figure 5.7, the MSP line serves as the boundary where

the area about it is the profitable region, where operating at any point will have an

NPV greater than zero. Below the line will be operating at a loss, and results in a

negative NPV.

Figure 5.7. Profitable (green) and non-profitable (red) operating regions separated by the MSP

line (blue), for varying HDO conversion.

In this model’s base case, the value of the HDO conversion was 100%. This

meant that biocrude components were converted to the HDO biocrude

components with some oxygen content, or coke, and not full conversion to

components found refined in petroleum products. This value also does not

represent the effects of reactor conditions such as space velocity and the

adsorptive properties of the catalysts. The model also assumed that the

conversions for each component were carried out independent of each other, thus

the synergistic or impeding effects of the reactions occurring in parallel on the

catalyst surface were not considered. These may cause the conversion to be less

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

50% 60% 70% 80% 90% 100%

MS

P, U

S$

/L

HDO Conversion, %

NPV>0

(Profitable)

NPV<0

(Non-profitable)


than 100%. Improvements in reactor design and effectiveness of catalysts can

enhance the stability of the process and ensure uniform outputs.

5.4 Conclusion

The model of a thermal liquefaction plant converting sugarcane bagasse to

liquid fuels located in Queensland, Australia demonstrated the viability of a plant

taking in waste lignocellulosic biomass and converting it to biofuels. The use of

ethanol instead of water as liquefaction solvent, which was proposed to be

advantageous, was employed in the model. The technology available at this time

leads to a high operating cost that cannot be exceeded by revenue generated from

selling the fuel competitively at the current market price of fossil fuels. The

minimum selling price of the biofuels was calculated to be US$ 0.99/L for the

base case. To determine the opportune areas to improve the net present value,

key technical and economic factors such as liquefaction yield, hydrodeoxygenation

conversion, plant capacity and raw material and product price were analysed. It

was determined that for this plant, product price, hydrodeoxygenation conversion

and plant capacity were the factors to which net present value is most sensitive.

This provides information that the economic risks can be better managed by

ensuring stability or positive improvement of these parameters along the plant life.

From analysing the minimum selling price, it was shown that improvements in

biocrude yield and hydrodeoxygenation conversion lower the minimum selling

price. Lower tax rates also affect profitability positively. Overall, it has been

determined that producing biofuels from sugarcane bagasse has potential, and

further research to improve yields and efficiencies should be pursued to enhance

profitability.

5.5 Acknowledgments


Australian Government. The authors would like to thank Dr Jana Adamovska for

some insights on process design.


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Chapter 6: Comparative techno-economic

analysis of biofuel production

through gasification thermal

liquefaction and pyrolysis of

sugarcane bagasse

Jerome A. Ramirez and Thomas J. Rainey




Submitted to Bioresource Technology (Q1)









publication;









Title: Comparative techno-economic analysis of biofuel production through

gasification thermal liquefaction and pyrolysis of sugarcane bagasse (2017,

submitted)



Thomas Rainey Assisted with preparing the manuscript; Provided

editorial contributions.





Name Signature Date


Comparative techno-economic analysis of biofuel

production through gasification, thermal liquefaction

and pyrolysis of sugarcane bagasse

Jerome A. Ramirez and Thomas J. Rainey




Abstract: Techno-economic models for three plants in Queensland, Australia

using thermochemical conversion of 10 t/h of sugarcane bagasse to liquid crude

biofuels were developed in this study. Thermochemical conversion was chosen

to maximise the product yield from second-generation feedstock. The process

models highlighted the differences in mass and energy flows and products of

each process. Factory models were generated reflecting current methods in heat

and material recovery. Liquefaction generated the highest amount of fuel

product per kg feed, followed by pyrolysis, and gasification had the least. Key

parameters that affect the plant economics were also highlighted in the models.

Based on net present values, the profitability of the three plants were ranked as

follows: pyrolysis>liquefaction>gasification. The three plants were all sensitive to

product price, thermochemical conversion ratio and refining conversion ratio

were shown to affect profitability the most. Conversion ratios also sharply affect

the minimum selling price of products, but the effect is attenuated by high

production volumes. Varying tax rates and capital costs affect the minimum

selling price moderately, but not as much as conversion ratios, therefore,

incentives around researching improving conversion rates, thermal efficiency

and consequently increasing product volume are recommended. For the facilities

modelled, gasification required more heat, while liquefaction required more

electrical power, compared with the others. The liquefaction plant emitted more

CO2 than the rest per kg feed.


6.1 Introduction

The increase in demand, the imperative to reduce greenhouse emissions,

and the drive for sustainable production present challenges to the future of

energy. The development of alternative energy sources and production processes

must consider affordability, accessibility, sustainability and equity [1]. From a

technology viewpoint, the analysis is focused on the efficiency in production

processes and the use of feedstock. Furthermore, a significant focus has been on

developing biofuels since transportation is responsible for 23% of energy-related

CO2 emissions mostly due to increasing transportation activity [2]. Biofuels have

been shown to provide a low carbon intensity alternative to fossil fuels based on

life-cycle greenhouse gas savings [3], therefore the development of viable

processes and biofuel products and the identification of suitable feedstock are

the focus of many biofuel studies.

Among the various feedstock identified for biofuel production, waste

agricultural and process biomass are advantageous due to better environmental

impacts over the life cycle. Compared with the use of dedicated crops, the use of

residues as biofuel feedstock instead of disposal result in lower net impacts and

emissions. This may offset fossil fuel inputs in biofuel processing [4]. The use of

non-edible feedstock also avoids complications related to food supply [5], which

are present in first-generation biofuels. Moreover, the conversion of agricultural

residues to biofuels is value-adding as additional revenue could be generated from

selling the fuel. For instance, using residues from sugar production (i.e. bagasse)

reduces potential costs related to disposal while taking advantage of the reliability

of feedstock supply. Established agricultural methods and the ubiquity of sugar

production enables bagasse supply to be consistent [6]. Sugarcane bagasse can

also be easily collected from the sugar factory and stockpiled to offset the effect

of variations in harvesting periods [7]. Globally, it was estimated that 540 million

t/y of sugarcane bagasse is produced [8], demonstrating significant potential for

large-scale biofuel production in areas where sugarcane is grown.

In Australia, there is an opportunity to use bagasse to augment biofuel

production. Bagasse has been demonstrated as a reliable energy source through

its use in providing 102 PJ of heat and power in 2015-16 from 10.7 million t/y of

bagasse [9]. This represents only 1.7% of the total Australian energy mix. In


contrast, biofuels (i.e. ethanol and biodiesel) only account for 0.5% of road

transport fuels in the same period. Although biofuels have been used for transport

in Australia for a decade, the reliance on fossil fuels are reflected resoundingly in

the transport energy mix [10]. This demonstrates an opportunity to shift or add

utilisation of bagasse to the production of liquid fuels. Kosinkova et al [11] also

emphasised the potential for bagasse and other biomass as feedstock for biofuel

production.

The use of appropriate technologies to convert solid biomass to liquid

biofuels is a crucial part of the supply chain [12]. Compared to first-generation

biofuel processing, second-generation biofuel processing has been met with

challenges in regard to dealing with the heterogeneity of biomass composition,

which requires several different methods to break the polymeric structures of

cellulose and hemicellulose, and the complex structure of lignin, and/or extract

sugars or lipids to generate energy dense products. From this juncture,

thermochemical processes have been promoted as suitable conversion

technologies for lignocellulosic materials due to its indiscriminating mechanism to

convert solids to liquids or gases that can be further refined to make fuels. Table

6.1 presents a comparison of three key thermochemical conversions: gasification,

thermal liquefaction and pyrolysis.


Table 6.1. Comparison of thermochemical processes from [13-16].

Thermochemical

Process

Gasification Thermal

liquefaction

Pyrolysis

Operating parameters

Temperature, °C 800-1000 250-330 280-630

Pressure, bar 1-20 1-240 1-5

Solvent requirement None Water or

organic solvent

None

Feedstock

requirement

Dry, size-

reduced

Dry, moist or in

slurry

Dry, size-

reduced

Product profile

Phase (main products) Gas Liquid Liquid, gas

Product namea Syngas Biocrude Bio-oil

Chemical composition Carbon

monoxide (CO),

carbon dioxide

(CO2), hydrogen

(H2), methane

(CH4)

Phenolic

compounds,

aromatics,

molecules with

carbonyl groups

Molecules with

carbonyl

groups, sugars,

dehydrosugars,

phenolic

compounds

Heating Value, MJ/kg 27-33 28-36 16-19 aTypical term used in literature; terminology to be used in this study

The common mechanism for gasification, liquefaction and pyrolysis is

breaking down large cellulose, hemicellulose and lignin molecules. The high

temperature in which the processes occur provides the enthalpy to break bonds.

In pyrolysis and gasification, components are vapourised in the early stages, then

crack into light hydrocarbons, aromatics and oxygenates [17]. In pyrolysis, the

intermediate products in gaseous form are rapidly condensed after a brief

residence time [16]. Higher gasification temperatures progress the conversion to

light olefins, CO and more aromatics, then to production of light gases such as CO,

CO2, H2 and CH4 and polynuclear aromatics that form soot [17]. Liquefaction on

the other hand, takes advantage of solvent properties at high pressures and

temperatures [18] such as reduced mass transfer resistances and higher

penetration of the solvent into the biomass structure [19]. Following

depolymerisation of the biomass, the components decompose through cracking,

dehydration, and decarboxylation and the reactive fragments recombine to

produce other compounds [20].

The differences in operating conditions affect the characteristics of the

products of the three processes. The high gasification temperature produces


gaseous products, which are mainly permanent gases and tars that make up

syngas, while the immediate cooling following pyrolysis, results in a condensed

phase and a gas product. Liquefaction produces a higher yield liquid product

alongside the solvent. All of the processes produce gaseous, liquid and solid by-

products that require separation from the main product.

Another common feature of the thermochemical processes is the need for

further upgrading and refining of their products into a fuel that can be readily used.

Syngas needs to be cleaned of impurities prior to fuel production [21]. Following

cleaning and before fuel synthesis, the syngas needs its composition to be

conditioned to a proper H2/CO ratio to achieve desired results [22, 23]. The syngas

then will be processed in a catalytic reactor where it is converted to fuel [24].

Biocrude and bio-oil have similar physical and chemical properties. Both are liquid

in ambient temperature; however pyrolysis bio-oil has a higher moisture and

oxygen content [25]. Both products also demonstrated changes in physical and

chemical properties over time (i.e. aging) [26, 27]. These undesirable fuel

properties may be corrected through catalytic hydrotreatment where oxygen is

removed and hydrogen is added [15, 28], although bio-oil requires a mild

hydrogenation step to reduce formation of coke at severe conditions [27], while

biocrude can be directly processed in a severe hydrodeoxygenation process [29].

There are several techno-economic studies on pyrolysis and gasification

since they have been developed previously. Studies that compare pyrolysis and

gasification to each other and to other processes have also been published [30].

However, as liquefaction is relatively novel, a comparative study between

gasification, pyrolysis and liquefaction has not been performed previously. To this

end, this study laterally compares these thermochemical processes that can be

employed to produce liquid fuels from sugarcane bagasse through a techno-

economic analysis. This is a common method to adequately compare the

economic implications of employing biomass to energy processes [31-33].

Development of mass and energy balances are also widely used as the initial step

in life cycle analysis [34].

The inputs, requirements and configurations of each process differ from the

others; however, the intent to produce a biofuel product is the same. Each process

has advantages and drawbacks compared with each other, such as the cost of


drying, the need for solvents, differences in temperatures and pressures, syngas

cleaning, and the like; however these differences have not been adequately

quantified and their effects to profitability have not been sufficiently analysed. The

ASPEN Plus models and economic models of each process allow the

demonstration of their technical and economic viability, enable a comparison of

economic indicators, and lead to the analysis of key process and economic

parameters.

6.2 Methodology

Bagasse to biofuel plants employing gasification, liquefaction or pyrolysis

were modelled in ASPEN Plus version 8.4. This modelling tool was shown to be

suitable in earlier work [35, 36]. Each plant was modelled with the different

conditions of each process, and generating different products. The refining and

upgrading processes for each thermochemical plant to generate a crude-oil like

product were also modelled and are described in the following subsections.

For each plant, 10,000 kg/h of sugarcane bagasse was the main input. The

feedstock was assumed to have 48% moisture as received and uniform

composition. Sugarcane bagasse is usually produced in sugar factories 5-9

months in a year and might be collected in stockpiles where moisture levels can

vary [7], however, for this study, it is assumed that the average values used in the

models are representative of the likely value of these properties when sampled.

The properties of the feedstock used are presented in Table 6.2.


Table 6.2. Properties of sugarcane bagasse used as feedstock in this study.

Bagasse Property Value Units

Proximate Analysis

Ash Content 6.7 % db

Volatile Matter 76.5 % db

Fixed Carbon 16.8 % db

Moisture 48 % as received

Ultimate Analysis

Carbon 43.16 % db

Hydrogen 5.47 % db

Nitrogen 0.51 % db

Sulfur 2.15 % db

Oxygen 42.57 % db

Particle size distribution [37]

>12.5 mm 0.25 mass fraction

4.0-12.5 mm 0.35 mass fraction

< 4.0 mm 0.4 mass fraction

It is expected that the size profile changes from mill to mill; however,

biomass particle size does not have a significant effect on liquefaction yield [38],

and bagasse feed size is reduced and screened for both gasification and pyrolysis,

where particle size matters [15, 39]. The different plants will be referred to as

gasification, liquefaction and pyrolysis, which is labelled after the central

thermochemical process and encompasses the corresponding refining and

upgrading processes, separation and auxiliary operations. To differentiate the

products of the thermochemical processes and refined products, the products of

the gasification plant will be referred to as syngas and FT liquids, that of

liquefaction will be called biocrude and HDO biocrude, while that of pyrolysis will

be named bio-oil and HT bio-oil.

6.2.1 Description of Process Models

6.2.1.1 Gasification

Bagasse as received enters the dryer where its moisture content is reduced

to 7%. The dried bagasse is then ground and screened to ensure that the particle

size entering the gasifier is no larger than 2 mm. The dried and ground feedstock

is fed with steam to the gasifier and converted to gaseous products and ash at

900 °C and 1 bar. This process has been modelled in ASPEN as a combination of

an RYield block separating the elements comprising the feedstock and feeding it

into an RGibbs block that generates products through an equilibrium path with the


minimal Gibbs free energy at a specified temperature, pressure and number of

phases. Following gasification, ash is removed in a cyclone, and the gas enters a

tar reforming reactor to convert hydrocarbons and ammonia to hydrogen, carbon

dioxide and nitrogen. The tar reformer was modelled with an RStoic block with the

reforming reactions and the specified conversion rates (Table 3). Heat is

recovered for heating other sections of the plant at the same time cooling the gas

product stream in preparation for gas cleaning.

The gasified products proceeds to the gas cleaning section where the syngas

is scrubbed with methyldiethanolamine (MDEA) to remove hydrogen sulphide [40],

which can poison catalysts in the fuel synthesis section [21]. Carbon dioxide is

also removed to reduce inert components in syngas that can increase reactor

volume unnecessarily. Scrubbers were modelled as an 8-stage RadFrac column

where impurities are both physically and chemically absorbed into the MDEA-water

stream, which ASPEN calculates with the ELECNRTL property method to handle

the electrolyte-water system. The H2S- and CO2-rich amine proceeds to a stripper

to separate the gases from the liquid stream and produce the lean amine for

recycling into the gas scrubbers. The stripper was modelled using a RadFrac with

a reboiler, heat exchangers and flash separators. The clean syngas is then

compressed and preheated for the Fischer-Tropsch (FT) reactor.

Syngas enters the FT reactor where the synthesis of hydrocarbons from

carbon monoxide and hydrogen in presence of a cobalt catalyst occurs at 200 °C

and 20 bar [41]. A water-gas shift reaction was also modelled to ensure the proper

H2/CO ratio of 2 that is required for the FT reaction. The FT reactions to produce

paraffins and olefins from C1-C25 were modelled with chain growth probability

factor, α = 0.9, using the Anderson-Schulz-Flory distribution (Eq 6.1), where Mn is

the mass fraction of a hydrocarbon product with n carbons [22]. A CO conversion

of 70% was used [42]. The FT reactor was modelled using an RStoic block.

𝑀𝑛 = 𝛼𝑛−1(1 − 𝛼) (Eq. 6.1)

The products of the FT reactor were cooled and separated. 95% of the

gaseous products were recycled to the gas cleaning section to maximise the

hydrocarbon production from the available CO. The rest of the gaseous products


were used in a syngas power generation unit to supply electricity to work-requiring

processes. The liquid products were decanted to separate the water and gases

and light components were separated from heavier components in a distillation

column with 15 stages, reflux ratio of 2 and a distillate to feed ratio of 0.2. The

distillation bottoms were cooled and collected as a product. Auxiliary processes

such as heat exchange, steam and power generation were also modelled in

ASPEN. Power was generated through the expansion of flue gases and steam in a

Rankine cycle. The parameters used in this process model are given in Table 6.3.

Table 6.3. Modelling data used in the gasification model.


Drying target 7 % moisture content

Grinding and screening target 2 mm

Steam to biomass ratio 1.8 kg steam/kg biomass [43]

Tar reforming conversion

Methane 0.80 [44]

Ethane 0.99 [44]

Ethene 0.90 [44]

Benzene 0.99 [44]

Ammonia 0.90 [44]

MDEA:Water ratio 0.25 kg MDEA/kg water [45]

Product Separation

Gaseous product (distillate) rate 0.2 mass proportion of

product distillation

feed

Liquid product (bottoms) rate 0.8 mass proportion of

product distillation

feed

6.2.1.2 Liquefaction

Bagasse as received is mixed with ethanol to prepare a slurry of 5% solid

content for liquefaction. Ethanol was chosen to be a solvent due to its desirable

thermal properties leading to more desirable yield and biocrude heating value

results [46]. The slurry is pumped and preheated to the continuous reactor where

the bagasse is liquefied at 300 °C and 165 bar. The products are then separated

in gaseous, liquid and solid streams. The liquid stream enters a solvent recovery

section where ethanol is recovered as a recycle stream. The concentrated

biocrude then enters the hydrodeoxygenation reactor where it reacts with

hydrogen over a solid catalyst at 300 °C and 80 bar. A significant amount of

oxygen is removed as water and carbon dioxide. The HDO products are separated

and the liquid product is collected and cooled. A further process description is


available in a previous study [47]. The parameters used in this process model are

given in Table 6.4.

Table 6.4. Modelling data used in the liquefaction model.


Liquefaction Yields

Gas 34.7 % of feed as received [46]

Solid 16.1 % of feed as received [46]

Biocrude 49.2 % of feed as received [46]

Ethanol recovered 99 % of input ethanol [48]

Biocrude and ethanol retention on

filter cake

2 % of total liquid

products

HDO stoichiometric conversion 100 % reactive components

6.2.1.3 Pyrolysis

As in gasification, bagasse as received enters the plant through the dryer

and grinder, where it is pre-processed to 7% moisture and particle size of no larger

than 2 mm. The dried and ground bagasse is fed to the pyrolysis process where it

is converted to condensable and non-condensable gaseous products, char and

ash at 500 °C and 1 bar. The pyrolysis reactor was modelled with an RYield block

using product yields and chemical profile by Varma and Mondal [49] matched to

the model compounds used by Jones, et al. [50] to represent functional groups of

chemicals in the bio-oil.

The pyrolysis products are then separated in an electrostatic precipitator

where solid residues and liquid droplets that might condense are separated from

the hot gases. The heavy components proceed to the filter where the liquid is

separated from the char. It was assumed that 20% of the liquid remains with char.

The filtrate then proceeds to the hydrotreatment section. From the electrostatic

precipitator, the gaseous bio-oil proceeds to a contact tower where condensed bio-

oil cools the bio-oil vapour. The contact tower was modelled with a RadFrac column

with 10 stages, a partial vapour condenser and a reflux ratio of 3. The condensed

bio-oil exits the tower and is cooled further and 70% of the cooled bio-oil is recycled

back to the contact tower, while the rest moves on to the hydrotreatment section.

The exiting cooled vapours from the tower are further flashed to separate non-

condensable gases from bio-oil that may have condensed further.

All streams of cooled and separated bio-oil are combined and proceed to the

hydrotreatment section. The bio-oil is pressurised and preheated before it enters


the first hydrotreatment reactor. Pressurised hydrogen is also combined with the

bio-oil upon entering the reactor. The first hydrotreatment reactor proceeds at 250

°C and 140 bar. In this mild hydrotreatment stage, the bio-oil is partially

hydrogenated and deoxygenated. The first stage products move to the second

stage, but not before preheating. The second stage proceeds at 400 °C and 140

bar, with pressurised hydrogen added in a stoichiometric amount. At this more

severe stage, significant amounts of oxygen are removed and hydrogenated to

water or removed as carbon dioxide. Some bio-oil species cannot be fully

deoxygenated but are converted into hydrofurans, cyclic alcohols, diols and

glycols. The hydrotreatment reactors were modelled using RStoic blocks, using

reactions from literature, with reaction conversions between 98-100% [51-57].

The reactivity of the bio-oil components in mild and severe reactor stages were

determined through the reactivity scale laid out by Elliott [58].

Following hydrotreatment, the products are cooled in the hydrotreatment

preheating heat exchangers to recycle heat. The products are also expanded in a

turbine and further cooled. Solid residues and gaseous fractions are separated

from the hydrotreated liquid product. Water is removed before the liquid product

is cooled and collected for storage.

Solids from the pyrolysis reactor and non-condensable gases from the plant

is used to generate heat and power for use in the plant, which was modelled in

ASPEN as a Rankine cycle. A simple heat recovery network was also designed for

major cooling and heating requirements. The parameters used in this process

model are given in Table 6.5.


Table 6.5. Modelling data used in the pyrolysis model.


Drying target 7 % moisture content [59]

Grinding and screening target 2 mm [15]

Pyrolysis product breakdown

Bio-oil 36 % total product [49]

Water 9 % total product [49]

Gas 26 % total product [49]

Solids 29 % total product [49]

Bio-oil retention on filter cake 20 % bio-oil [45]

Bio-oil quenching

Recycled to quench 0.7 Mass proportion of

cooled bio-oil

For hydrotreatment 0.3 Mass proportion of

cooled bio-oil

6.2.2 Modelling

The mass and energy models of the three plants were constructed using pre-

defined ASPEN blocks. Separations were carried out as programmed in ASPEN,

although in some instances assumptions were made in order to efficiently build

process models that maximises the product streams. ASPEN process flow

diagrams are shown in Appendix B2-B4.

Following the process model, deterministic economic models were also built

to analyse the profitability of the plants. Using mass and energy balances, the

equipment sizing and capital costs, raw material, energy, catalyst, labour and

maintenance costs were estimated from various data sources. The revenue from

product sales was also calculated using the product streams and market price of

the product. The plant was modelled to be located in the South East Queensland

region in Australia, therefore the relevant rates and prices were used in the

economic model. Capital costs were determined through calculations in cost

databases [60, 61], and adjusted using the Chemical Engineering Plant Cost Index

(CEPCI) to 2017 values. Direct and indirect costs were estimated using ratio

factors based on delivered-equipment costs [62]. A tax rate of 30% and a straight

line depreciation schedule based on the method prescribed by the Australian

Taxation Office [63] was used. Net present value (NPV) and internal rate of return

(IRR) were calculated using a discount rate of 10% and used as the primary

economic indicator for this study. The minimum selling price was calculated by


determining the product price where in the NPV is zero at the discount rate. The

economic parameters used in the economic models are presented in Table 6.6.

Table 6.6. Economic modelling data used in this study.

Economic quantitiesa Value Unit Source

Plant size 84000 tonnes per year

feed as received

Operating days per year 350 days

Exchange rate 0.78 US$/AU$ [64]

Power law scaling factor 0.7 [62]

CEPCI for study year 553 [65]

Location factor, Australia 1.4 [66]

Feedstock price 46.80 US$/t dry feed [67]

Hydrogen price 1.56 US$/kg [68]

Ethanol price 0.64 US$/L [69]

FT Catalyst priceb 38.49 US$/kg [70]

Tar Reforming Catalyst priceb 20.53 US$/kg [24]

HDO Catalyst pricec 17.33 US$/kg

HT Catalyst pricec 17.33 US$/kg

MDEA priceb 2.60 US$/kg [71]

Natural gas price 4.63 US$/GJ [72]

Electricity price 38.10 US$/MWh [73]

Water price 2.20 US$/kL [74]

Trade waste handling price 0.76 US$/kL [74]

Crude oil price 0.327 US$/L [75]

Maintenance rate 2 % FCId

Discount rate 10%

Company tax rate 30% [63] a: Values as of July 2017 b: CPI adjusted to 2017 values c: Vendor information d: FCI: Fixed Capital

Investment (Direct Costs + Indirect Costs)

Sensitivity analyses were also conducted to present the effect of fluctuating

conditions on NPV and the MSP. The parameters were varied ±50% of their base

values. Thermochemical conversion and refining conversion were the process

parameters changed, while the prices of products, feedstock, natural gas,

chemicals (ethanol and MDEA) and hydrogen were the economic parameters

varied. The tax rate was also varied to illustrate the effect of policy-driven changes

to profitability.


6.3.1 Process Model

The main inputs and outputs of the three processes are comparable.

Conceptually, fibrous feedstock enters the plant and a liquid hydrocarbon-like

product is produced. The major inputs are heat for the thermochemical


conversions, separations and refining processes, chemicals and steam. The

emissions are flue gases and waste water. Table 6.7 presents the summary of the

major inputs and outputs of the three processes.

Table 6.7. Process model results for the three plants.

Stream Consumption/Production (per kg feed

dry basis)

Units

Gasification Liquefaction Pyrolysis

Thermochemical product 0.74 0.67 0.41 kg

Refined product 0.20 0.47 0.27 kg

Hydrogen N/A 0.03 0.02 kg

Steam N/A 1.92 N/A kg

Natural gas heating 0.03 0.006 N/A GJ

Electricity consumption 0.012 0.18 0.004 kWh

Combustion and drying air 39.8 6.54 37.8 kg

Chemical replacement 0.00009a 0.12b N/A kg

CO2e process emissions 2.89 4.40 1.63 kg

Wastewater 1.54 2.23 0.12 kg aAmine bEthanol

Among the three thermochemical processes, gasification had the highest

yield from the thermochemical conversion. This was expected due to the addition

of steam to the gasifier and with most of the mass exiting the gasifier as syngas.

There is only one by-product, solid char and ash produced at 0.09 kg/kg dry feed.

Liquefaction yield was less than gasification due to the products segmenting into

biocrude, gases and solids, which was the same case as pyrolysis; however, a

significant part of the ethanol solvent reacted with the biomass and augmented

the product mass in liquefaction. Pyrolysis also has a tendency to produce larger

amounts of light gaseous products due to the low pressure in which it operates.

On the other hand, production of liquid products are enhanced in liquefaction due

to high pressure [17].

Following upgrading or synthesis, liquefaction has the highest refined

product yield among the three, because the upgrading process involves the

addition of hydrogen into the biocrude. The refined HDO biocrude yield is lower

than raw biocrude due to the removal of oxygen and entrainment of light gaseous

products in HDO off-gas. Pyrolysis HT bio-oil yield follows liquefaction HDO

biocrude yield, similarly caused by hydrogen-addition and oxygen removal.

However, there was less raw bio-oil coming into the hydrotreatment process

compared with the raw biocrude fed into HDO, so it is expected that the HT bio-oil


yield is less than HDO biocrude. For gasification and FT, the FT liquid yield drops

from the syngas yield due to the 70% conversion of CO assumed in the model. The

converted products were also split into FT liquids, water and gas, thereby

diminishing the final product yield. Hydrogen consumption follows the upgraded

product trend: more hydrogen was required for upgrading biocrude due to the

higher flow rate. FT does not require addition of hydrogen so none was reported

for the gasification case. The addition of steam was unique for liquefaction, which

was used in solvent recovery. Gasification also had steam added in the

thermochemical process, however, it was modelled to be generated in-house

using recycled heat and natural gas.

Gasification had a much higher natural gas heating requirement brought

about by the high gasification temperature, which cannot be completely

maintained using heat from burning char. Another large heat draw was rich amine

stripping, which handles 83.3 t/h of rich amine (75% water). Liquefaction had a

relatively smaller natural gas requirement, which was offset by the heat supplied

by combustion of off-gases and char. Most of the liquefaction heating requirement

was to raise 111 t/h of the liquefaction slurry to the liquefaction temperature of

300 °C. Pyrolysis generated enough char to supply the heat required for both

pyrolysis and drying and thus, did not need additional heat from burning gas. The

electricity requirements are also lowest for pyrolysis, with only minimal make-up

electricity required for size reduction. The requirement for pumping bio-oil and

compressing hydrogen was supplied by in-house electricity produced from burning

char and off-gases and expanding HT products. Gasification had higher electricity

requirements from syngas compression prior to the FT reactor. Liquefaction

required much more electricity to pump liquefaction slurry at a high flow rate to

liquefaction pressure, with all the combustion energy directed to supplying heat

rather than producing electricity. In terms of total energy input (heat and

electricity) on a dry feed basis, liquefaction had the largest requirement.

The high feedstock moisture content required large amounts of drying air for

both gasification and pyrolysis, while liquefaction did not have the same needs.

Only combustion air was required for the liquefaction model and thus its air

requirement is only a sixth of gasification and pyrolysis. Gasification needed

slightly more than pyrolysis to supply a stoichiometric amount of air in combustion.


Chemical replacement rates for the three models are also vastly different.

Gasification was modelled to have 20% replacement of MDEA over one year of

operation. For liquefaction, it was assumed that 40% of the solvent reacts in

liquefaction and 1% of ethanol is lost in recycling so make-up ethanol is required.

The pyrolysis model does not include chemicals other than hydrogen, therefore

there was no chemical replacement required.

For both CO2 emissions and waste water, the order is

liquefaction>gasification>pyrolysis. The CO2 emissions calculated by ASPEN are

mostly from the use of natural gas and combustion of by-products. Most of the

waste water from liquefaction was from the solvent recovery section where some

steam added to the biocrude-ethanol mixture condensed, while for gasification,

the waste water was water condensed from syngas after gasification and tar

reforming. In pyrolysis, waste water was collected at product separation by

decanting water generated in HT.

6.3.2 Product Properties

Figure 6.1 presents the elemental composition of the main streams for the

three processes. The lines show the progression from feedstock to intermediate

product to refined product.


Figure 6.1. Van Krevelen diagram for the feedstock (orange dot), intermediate products (ring) and refined products (dot) of the processes in this study. Arrows show progression through

gasification (green), liquefaction (blue) and pyrolysis (purple).

It can be seen from Figure 6.1 how the elemental composition of the main

biomass/biofuel stream progresses through each process. In gasification, the O/C

and H/C ratio both increase from feedstock to syngas as some carbon is converted

to char and the added steam reacts with the intermediate chemicals to form CO,

CO2 and H2. In the Fischer-Tropsch reactor, carbon and hydrogen come together

to form hydrocarbons and oxygen and some hydrogen leaves as water. A drastic

reduction of both H/C and O/C can be observed. In liquefaction, the O/C ratio

decreases from feedstock to biocrude by forming water and carbon dioxide that

gets separated in the gaseous phase. After HDO, the H/C ratio is significantly

higher and the O/C ratio also decreases by the addition of hydrogen and removal

of oxygen as a by-product of the upgrading process. In pyrolysis, there is a slight

reduction of O/C from feedstock to bio-oil, with some of the oxygen going into

gaseous by-products. In hydrotreatment, hydrogen is added and some oxygen is

liberated as water, causing a decrease in O/C and an increase in H/C.

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.00 2.00 3.00 4.00 5.00 6.00

Mo

lar

O/C

Molar H/C

Bagasse Syngas Biocrude Pyrolysis bio-oil FT Product HDO HT Bio-oil

Liquefaction

Pyrolysis

Gasification


The refined products generated from these processes can be further

upgraded or separated to fractions analogous to their petroleum counterparts as

blendstock for diesel or gasoline products. For instance, decanting water from the

hydrocarbons generated in gasification (FT products) can bring the O/C ratio down

to nil and the H/C value to 2.15. The hydrocarbon composition also suggests a

product similar to gasoline. It may also be possible for the products to be co-

processed in refineries if the properties match. The refining processes in this study

follow recommendations to reduce oxygen content and approximate the

properties of petroleum crude oil to enable co-processing [76, 77]. From the

results of the modelling, it can be surmised that the oxygen contents of HDO

biocrude and HT bio-oil approach the very low oxygen content of crude oil [78].

This result is in agreement with numerous HDO and HT studies in literature [76,

77, 79]. Despite the good agreement in the oxygen content, it has been presented

that the chemical profile of HT bio-oils and HDO biocrudes are still different

compared with crude oils [80]. The different chemical moieties have implications

in energy and hydrogen requirements [81], thus the efficiency of the refinery

processes may be affected. Instead of blending the bio-based crudes with

hydroprocessing feed, it has been suggested to blend them with distillation feed

in order to generate fractions that may be more homogeneous in chemical

composition [82, 83]. The suitability of the products from these thermochemical

processes to be processed in distillation units can be determined by analysing

their distillation curves. Figure 6.2 presents the ASPEN-generated true boiling

point (TBP) distillation curves of the products compared with that of a typical crude

oil.


Figure 6.2. Distillation curves of the products of the thermochemical processes in this study,

compared with the distillation curve of crude oil obtained from [84].

The distillation curves of the FT products, HDO biocrude and HT bio-oil

appear to be in the same range albeit around 100 °C lower than the crude oil

curve. Among the bio-based products, HT bio-oil has the narrowest range of 312

°C, followed by HDO biocrude (397 °C) and FT products (402 °C). In contrast, the

crude oil in this graph has a range of 470 °C. It is also important to note that the

crude oil’s initial boiling point (IBP) is 50 °C, while both FT products and HDO

biocrude have sub-zero IBPs. Therefore, a degassing step to remove light

components can be useful for better integration. HT bio-oil on the other hand, has

a similar IBP with petroleum crude oil; however its final boiling point is 207 °C

below that of petroleum crude oil. The differences in boiling range of the bio-based

products with that of the petroleum crude oil here mean that the distillation

column processing this particular crude oil might not be appropriate for the bio-

based products. The distillation curves show that 100% of the bio-based crudes

distil at a temperature where only 50-70% of the petroleum crude oil has distilled.

For instance, if any of the bio-based products were blended with crude oil at a

significant blend ratio, it can be expected that there will be a significant increase

in the amount of lighter fractions obtained. Processes downstream of the

fractionator can be undersized to handle increased feed flow rates. It is typical

-100

0

100

200

300

400

500

600

0 20 40 60 80 100

Te

mpe

ratu

re (°C

)

% Distilled

FT Products HDO Biocrude HT Bio-oil Petroleum Crude


that refinery feedstock undergo light end removal [85] so the matching of the bio-

based products to the distillation properties of this particular petroleum crude oil

can be improved.

6.3.3 Economic Results

The results of the economic modelling are presented in Table 6.8.

Table 6.8. Economic results of the modelling of gasification, liquefaction and pyrolysis processes.

Quantity Gasification Liquefaction Pyrolysis

Plant capacity, t/y feed as received 84000 84000 84000

Capital cost estimates, million US$

Total Installed Cost 16.77 17.87 23.20

Location-adjusted Direct Cost 41.57 44.46 38.42

Total Indirect Costs 15.09 16.20 4.95

Working Capital 11.33 12.13 8.67

Total Capital Cost 67.99 72.79 52.05

Operating Costs, million US$/y

Feedstock Cost 2.04 2.04 2.04

Electricity 0.02 0.30 <0.01

Heating 6.27 1.01 -----

Ethanol or Amine Make-up 0.01 3.37 -----

Catalyst replacements 0.07 0.12 0.31

Hydrogen ----- 1.77 1.17

Steam Supply ----- 2.07 -----

Trade Waste Handling 0.05 0.06 <0.01

Water 0.94 1.46 0.60

Labour 1.81 1.17 1.17

Maintenance (2% FCI) 1.13 1.21 0.87

Total Operating Costs 12.35 14.57 6.16

Total Products, million L/y 11.50 25.78 11.58

Revenue, million US$/y 3.76 8.44 3.79

Base Economic Indicators

Annual Cash Flow, Million US$ -8.59 -6.13 -2.37

NPV, Million US$ -128.3 -113.7 -65.7

The economic results for the three plants were widely different from each

other. Liquefaction had the largest capital costs due to it having the highest total

purchased cost, from which the other direct costs are factored. Gasification

follows liquefaction closely and pyrolysis had the smallest total capital cost. The

costs calculated in this study were comparable to the capital costs calculated from

other studies, with adjustments based on scale and year of cost estimation.

The gasification plant in this study had a total capital cost of US$ 68 million,

which is similar to the scaled down, adjusted costs of US$ 71 million from the


plant described by Swanson et al. [24]. The differences between the plant

modelled by Swanson et al. and this plant was oxygen was added to gasification

via air separation, in addition to steam, FT products were hydroprocessed and

sulphur was recovered using a catalytic recovery system. The study by Swanson et

al. was also estimated on costs based in the United States of America. Another

study by Tijmensen et al. [86] in a Dutch context had a slightly lower capital cost

of US$ 61 million (scaled, 2017 US$), employing a gasification process with

oxygen added from an oxygen plant, slightly different gas cleaning equipment, and

a different gasification process taking in feedstock at 15% moisture, dried from

30%. The high capital costs are usually influenced by stringent syngas impurity

levels in the order of 10 ppb for Fischer-Tropsch synthesis [21].

The liquefaction plant, on the other hand, was estimated to cost US$ 73

million, comparable to the estimate by Zhu et al. [87] of US$ 77 million (scaled,

2017 US$). The main difference of the two plants is that the liquefaction plant in

this study involved extensive solvent recovery equipment, while that of Zhu et al.

had a hydrogen production plant.

The cost to build the pyrolysis plant was the least among the three plants,

since the separation of by-products and impurities is very little in pyrolysis

compared to the two processes. The estimated capital cost was US$ 52 million,

which is higher than a plant modelled by Anex et al. [35] in 2007 costing US$ 30

million (scaled, 2017 US$). An update to Anex et al. by Brown et al. [88] in 2011

adjusted the capital costs to US$ 61 million for almost the same plant, with the

significant differences around sizing of power generation and in how the costs

were estimated. The power generation section modelled by Brown et al. was

similar to the set-up in the pyrolysis plant in this study.

The breakdown of annual operating costs were also different from plant to

plant. As anticipated from the mass and energy balances, liquefaction had the

highest annual operating cost, while gasification was second. Pyrolysis had around

half of gasification’s total operating cost, due to the absence of heating costs.

Figure 6.3 shows the detailed breakdown of each process’ annual operating costs

and its comparison with annual revenue.


Figure 6.3. Operating cost breakdown of each plant, compared to annual revenue generated.

Revenue was calculated using a unit price calculated from the crude oil price.

It can be seen from Figure 6.3 that each plant has different operating cost

breakdowns. All plants have the same feedstock cost as a given for the study. For

pyrolysis, feedstock cost has the highest contribution to operating costs (33%).

This is common in similar studies [50, 59]. Moreover, feedstock cost has been

identified as a major factor in predicting production cost of bio-oil [15]. For both

gasification and liquefaction, feedstock costs were determined to contribute

around 15% to the total operating cost. The largest cost in gasification is heating,

making up more than half of the total cost. In liquefaction, the largest contributor

is ethanol replacement, bearing 23% of the total cost. Labour costs are

comparable, but the gasification plant required US$ 642,000 more in annual

labour costs due to the numerous columns in gas cleaning for which attention is

needed. The other costs correspond to the mass and energy balances discussed

in Section 3.2.

The revenue of the three plants were all based on the same crude oil unit

price. Each plant differs in annual product volume, and consequently, annual

revenue. From the revenue calculations, it was determined that liquefaction had

the highest revenue, followed by pyrolysis and closely behind, gasification.

However, since each plant produces different annual volumes of products, it may

be difficult to compare them based on annual values of revenue and operating

cost. For this reason, the unit margin/loss value as calculated using Eq. 6.1.

$0

$2

$4

$6

$8

$10

$12

$14

$16

Gasification

Op Costs

Gasification

Revenue

Liquefaction

Op Costs

Liquefaction

Revenue

Pyrolysis Op

Costs

Pyrolysis

Revenue

US

$ M

illio

ns

Revenue

Trade Waste

Maintenance (2%FCI)

Steam

Hydrogen

Catalyst Replacement

Water

Chemicals

Labour

Electricity

Heating

Feedstock cost


𝑈𝑛𝑖𝑡 𝑀𝑎𝑟𝑔𝑖𝑛 (𝑈𝑆$

𝐿) =

𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒−𝐴𝑛𝑛𝑢𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐶𝑜𝑠𝑡𝑠

𝐴𝑛𝑛𝑢𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑉𝑜𝑙𝑢𝑚𝑒 (Eq 6.1.)

The unit margin summarises the earnings or losses before depreciation and

tax, or annual cash flow of each plant per unit volume of fuel produced. Due to all

plants having a revenue that was less than the operating costs, the annual cash

flows, and consequently, margins were calculated to be negative. A smaller

negative value can be viewed as more favourable since there is a smaller hurdle

that needs to be overcome to achieve profitability. The unit margin for pyrolysis

was deemed the best result at US$ -0.21/L, while liquefaction was not far behind

at US$ -0.24/L. Due to a high operating cost and low production volume,

gasification achieved a margin of US$ -0.75/L, more than thrice of the other cases.

The NPVs also followed this trend, with gasification having the most negative NPV

of US$ -128.3 million, liquefaction following with US$ -113.7 million and pyrolysis

with US$ -65.7 million. The difference in NPV widened between the three plants

due to the effect of the large capital outlay required for the gasification and

liquefaction plants compared with the pyrolysis plant, and the significant

difference between the three plants’ annual cash flows.

6.3.4 Sensitivity analysis

The capital costs determined in this study can be considered a study

estimate where only major items were considered; therefore, it can be expected

that the estimates can be ±30% accurate [62]. Furthermore, installation and

indirect costs were estimated using ratio factors, which can be ±20% accurate. It

follows that the capital cost estimates can vary by 50% due to a number of factors

such as market fluctuations or unforeseen costs. From the sensitivity analysis, it

was determined that the NPV for each plant was most sensitive to the capital

costs. This presents a financial risk from this determination up to the plant’s

construction phase. Figure 6.4 shows the sensitivity of NPV of the three processes

to the capital costs.


Figure 6.4. The sensitivity of NPV to prospective changes in capital costs.

Among the three plants, pyrolysis was most sensitive to changing capital

costs. This is due to the large changes in cash flow for each case. When the capital

cost changes, the operating costs also change since the maintenance cost is

estimated as 2% of the capital cost. With these changes in operating cost and no

change in revenue, the before tax cash flow for pyrolysis changes by 18% when

the capital cost increases or decreases by 50%. For liquefaction the cash flow

change is 10%, and for gasification, 7%. While the capital changes at the same

rate, the cash flows change larger for pyrolysis, so the resulting NPV will bear a

larger change compared with those of gasification and liquefaction.

Upon operation, the profitability of the plant will change if parameters

fluctuate due to key processes operating below or above expected levels, or price

changes in the market. The sensitivity of the NPV of each process to these key

technical and economic parameters have been determined and is presented in a

tornado diagram shown in Figure 6.5.

-50%

-25%

0%

25%

50%

-50% -25% 0% 25% 50%

Ch

an

ge

in N

PV

fro

m B

ase

Va

lue

Change in Capital Costs from Base Value

Gasification Liquefaction Pyrolysis


Figure 6.5. Sensitivity of the NPV to selected process and economic variables (±50%). Green bars

represent gasification, blue bars represent liquefaction and purple bars represent pyrolysis

values. Change in NPV values were derived from the base case value for each process.

Figure 6.5 compares the effect of each parameter of the individual process

to its NPV and across the three processes. For all processes, the product price is

a significant parameter, although it affected the NPV of liquefaction the most. This

is due to the larger volume of product from the liquefaction process compared

with gasification and pyrolysis. It is expected that the product price will change

according to market factors, although it could be possible that incentives and

tariffs to support biofuel production can augment revenues and make the venture

profitable.

Thermochemical conversion was also a major parameter for liquefaction and

pyrolysis. This is expected since the amount of biocrude and bio-oil dictate the

amount of refined product produced. An opposite but much smaller effect

happens to gasification due to the increase in operational costs involved in

cleaning higher flows of syngas, and the increase in revenue is dampened by the

Fischer-Tropsch conversion with a value less than unity. With any production

process, improvements are focused largely on obtaining greater yields from the

0.164

0.37

0.34

0.21

2.31

35

50

50

1.3

0.32

23.4

0.78

0.491

1.11

1

0.62

6.94

100

100

100

3.9

0.96

70.2

2.34

-40 -30 -20 -10 0 10 20 30 40

Product Price

(US$/L)

Thermochemical

Conversion

(kg/kg dry feed)

Natural Gas Price

(US$/GJ)

Refining Conversion

(%)

Chemical Price

(US$/kg)

Feedstock Price

(US$/dry ton)

Hydrogen Price

(US$/kg)

Change in NPV (Million US$)

Gasification

Liquefaction

Pyrolysis


same amount of raw material, however, as seen in gasification, there should also

be a focus on increasing the cost efficiency of gas cleaning processes to maximise

the positive effect of higher yields to NPV. Another cost reduction prospect is to

develop synthesis processes and catalysts that require less stringent gas cleaning.

As expected, the natural gas price affects gasification immensely, due to the

large requirement for heating, while pyrolysis was not at all sensitive due to its

self-sustaining design. The effect on liquefaction NPV by natural gas price was

moderate. The development of more energy efficient processes can ameliorate

both the costs of heating and the fossil fuel-related CO2 emissions. Furthermore,

the use of by-product streams for heat and power production can be considered.

Refining conversion affected gasification significantly as a change in FT

conversion proportionally affected the volume of product. The effect to both

liquefaction and pyrolysis were different, primarily due to the base case modelled

at 100% conversion, making the base value the concurrent with the “high case”

in the sensitivity analysis. The incomplete conversion had different effects too.

Upon comparison of boiling point distributions, it was determined that for

liquefaction, 5% of the unreacted biocrude is beyond the boiling point range of the

HDO biocrude product, while for pyrolysis it was 26%. These values allowed for an

estimation of the “off-spec” amount in the product, and using this number to

determine the value-reduction factor for the sensitivity cases, with which the base

case revenue was multiplied. For liquefaction, the revenue was reduced by 96%

for 50% HDO conversion, while for pyrolysis it was 87% reduction for 50% HT

conversion. These values can be seen affecting NPV proportionately. The focus on

improving conversion values are both on product yields and quality of products.

This encompasses many different considerations such as catalyst activity, reactor

design, fouling and deactivation of catalysts and process effectiveness relating to

deoxygenation, cracking and formation of by-products. In all three processes, it

was determined that catalyst costs were not critical, however, for a larger plant

with larger catalyst beds, the cost of new catalysts with superior performance can

affect profitability. The cost of more effective catalysts should be balanced with

the effect of resulting higher conversion ratios. An improvement in quality of the

refined product can also affect product price, which has been demonstrated to be

a very significant parameter in profitability.


The effect of the prices of inputs can be seen at the bottom of the diagram.

The price of ethanol affects liquefaction NPV due to the significant amount of

annual replacements, even with an assumed process loss of 1%. This presents an

opportunity for cost savings by focusing process improvements on ethanol

recovery, or maintaining a supply of stably-priced ethanol by hedging contracts, or

in-house supply from an integrated liquefaction-cellulosic bioethanol plant. In

contrast, the amine price affects gasification NPV very slightly. The variation of the

feedstock price within 50% of the base case price generates a consistent change

of US$ 7.90 million across the three processes. This was interesting considering

the different cost contributions of feedstock between these processes. The

variation in feedstock cost could be managed by stockpiling to reduce the effect

of the seasonality of sugarcane supply. Hydrogen is another key input for

liquefaction and pyrolysis, although the variation in price does not affect NPV as

much as other raw materials. Nonetheless, its effect on the NPV was higher than

HT or HDO conversion. From the process side, the effect of the varying hydrogen

price can be managed by process improvements related to hydrogen uptake. In

the models used in this study, the hydrogen requirements in HDO and HT were

determined by stoichiometric quantities required to carry out the reactions. The

hydrogen requirement may increase if catalysts were developed to increase the

deoxygenation of the biocrude or bio-oil components, and may cause the NPV to

be more sensitive to the hydrogen price. There can also be improvements in the

thermochemical process where the biocrude or bio-oil generated will have lower

oxygen content and will require less hydrogen in refining. This can reduce the

effect of the hydrogen price. Furthermore, a hydrogen plant can be integrated with

the liquefaction or pyrolysis plant; however, the effect of an increased capital cost

vis-à-vis decreased operational cost should be further analysed.

6.3.5 Minimum selling price analysis

The minimum selling price was calculated as a measure of product value

based on costs and revenues. This allows a parametric prediction of the product

price and enables development of profitable and non-profitable operating regimes

based on key process and economic parameters and product price. The base case

MSP of the three processes are the following: gasification, US$ 1.94/L,

liquefaction, US$ 0.98/L, and pyrolysis, US$ 1.19/L. The following figures show


the variation of the MSP with varying parameters. The area from the MSP lines

going up is the profitable operating region, and conversely, the area below the line

for the specific process is the non-profitable operating region.

Figure 6.6. Effect of varying thermochemical conversion (circles, solid lines) and refining

conversion (triangles, dashed lines) to the minimum selling price of the product.

The thermochemical and refining conversions are palpably critical

parameters in these three plants’ operation. For gasification, there is a sharper

change to lower prices between the low values (-50% and -10%) and base case

values than the high values (+10% and +50%), although thermochemical

conversion has a slightly lower MSP with a 50% reduction compared with FT

conversion. Pyrolysis conversion also has a similar effect on MSP, although the

MSP values are lower. Lower HT conversion values increase the price, but not as

much as the pyrolysis conversion. The small product volumes contribute to a sharp

increase when conversions are lower, with higher prices required to make up the

revenue needed to achieve profitability. The effect of a lower HT conversion,

however, is dampened because a 50% reduction in conversion corresponds to

only 13% reduction in volume. For liquefaction, the MSP does not move greatly

due to a larger product volume.

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

$4.00

$4.50

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

MS

P (U

S$

/L)

Change in conversion (%)

Gasification Liquefaction PyrolysisGasification (FT) Liquefaction (HDO) Pyrolysis (HT)Gasification High Values

Liquefaction

Pyrolysis

Gasification

Base case


Figure 6.7. Effect of varying feedstock price (triangles, solid lines), natural gas price (circles) and

hydrogen price (diamonds, dashed lines) to the minimum selling price of the product.

The effect of the input prices are shown in Figure 6.7. Gasification product

MSP had the largest variations among the three processes. This is ostensibly due

to the smaller volume of product. Natural gas price greatly affects the MSP since

the process is dependent on a large amount of heating. The feedstock price, as

seen in the NPV sensitivity analysis, moderately affects the MSP; however, a

slightly larger effect is shown for gasification and pyrolysis, compared with

liquefaction, which is again influenced by the product volumes. The pyrolysis MSP

values change between US$ 1.10/L to US$ 1.28/L, which is moderate, compared

with gasification, which swings between US$ 1.67/L and US$ 2.21/L. At lower

values, hydrogen price drives a higher MSP compared with feedstock price, while

the effect flips at higher values. Feedstock cost, which comprises a third of the

operating cost of pyrolysis is expected to vary the MSP more, compared with

hydrogen costs. On the other hand, liquefaction MSP lines are comparably docile

with the other processes, due to the diminished contribution of feedstock, natural

gas and hydrogen prices and the relatively larger product volume.

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

MS

P (U

S$

/L)

Feedstock, Natural Gas or Hydrogen Price change (%)

Gasif Feedstock Liq Feedstock Pyr Feedstock Gasif Natural Gas

Liq Natural Gas Liq Hydrogen Pyr Hydrogen

Base case

Liquefaction

Pyrolysis

Gasification


Figure 6.8. The effect of varying tax rate (solid line) and capital costs (dashed line) to the minimum selling price for gasification (green), liquefaction (blue) and pyrolysis (purple). Zero

percent in capital costs refers to the base case cost. Base case tax rate is 30%.

Figure 6.8 shows the effect of different corporate tax rates and variations in

capital costs to the minimum selling price. This provides some insight on potential

policies on taxation and incentives for thermochemical biofuel plants. This

approach, compared with NPV analysis, can be useful since the MSP is more

relevant to the public, while the NPV is more relevant to companies and investors.

The corporate tax rates were applied only to income, therefore the effect on

profitability was in the final annual cash flows. Capital costs, on the other hand,

are applied as a single negative cash flow at the start of the plant life. Moreover,

it affects operational expenses through the maintenance costs, which

consequently affect future cash flows.

The three processes exhibit the same trend for both tax rate and capital cost.

Higher values cause higher MSP values. Compared with the base case tax rate,

the change in tax rate influenced the MSP very slightly. A reduction from 30% to

0% tax rate changed the MSP by only 8-11%, while an increase to 50% increased

the MSP by 11-15%. Considering that tax rates primarily affect company profits,

changing the amount of profit does not translate to equitable cost savings for

consumers, although it can be argued that lower tax rates ease the burden of

unfavourable cash flows in the early years of the plant’s life, and higher tax rates

compensate for externalities that production might incur. The varying capital costs

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

0% 10% 20% 30% 40% 50%

MS

P (

US

$/L

)

Tax Rate or Change in Capital Costs (%)

Liquefaction

Pyrolysis

Gasification


show similar trends with changing tax rates. Therefore, for the three plants in this

study, capital-related incentives or tax holidays could have the same effect to the

MSP. Of course, if both measures were applied, the MSP can further decrease,

making the biofuel products more competitive and accessible to consumers.

6.4 Conclusions

This study presented a comparison of three thermochemical process plants

that can potentially produce biofuels from sugarcane bagasse. This serves as a

case study for biofuel production plants that use similar lignocellulosic feedstock

in thermochemical pathways. The technical and economic models presented key

production and profitability aspects of the plant such as thermochemical

conversion, refining conversion, product price, feedstock price, natural gas price

and hydrogen price. Furthermore, the relationship of these factors to economic

indicators such as operational expenses, revenue, net present value and product

minimum selling price were analysed. The use of current technology in the process

models returns unfavourable values of net present value, making the minimum

selling price higher than the current market price. Moreover, the models highlight

opportunity areas to improve cost efficiency for each plant. Heating costs affect

gasification greatly, while ethanol costs make up a large portion of liquefaction

operating expenses.

The profitability of the three plants were sensitive to product price,

thermochemical conversion and refining conversion, while input prices affect the

NPV moderately. The process parameters influence the minimum selling price the

most, with low production volumes intensifying MSP increases. Tax rates and

capital costs influence product price but not to the extent conversion ratios do.

Therefore, it could be advantageous to focus policies on incentives towards

improving process conversion ratios, increasing thermal efficiency and reducing

process losses. This might be in form of grants or incentives for producers that

enable them to reinvest in research to improve production or develop ways to

increase efficiency.

6.5 Conflicts of Interest

There are no conflicts to declare.


6.6 Acknowledgements


Australian Government.


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Chapter 7: Conclusions

In 1944, Berl predicted that in only fourteen years, the supply of cheap oil in

the United States will all be consumed and that “it is imperative that ways and

means should be used in order to allow a continuous production of liquid fuel after

the exhaustion of that oil underground which can be recovered at relatively small

cost” [1]. Over the decades, the United States, and other oil producing countries

have learned to maximise their petroleum resources amidst technical and

geopolitical hindrances. This resulted to increases in proven oil reserves, oil

production and consumption [2]. As technology progressed in the modern world,

the dependence on reliable energy has become greater. In time, the effects of

fossil fuel use in increasing greenhouse gas (GHG) concentration in the

atmosphere and consequently, global temperature have been identified as a

threat [3]. Brundtland, in a landmark sustainability report, compelled countries to

develop programmes on sustainable renewable energy [4], which meant that

providing reliable supply should not be the only consideration in new energy

development, but also efficiency in resource use, public health and environmental

protection.

In this regard, science has responded to the global challenge of sustainable

energy. For instance, technologies have been developed to produce biofuels, with

the aim of decreasing the carbon intensity of transportation fuels. The first

generation of biofuels have met some sustainability targets in GHG emissions and

suitability for use in internal combustion engines [5, 6], however, concerns around

land use and food supply [7] drive the development of advanced biofuels using

non-food feedstock.

Among the variety of biofuel production technologies, liquefaction was

chosen for this research due to its versatility in converting biomass with high

moisture into liquids. The nature of the liquefaction process allows the conversion

of biomass with complex biochemical compositions, biomass slurries, waste

biomass, sewage sludge, and garbage, which can reduce disposal costs and

create more value from waste streams. The current body of literature in

liquefaction technology is largely in lab-scale explorations of various forms and


compositions of feedstock, parametric tests in a range of autoclave and batch

reactor sizes, and product analysis. These studies are usually not demonstrative

of technical or economic viability of the process for biofuel production. Research

on upgrading, or production of biocrudes compatible with existing engines, are

disproportionately scarce. Similarly, only a small number of techno-economic

studies were conducted to present the value of the liquefaction process in biofuel

production. However, the plethora of knowledge gained in lab-scale experiments

needs to be translated into techno-economic models so the value of the results of

these experiments can be realised.

This PhD research explored the commericalisation of liquefaction as a

biofuel production process. Upgrading processes were considered as essential

components of a liquefaction plant to produce biofuels. Potential upgrading

technologies were discussed and their utility was analysed based on their ability

to process biocrude into fuel with desirable properties (i.e. Objective 1). This led

to an exploration of blending biocrudes with petroleum crudes for co-processing

and modelling the blending process in ASPEN Plus. The ability of software to model

biocrude with distillation curves allowed a more accurate representation of

biocrude and could facilitate further modelling of upgrading processes (i.e.

Objective 2). A liquefaction plant using waste biomass, sugarcane bagasse, was

then modelled using ASPEN Plus. The use of ethanol, a renewable solvent, was

also modelled in the liquefaction process due to its success in improving biocrude

yield and quality in literature. Process modelling results were compared with

literature values and mass and energy balances were used as inputs to the

economic model. Economic indicators were calculated and the sensitivity of the

net present value and minimum selling price of the product fuels to changing

process and market parameters were analysed. Similar process and economic

models for pyrolysis and gasification were also developed for converting

sugarcane bagasse to liquid fuels, to put the analysis of the liquefaction process

in the right context (i.e. Objective 3). Modelling of the three processes in

Queensland, Australia also aimed to demonstrate the viability of the

thermochemical processes in biorefinery scenarios aligned with the state’s

biofutures roadmap [8].


7.1 Conclusions and significance of the results

The link between the properties of biocrude and fuel properties as they relate

to engine performance and emissions has been established in Chapter 3.

Furthermore, an understanding of the properties of biocrude enabled a discussion

of prospective upgrading processes with desirable fuel properties in mind.

Biocrude upgrading research can also take its cue from pyrolysis bio-oil upgrading

research with regard to methodologies and analyses, while taking into

consideration the differences between the two substances. Among the processes

discussed, petroleum refining analogous processes were particularly interesting

for biocrude for potential co-processing in conventional petroleum refineries.

This co-processing approach was examined in Chapter 4. In particular, the

potential to blend biocrudes with petroleum crude oil was investigated through

modelling and verification. Biocrudes, petroleum crudes and their blends were

characterised using an established simulated distillation method used to measure

petroleum assay properties. The resulting distillation curves of pure biocrude and

petroleum crude were used to model blending in ASPEN Plus. Blends were

prepared with different blending ratios and in different blending temperatures.

Fourier transform infrared (FTIR) spectroscopy was used to determine miscibility

of the blends. It was established that due to the difference in polarity, biocrude

and petroleum crude did not readily mix. Blending in higher than ambient

temperatures and vigorous mixing enabled a more homogeneous mixture, which

was demonstrated in a combined FTIR spectra. The blends in different ratios

generated different distillation curves, which were matched with the resulting

distillation curves from the ASPEN model. This purports the utility of ASPEN in

modelling biocrudes, and thus liquefaction and upgrading processes. The

simulated distillation of biocrudes and biocrude blends with petroleum crude also

demonstrated the co-processing potential of biocrude.

The modelling work in Chapters 5 and 6 provided the tools to analyse

profitability of a sugarcane bagasse liquefaction plant for biofuel production. In

previous models, wood is usually the biomass feedstock and water is the

liquefaction solvent. The model in this research was built around the use of a

waste biomass material, sugarcane bagasse and a renewable organic solvent,

ethanol. The choice to use bagasse in the model allowed an examination of the


use of biomass feedstock that may have been perceived to have lower value than

wood, and could thus carry a lower price. It was also of great interest since

bagasse has been determined as a viable feedstock in terms of supply in

Queensland, Australia and the resulting quality of biocrude produced. It was

determined that biofuels can be produced through liquefaction and subsequent

hydrodeoxygenation (HDO) with a high operating cost. This was mostly due to the

cost of making up for ethanol losses, heating and hydrogen costs. The minimum

selling price for a profitable plant was determined to be US$ 0.99/L. The

profitability of the plant was most sensitive to product price, HDO conversion

efficiency and plant capacity. Moreover, HDO conversion, along with biocrude yield

affect the minimum selling price the most. These analyses inform the risk

mitigation strategies required to ensure financial stability. Since product price is

likely to be market driven, maintaining high and stable biocrude yield and HDO

conversion is key to sustainable profitability. Opportunities such as more efficient

material and energy efficiencies and lower solvent-biomass ratios were

recommended.

Similar models of thermochemical plants using pyrolysis or gasification as

the central process were developed to put the liquefaction model in the proper

context. This allowed a lateral comparison of the three plants, with liquefaction as

the relatively novel process, and pyrolysis and gasification as more mature

processes. These models included pre-treatment, thermochemical conversion,

separations, refining/upgrading and product separation to produce a crude oil-like

product. In terms of overall biofuel yield the processes were ordered as follows:

liquefaction>pyrolysis>gasification. In terms of heating and electrical energy input

the order is liquefaction>gasification>pyrolysis. The ranking based on CO 2

emissions is liquefaction>gasification>pyrolysis. The mass and energy flows were

used in the economic model to calculate net present value and minimum selling

price. The ranking based on capital cost was pyrolysis>gasification>liquefaction,

while the based on NPV the ranking was pyrolysis>liquefaction>gasification. The

minimum selling price for the product of the different processes are, US$ 1.94/L

from gasification, US$ 0.98/L from liquefaction, and US$ 1.19/L from pyrolysis.

The larger volume of product from the liquefaction plant helped attenuate the

effects of large capital costs, however liquefaction was also most sensitive to the

changing product price. Pyrolysis was the most sensitive to variations in capital


cost from estimation uncertainties. Gasification was most sensitive to natural gas

price due to the large heating requirement, while liquefaction and pyrolysis were

both sensitive to biocrude/bio-oil yields. All three processes were similarly

sensitive to feedstock price, however, gasification was more sensitive to Fischer-

Tropsch conversion rate. The minimum selling price of the products were most

sensitive to thermochemical and refining yield, and only slightly sensitive to input

prices, tax rate and capital cost variations. This poses that early investments from

the public and private sector should focus on research around maximising process

yields to achieve sustainable profitability. Thermal liquefaction was determined to

be a technically and economically viable biofuel production technology.

Overall, the models will help accelerate the development of lignocellulosic

biofuels in Queensland, Australia, but are also easily adaptable to other states and

locations. The process models can also be modified with minimal effort to reflect

new data of improved yields and efficiencies as technology progresses. The

research also presents new approaches to liquefaction research as follows:

1. Representing liquefaction biocrudes as distillation curves in ASPEN Plus

facilitates mass and energy balances of distillation and upgrading

processes. The use of distillation curves provides a uniform way to

characterise biocrudes without extensively quantifying hundreds of

compounds in biocrude using overly complicated sample preparation

and analysis methods.

2. Using process models to properly compare liquefaction with other biofuel

production pathways, using material and energy flows to quantify

differences.

3. Enable expedient techno-economic evaluation of process improvements

in the liquefaction process to determine the value of the improvement

while accounting for incremental cost and impact.

7.2 Recommendations for future research

The direction for future research in liquefaction was concluded in the techno-

economic studies. This research recommends further research in improving

biocrude yields and upgrading conversions. More efficient use of energy and

solvents should also be incorporated in future work. The use of catalysts,


hydrogen-donor solvents and reducing gas in liquefaction are emerging in lab-

scale liquefaction literature. These can be included in techno-economic models to

adequately assess the cost efficiency of these measures. From the technical

models, life cycle analyses for liquefaction plants can be developed to enable

more accurate comparisons of the environmental impacts among biofuel

production technologies. The liquefaction model can also be attached to existing

conventional refinery models to analyse the viability of co-processing biocrudes

with crude oils.

The distillation curves of biocrude and biocrude-petroleum crude blends

generated using ASPEN Plus can also be further verified in distillation equipment.

While the simulated distillation data was determined to be adequate, the analysis

of fractions generated in the distillation of biocrude and biocrude-petroleum crude

blends will be interesting for comparison with petroleum distillation fractions to

determine compatibility for co-processing. Moreover, establishing a standard

distillation curve measurement procedure for biocrude can enable comparison of

distillation properties of biocrudes produced with varying feedstock and

conditions. The effect of blending different kinds of biocrude to the distillation

properties of the resulting mixture can also be explored to determine if a

distributed supply of biocrude can be consolidated in central upgrading facilities.

These all add to the verification of ASPEN models and continuously improve the

quality of techno-economic analysis.

Modelling the liquefaction in ASPEN Plus in this research is an initial step in

maximising the capabilities of modelling software in this field. Establishing

distillation curves for upgraded biocrude can enhance the models by using real

biocrude models rather than a composite chemical mixture. Incorporating pilot-

scale data from continuous reactors can validate the overall mass and energy

balance of the process model, including solvent recovery and recycling. Other

plant configurations and supply scenarios can be developed and modelled in

ASPEN using the liquefaction model from this PhD. The models can also be varied

to simulate other separation or upgrading technologies that may emerge in the

future. Minor modifications in can be made to simulate the production of

chemicals instead of fuel, and techno-economic analyses for a variety of chemical

production cases can be developed.


Stochastic models can also be attached to the techno-economic model to

generate probabilistic outcomes. This analysis factors in the uncertainties in

estimation and provides a more realistic range of values of technical and

economic indicators. This information is helpful in analysing investment risk, and

environment and energy policy. The realistic outcomes can make techno-

economic models have more meaningful results, especially when comparing

technological advances or policy incentives that can potentially make liquefaction

technology more viable.


7.3 References

1. Berl, E., Production of oil from plant material. Science, 1944. 99(2573):

p. 309-312.


London, UK.

3. Schneider, S.H., The greenhouse effect- Science and policy. Science,

1989. 243(4892): p. 771-781.

4. Brundtland, G.H., Our Common Future—Call for Action. Environmental

Conservation, 2009. 14(4): p. 291-294.

5. Hossain, A.K. and P.A. Davies, Plant oils as fuels for compression ignition

engines: A technical review and life-cycle analysis. Renewable Energy,

2010. 35(1): p. 1-13.

6. Börjesson, P., Good or bad bioethanol from a greenhouse gas perspective

– What determines this? Applied Energy, 2009. 86(5): p. 589-594.

7. Escobar, J.C., et al., Biofuels: Environment, technology and food security.

Renewable and Sustainable Energy Reviews, 2009. 13(6): p. 1275-1287.

8. Queensland Biofutures 10-Year Roadmap and Action Plan. 2016,

Department of State Development: Queensland, Australia.


Appendices

Appendix A: Gas Chromatography Traces

Figure A1. GC Trace of BB biocrude.

Figure A2. GC Trace of PCRU

Figure A3. GC Trace of BIOBL-COOL blend

0

10

20

30

40

50

0 1 2 3 4 5

Ab

so

lute

Inte

nsity

Millio

ns

Retention Time, min

0

50

100

150

200

0 1 2 3 4 5 6 7 8

Ab

so

lute

Inte

nsity

Mill

ion

s

Retention Time, min

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

0 1 2 3 4 5 6 7 8

Ab

so

lute

Inte

nsity

Mill

ion

s

Retention Time, min


Appendix B: Process Flow Diagrams of the Plants


Fig

ure

B1

: Liq

ue

factio

n P

lan

t d

escri

be

d in

Ch

ap

ter

5.


Fig

ure

B2

: Liq

ue

factio

n P

lan

t d

escri

be

d in

Ch

ap

ter

6.


Fig

ure

B3

: G

asif

ica

tio

n P

lan

t d

escri

be

d in

Ch

ap

ter

6.


Fig

ure

B4

: P

yro

lysis

Pla

nt

de

scri

be

d in

Ch

ap

ter

6.


Appendix C: List of compounds used in the liquefaction model Table C1: Compounds used in the modeling liquefaction biocrude and HDO biocrude streams.

Biocrude compounds HDO Biocrude compounds

1-hexane, 4,5-dimethyl- 3-methylpentane

3-buten-2-yl-acetate cyclohexane

2-pentanol, 2-methyl- 3-methylhexane

benzene, chloro- ethyl cyclohexane

butanoic acid, 2 methyl -, ethyl ester heptane

ethylbenzene butane

butanoic acid, propyl ester propane

1,3-dioxolane, 2-methyl-2-propyl- propylene glycol

propanoic acid, 2-hydroxy-, ethyl ester 2,2,4,4-methylpentane

acetic acid, hydroxyl-, ethyl ester 2,6-dimethylheptane

β-propiolactone pentane

2,2,4,4-tetramethyl-3-pentanol nonane

isobutyl 3-methylbutanoate 1,5-pentanediol

2-furanmethanol propyl cyclohexane

2,2’-methylenedifuran 1,2,3-methylcyclopentane

2-furanmethanol, tetrahydro- 1,4-butanediol

benzofuran, 2,3-dihydro- anisole

2-methyl benzofuran ethyl cyclohexanol

2,3,4-trimethyl-2-cyclopentanone ethyl anisole

butyrolactone cyclohexanol

phenol, 2-methoxy- decahydroazulene

pentanoic acid, 4-oxo-, ethyl ester propyl cyclohexanol

phenol,4-ethyl- cyclopropyl cyclohexane

2-ethylcaproic acid ethyl-m-anisate

diethyl squarate octyl cyclohexanol

trimethyl methanetricarboxylate 1,3-di-tert-butylcyclohexane

phenol, 5-ethyl-2-methoxy- heptadecane

phenol,3-ethoxy- mannitol

azulene

(2,2,3-trimethyl cyclopentyl) acetic acid

phenol,2,6-dimethoxy-

phenol, 2-methoxy-4-(1-propenyl)-

3-oxabicyclo [3.2.1] octan-2-one, 5,8,8-

trimethyl-

2-phenylcyclopropane carboxylic acid

methyl-4-hydroxycinnamate

p-(1-methylheptyl) phenol

3,5-di-t-butylcatechol

hexadecanoic acid, ethyl ester

ethyl α-d-riboside

d-mannose

cellobiose

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