h2020 lce-11-2015 developing next sustainable alternative ... · waste2fuels co deliverable 7.4...

Waste2Fuels CO Deliverable 7.4

H2020 – LCE-11-2015 – Developing next

generation technologies for biofuels and

sustainable alternative fuels

Title: Sustainable production of next generation biofuels from waste streams

Acronym: Waste2Fuels

Grant Agreement No: 654623

Deliverable 7.4 Sustainable life cycle assessment final report.

Associated WP WP7 Process Finger Print

Associated Tasks T7.4 Sustainable Life Cycle Assessment of next generation

biobutanol from AFW

Due Date 31/10/2018

Date Delivered 18/01/2019 (new version resubmitted 04/02/2019)

Prepared by (Lead

Partner) UCL

Partners involved UCL

Authors Roberto Chirone, Paola Lettieri

Dissemination Level Confidential (CO)

Ref. Ares(2019)617476 - 04/02/2019


© European Communities, 2019. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the European Communities. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.

This project has received funding from the European Union’s Horizon 2020 research and

innovation programme under grant agreement No 654623.


Table of Contents

1. Executive Summary ............................................................................................................. 1

2. Introduction ......................................................................................................................... 1

3. Life cycle assessment ........................................................................................................... 2

3.1. Reference model ............................................................................................................. 2

3.2. Life cycle inventory ........................................................................................................ 5

3.3. Life cycle assessment ..................................................................................................... 7

3.4. Life cycle impact assessment ......................................................................................... 9

3.4.1. Bio-fuels from Algae ............................................................................................ 13

3.4.2. Microalgal bio-diesel energy demand vs Agro-food ............................................ 13

4. Life Cycle Cost Analysis ................................................................................................... 19

5. Social Life Cycle Analysis ................................................................................................. 21

6. General results and conclusions ....................................................................................... 22

7. References........................................................................................................................... 24

Appendix A ............................................................................................................................. 25


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1. Executive Summary The deliverable 7.4 reports the final findings regarding the environmental assessment and life

cycle cost analysis of bio-butanol production route from agro-food wastes. This combines the

primary data for the pre-treatment, fermentation and final product purification by distillation

according with the technologies developed within the W2Fs project and secondary data taken

from similar process for the enzyme production, lignin separation, wastewater treatment and

boiler/combustion. Six different scenarios were simulated. In particular, three agro-food

wastes were analyzed as feedstock and two different patterns of separation were considered.

Together with the environmental burdens, also a life cost analysis and preliminary

consideration related to social life cycle analysis will be presented.

The report includes a summary of the previous report and describe the integrated

methodology for the three frameworks. Then, results and general guidelines will be drawn

2. Introduction Bio-butanol route production from biomass and in particular from second generation of

biomass has gained visibility in recent years as a replacement for gasoline. Butanol is a very

promising biofuel due to its superior fuel properties, its lower water solubility and its adoption

as engines require almost no modifications to use it compared to current main biofuels,

bioethanol and biodiesel. However, W2Fs consortium also wants to quantify its performance

in terms of environmental burdens. Following the same approach used for the scale-up of the

experimental processes, Deliverable 7.1 reported the first level of the environmental analysis

according to the results reported in Deliverable 6.1. In particular, in D7.1 the model developed

for the life cycle analysis of butanol production plant from corn stover (as feedstock) together

with some preliminary results is reported. The current deliverable D7.4 expands and extends

the model previously developed. In particular, Life cycle inventory of pre-treatment,

fermentation and separation is based on process modelling outputs from AspenPlus and

reported in deliverable 6.2. D6.2 reports a detailed description of the pre-treatment,

enzymatic hydrolysis, fermentation and separation steps. They are extended in terms of

process steps following the pre-treatment protocols reported from WP1, the enzymatic

hydrolysis and fermentation results of WP3. Furthermore, the number of reactions under

consideration are increased to represent the experimental results for selected feedstock

options and a grey-box modelling was used for steady-state process simulation of ABE

fermentation using stoichiometric reactors in Aspen Plus®. The model allows representation

of the reactor cascade proposed by UNINA and enables the placement of a suitable in-situ

recovery technology in the connecting streams or by-pass streams of reactors. Regarding the

in-situ recovery of butanol multiple options exist like gas-stripping, pervaporation, adsorption

or liquid-liquid extraction. The goal of these in-situ processes is the reduction of the butanol

concentration during the fermentation step. The best overall option is then coupled with

fermentation and pre-treatment for simulation of the overall process. Model parameters are

extracted from experimental data summarized in deliverable D3.2. For the separation step, a


2

classic ABE distillation sequence was modelled based on literature data. More details can be

found in D6.2.

Furthermore, the results obtained for the Life Cycle Costing (D7.2) and the results for Social

Life Cycle Assessment (D7.3), will be summarized and integrated with environmental

performance in order to draw general comments and guidelines for the future development

of Butanol for fuel transportation.

3. Life cycle assessment

3.1. Reference model

The environmental performance of the bio-butanol production from agro-food wastes is

analyzed using the LCA methodology. The LCA analysis is performed in order to have a clear

vision of the environmental and energetic burdens with a consequently opportunity to use

the results for a green design.

The study is an attributional study and the target audiences are: W2Fs partners (mainly

technical personnel, such R&D personnel) and European Commission (mainly technical

people).

Figure 1 – Scheme of the System Boundary.

The LCA study follows a “cradle-to-gate” approach where the functional unit considered is 1

kg of butanol produced. Figure 1 shows the system boundary considered for this study. It

includes all raw materials and butanol production excluding the construction, distribution and

final use of the butanol. In particular, two different scenarios are considered for each biomass

used:

• Scenario A: apple pomace is used as feedstock and the separation unit is designed to

obtain Butanol, acetone and ethanol;

• Scenario B: apple pomace is used as feedstock and the separation unit is designed to

obtain only Butanol whereas acetone and ethanol are considered as waste stream;


3

• Scenario C: potato peel is used as feedstock and the separation unit is designed to

obtain Butanol, acetone and ethanol;

• Scenario D: potato peel is used as feedstock and the separation unit is designed to

obtain only Butanol whereas acetone and ethanol are considered as waste streams;

• Scenario E: brewer spent grain is used as feedstock and the separation unit is designed

to obtain Butanol, acetone and ethanol;

• Scenario F: brewer spent grain is used as feedstock and the separation unit is designed

to obtain only Butanol whereas acetone and ethanol are considered as waste stream.

The product displacement method is used for the by-products generated in the butanol

production process. It is based on the concept of displacing each existing product with the

corresponding new by-product obtained. In particular, 1 kg of acetone produced from the n-

butanol process was assumed to displace 1 kg of acetone from petroleum-based production.

Similarly, 1 kg of ethanol from the n-butanol process displaced 1 kg of corn ethanol and the

excess electricity co-product displaced an equivalent amount of grid electricity, thus avoiding

a significant amount of GHG emissions, assuming an average EU electricity grid mixture. The

system also considered the credits related to the surplus of thermal energy generated from

the combustion of the lignin. Furthermore, it must be highlighted that within the system

boundary it is also considered the environmental burdens, related to the fact that the agro-

food wastes do not go to incineration process (nowadays the 80% of these wastes goes in

landfill or incineration).

The butanol production plant considers 6 units interacting among each other. The 6 units can

be divided in core units and utility units. The core units are:

1) Pre-treatment unit;

2) Fermentation with in-situ butanol removal with several adsorption column;

3) Separation unit.

Whereas the utility units are:

1) Enzyme in-situ production;

2) Wastewater treatment with lignin separation and recovery;

3) Boiler and combustion which is used for the valorisation of the energy content of the

lignin.

The model developed accounts for the innovations and experimental results obtained during

this project. The model focuses on the core units and, in particular, it starts from the pre-

treatment process for the feedstock, apple pomace, brewer's spent grain and potato peel,

respectively. It includes the conversion of feedstocks into sugars, acids and other by-products.

Heating and cooling demand during the pre-treatment and the hydrolysis steps, as well as

addition of the necessary additives is included. Table 1 reports the pre-treatments and the

operating conditions used for the simulations.


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Table 1 - Operating conditions of pre-treatment and hydrolysis step (CH = Chemical hydrolysis, AH = Autohydrolysis, EH = Enzymatic hydrolysis)

Apple pomace Potato peel Brewer's spent grain

Step CH EH AH EH CH EH

Time in h 0.08 72 0.94 72 0.18 72

Temperature in °C 100.2 50 140.2 50 114.2 50

PEG 6000 in w% 1.96 - - - -

HNO3 in w% - - - 2.08

Enzyme Cellic CTec2 in µl/g dry BM 29 29 29

Enzyme Spirizyme in µl/g dry BM - 10 10

Citric acid anhydr. in mg/g dry BM 96 96 96

Water in ml/g dry BM 0.5 0.5 0.5

NaOH, 40% im ml/g dry BM 0.3 0.3 0.3

The feed stream of AWFs to the pre-treatment was fixed to a mass flowrate of 500 kg/h.

Before the product from the pre-treatment step was fed to the fermentation, an evaporator

was used decrease the temperature to 37°C in order to meet fermentation's demands. The

fermentation block contained structures for fermentation as well as in-situ product removal,

in this case adsorption. Four fermenters were considered and a set of 34 reactions (see D6.2

for the reactions) were used to describe conversion of fermentable sugars to acetone, butanol

and ethanol by Clostridium acetobutylicum. Adsorption took place between the third and

fourth reactor. The adsorbent chosen was Amberlite XAD-7 and its performance data are

reported by Raganati et al. 1 and can be found in D6.2. The final step was purification by

distillation. As the necessary purity of butanol cannot be achieved by in-situ methods alone, a

final distillation was mandatory. Two options were considered. A complete separation

sequence with distinct distillation columns for recovery of acetone, butanol and ethanol

treating the output stream from the last fermenter as well as the product from the in-situ

recovery unit and a reduced distillation scheme for butanol recovery dealing only with the

stream from the in-situ recovery unit. These two approaches were chosen to allow a direct

comparison of the two distillation routes for each simulation case. The recovery target for

acetone, butanol and ethanol columns was at least 98.5 w%. Table 2 gives a short overview

on the distillation column settings chosen for the separation unit. A more detailed discussion

can be found in deliverable 6.2.

Table 2 - Operating conditions of the distillation column settings

Flowsheet name Dist1 Dist2 Dist3 Dist41 Dist42

Purified components Acetone Butanol Ethanol

Acetone Ethanol Water Butanol

No. of stages 45 30 40 10 10

Feed stage 1 15 10 1 1

Operating pressure in bar 1.5 0.7 0.3 1.0 1.0

Recovery in w% 97.5 98.5 98.5 > 98.5 > 98.5

Purity in w% - 98.5 80 (60) > 98.5 > 98.5

Murphree efficiency 0.6 0.6 0.6 0.6 0.6


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3.2. Life cycle inventory

Table 3 summarizes the inventory of the process and the key parameters of the pre-treatment

step, enzymatic hydrolysis, ABE fermentation and butanol recovery stages developed within

this project.

Table 3 – List of the key parameters associated with the pre-treatment step, enzymatic hydrolysis, ABE fermentation and butanol recovery stages developed within this project

Apple Pomace Potato Peel Brewer spent grain

Case 1 Case 2 Case 1 Case 2 Case 1 Case 2

conversion output

biofuel yield, kg/kg carbohydrates in

biomass 0.14 0.14 0.11 0.10 0.09 0.10

co-product output

EtOH, kg/kg BuOH 0.12 - 0.12 - 0.13 -

Acetone, kg/kg BuOH 0.18 - 0.19 - 0.20 -

conversion inputs

Pre-treatment Feedstock 500 500 500 500 500 500

Ethylene Glycol 98 98 - - - -

Nitric Acid - - - - 104 104

Cellic CTec2 (enzyme) 11.8 11.8 13.7 13.7 14.0 14.0

Spirizyme (enzyme) - - 4.7 4.7 4.8 4.8

Citric Acid 48 48 48 48 48 48

Sodium Hydroxide 60 60 45 45 60 60

Fresh water 4402 4402 4788 4788 4736 4736

fermentation Nitrogen 632 632 590 566 611 566

Amberlyte XAD-7 17.7 17.7 15.9 15.9 16.9 16.9

Energy demand

Heating 590 190 671 257 617 204

Cooling 103 106 114 157 44 71

Mechanical 122 122 112 111 127 111

Thermal energy, electricity demand, chemicals and waste stream of the utilities units were

taken from the literature2 for similar process (ethanol production from corn stover). In

particular, the relevant parameters of wastewater treatment, boiler/combustion systems and

enzyme production are reported in Table 4, Table 5 and Table 6, respectively.


6

Table 4 – Key parameters of the wastewater treatment unit

Waste Water treatment

Hydraulic load 3.76 E5 kg/h

Inlet pH 5.2

Total COD 87 g/L

Soluble COD 85 g/L

Total solids 68 g/L

Total suspended solids 1.5 g/L

Total alkalinity 2,750 mg/L as CaCO3 2750 mg/L as CaCO3

Ammonia-N 1060 mg/L

Total Kjeldahl, N 1200 mg/L

Sulfate 4400 mg/L

Silica 1600 mg/L

Potassium 500 mg/L

Phosphate 805 mg/L

Heating demand kW/L wastewater to treat 0.005

Cooling demand kW/L wastewater to treat 0.018

Electricity demand kW/L wastewater to treat 0.018

Table 5 – Key parameters of the boiler and combustion unit

Boiler and combustion

Generator efficiency 85%

Steam produced 2.39E5 kg/h, 454°C, 62atm

Calcium hydroxide load 20% of the slurry

CO2 production 0.31 kg/MWh

NO2 production 0.31 kg/MWh

Electricity produced kW/kg lignin 2.5

Heating demand kW/kg lignin 0.16

Cooling demand kW/kg lignin 3.6

Electricity demand kW/kg lignin 0.08

Water consumption kg/kg lignin 0.06

Table 6 – Key parameters of the in-situ enzyme production unit

Enzyme production

Glucose load kg/kg enzyme 4.6

Ammonia load kg/kg enzyme 0.22

Corn steep liquor load kg/kg enzyme 0.32

Air load kg/kg enzyme 62.5

Carbon Dioxide produced kg/kg enzyme 4.6

Enzyme produced kg/h 1

Heating demand kW/kg enzyme 0.9

Cooling demand kW/kg lignin 21.7

Electricity demand kW/kg lignin 7.2

Water consumption kg/kg lignin 6.3


7

3.3. Life cycle assessment

Direct emissions in terms of carbon dioxide, energy and water consumption for the all bio-

butanol production (including main and utilities units) are shown in Table 7. The CO2 produced

within this process is originated from biomass and thus, it is considered as biogenic CO2

emissions. The biogenic CO2 is considered neutral with respect to global warming because it

is part of the renewable carbon cycle. Its characterization factor is taken as zero throughout

the study. The estimation of carbon dioxide emissions in an outcome of the model simulation.

The results show that the Apple Pomace has the lowest CO2 direct emissions (around 22.8

kg/kg BuOH) followed by the Brewer Spent Grain and Potato Peel (35.4 and 38.9 kg/kg BuOH,

respectively). Since the plant energy/electricity requirements are met internally, there are no

direct fossil CO2 emissions. All scenarios of butanol production from agro-food wastes show

higher CO2 direct emissions, water and energy consumption compared to the case of butanol

production from corn stover. The agro-food wastes results were taken out the process

simulation developed within this project and reported in deliverable 6.2 and 6.3. However, it

must be stressed that while pre-treatment and fermentation have been optimized in terms of

reactors, chemicals, resident time, temperature and pressure, the enzymatic hydrolysis step

was just tested under a single set of operating conditions. For this reason, further research on

this step could decrease the gap between butanol production from agro-food wastes and corn

stover.

Table 7– Direct emissions of CO2, water consumption, heat/cooling and electricity demands

Apple Pomace Potato Peel Brewer spent grain

Case 1 Case 2 Case 1 Case 2 Case 1 Case 2

CO2 emission kg/kg BuOH 22.8 22.8 38.9 38.9 35.4 35.4

H2O consumption kg/kg BuOH 16.8 17.7 28.5 27.0 29.9 31.3

Heating demand kW/kg BuOH 22.9 9.0 36.2 16.3 35.8 15.7

Cooling demand kW/kg BuOH 43.6 43.7 73.4 75.4 81.6 83.1

electricity consumed kW/kg BuOH 12.2 12.7 19.5 20.3 22.9 23.8

The butanol production from Apple Pomace also shows the lowest water consumption and

energy demands, whereas Potato Peel and Brewer Spent Grain have similar results. Figure 2

shows the internal plant distribution of CO2 emissions and energy demands for the Apple

Pomace feedstock when considering Case 1 and Case 2, all the other results can be found in

the appendix. The largest difference between the two cases is observed for all the agro-food

wastes in the heating demands. Separation unit represents the main contributor for heating

requisite for case 1 and it becomes the lowest in Case 2. This result reflects the fact that in

Case 2, butanol is the only product recovered by distillation. According to Figure 2, the other

relevant contributors for the heating demands are the pre-treatment and fermentation units.

On the other hand, the exhausts combustion gases are the main contributors for the CO2

emissions followed by pre-treatment and enzyme production units (same results for both

cases). A very limited variation was observed for the cooling demands in Case 1 and Case 2,

mainly because around 80% of this energy is used for condensing the steam turbine exhaust


8

in the boiler and to maintain a constant temperature in the enzyme reactor production.

Moreover, the lignin content of the feedstock is used as fuel for the boiler/combustion unit.

This unit generates steam and electricity, which allows the plant to be self-sufficient in energy

(“thermal-neutral”). Part of the electric energy, around 70%, is used throughout the plant

(power pumps, agitators, compressors, etc.), whereas the surplus, around 30%, can be sold to

the grid for credits. The plant electricity distribution among all areas is also shown in Figure 2,

it is only reported case 1 since the results are the same. According with the diagram, the pre-

treatment, wastewater treatment and enzyme production units require the highest amount

of electricity.

Figure 2 – Internal plan distribution of direct CO2 emissions, heating, cooling and electricity

requirement in case of Apple Pomace.

.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CO2emissions

heatingcase 1

heatingcase 2

coolingcase 1

coolingcase 2

electricitydemand

pretreatment Fermentation/adsorption separationenzyme production wastewater treatment boiler/combustion


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3.4. Life cycle impact assessment

The Life Cycle Impact Assessment (LCIA) identifies and evaluates the amount and significance of the potential environmental impacts arising from the LCI. The methodology of impact assessment used for this study is the ‘‘ILCD” (The International Reference Life Cycle Data System) since it is has been identified as best available approaches for impact assessment from the Research Centre (JRC) of the European Commission3,4. In particular, the environmental performances are measured considering the Global Warming potential, Acidification, Eutrophication and resource depletion. A description of these impact categories can be found in the D7.1. Table 8 summarizes the LCA results for the Butanol production from agro-food wastes. All six

scenarios show higher burden compared with the reference case (conventional butanol from

fossil) and second generation from corn stover of butanol production. More detailed

information on the environmental burdens related to conventional butanol production from

fossil resources and second-generation butanol production from corn stover can be found in

deliverable 7.1. In the following a brief summary is reported. The butanol production from

corn stover is shown to have lower environmental burdens when compared with the

reference case in terms of total GHG emissions, eutrophication fresh water, Ozone depletion

and resource depletion. The results are different when the focus is on acidification,

eutrophication marine and terrestrial; for these three indicators, the butanol production from

corn stover has the higher environmental impacts compared with the reference case. This is

mainly due to the emissions from the combustion of the lignin and biogas, accounting for

NMVOC emitted to air

Table 8– ‘‘ILCD” impact assessment results

Reference

case

CS AP

Case 1

AP

Case 2

PP

Case1

PP

Case 2

BSG

Case 1

BSG

Case 2

Acidification midpoint (v1.09) [moles of H+ eq.]

1.17E-02 1.94E-02 1.36E-01 1.43E-01 1.87E-01 1.92E-01 2.32E-01 2.38E-01

Climate change midpoint, excluded biogenic carbon (v1.09) [kg CO2-Equiv.]

2.28E+00 -1.58E-01 1.52E+01 1.29E+01 1.78E+01 1.36E+01 3.30E+01 2.86E+01

Eutrophication freshwater midpoint (v1.09) [kg P eq]

7.39E-04 4.12E-05 3.73E-03 3.94E-03 5.31E-03 5.58E-03 6.21E-03 6.52E-03

Eutrophication marine midpoint (v1.09) [kg N-Equiv.]

1.68E-03 2.88E-03 2.56E-02 2.66E-02 3.66E-02 3.74E-02 4.93E-02 5.04E-02

Eutrophication terrestrial midpoint (v1.09) [moles of N eq.]

1.71E-02 6.40E-02 3.84E-01 4.02E-01 5.56E-01 5.73E-01 7.03E-01 7.23E-01

Ozone depletion midpoint (v1.09) [kg CFC-11 eq]

3.03E-07 -1.08E-08 9.28E-07 9.97E-07 1.27E-06 1.36E-06 1.47E-06 1.57E-06

Photochemical ozone formation midpoint, human health (v1.09) [kg NMVOC]

9.28E-03 5.42E-03 4.11E-02 4.13E-02 4.28E-02 4.03E-02 6.34E-02 6.13E-02

Resource depletion water, midpoint (v1.09) [m³ eq.]

3.89E-02 -1.77E-02 3.96E-01 3.93E-01 3.83E-01 3.72E-01 6.02E-01 5.91E-01

Resource depletion, mineral, fossils and renewables, midpoint (v1.09) [kg Sb-Equiv.]

6.74E-05 -4.26E-06 9.24E-04 9.78E-04 1.26E-03 1.32E-03 1.46E-03 1.53E-03


10

From the table 8 and figure 3, it can be observed that Apple Pomace among the others agro-

food wastes, specifically for Case 1, always has a better environmental performance except

for the GWP impact category. This result is because the energy consumption necessary for the

separation of the ethanol and acetone has higher burdens compared with the credits related

to the number of by-products produced.

Figure 3 – General overview on LCA results for all agro-food wastes in case 1 and case 2.

Figure 4 reports in different colours the contributors to each impact categories for scenario 1

(apple pomace, case 1), all the results for the other feedstock can be found in the appendix.

According to the figure, the pre-treatment process is the main responsible for the

environmental burdens in all the impact categories except for the Eutrophication terrestrial,

where the key unit is the boiler/combustion unit. It can also be noted that the fermentation

unit gives an important contribution to each impact categories. In order to have a clear

understanding of the main actors a specific analysis was performed for each impact categories

for these two main units.

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

1.00E+02

Acidificationmidpoint

(v1.09) [Moleof H+ eq.]

Climate changemidpoint, excl

biogeniccarbon (v1.09)[kg CO2-Equiv.]

Eutrophicationfreshwatermidpoint

(v1.09) [kg Peq]

Eutrophicationmarine

midpoint(v1.09) [kg N-

Equiv.]

Eutrophicationterrestrialmidpoint

(v1.09) [Moleof N eq.]

Ozonedepletionmidpoint

(v1.09) [kgCFC-11 eq]

Photochemicalozone

formationmidpoint,

human health(v1.09) [kgNMVOC]

Resourcedepletion

water,midpoint

(v1.09) [m³eq.]

Resourcedepletion,

mineral, fossilsand

renewables,midpoint

(v1.09) [kg Sb-Equiv.]

AP Case 1 AP Case 2 PP Case1 PP Case 2 BSG Case 1 BSG Case 2


11

Figure 4 – Main contributors to each impact categories for scenario 1 (apple pomace).

Figure 5 reports the breakdown results of the pre-treatment unit process for each impact categories. The citric acid production is the major responsible for the impacts in all categories followed by the production of the ethylene glycol. Regarding the fermentation unit, the adsorbent production (Amberlyte XAD-7) used for the in-situ separation of Butanol has the largest impacts in all categories.

Figure 5 –Results breakdown of the pre-treatment unit process.

-

10

20

30

40

50

60

70

80

90

100


(v1.09) [Mole ofH+ eq.]


biogenic carbon(v1.09) [kg CO2-

Equiv.]


(v1.09) [kg Peq]



Equiv.]


(v1.09) [Mole ofN eq.]


(v1.09) [kg CFC-11 eq]

Photochemicalozone

formationmidpoint,


Resourcedepletion

water, midpoint(v1.09) [m³ eq.]

Resourcedepletion,

mineral, fossilsand

renewables,midpoint

(v1.09) [kg Sb-Equiv.]pretreat ferment enzyme separation wastewater treatment boiler/combustion

0

10

20

30

40

50

60

70

80

90

100


(v1.09) [Moleof H+ eq.]

Climatechange

midpoint, exclbiogenic

carbon (v1.09)[kg CO2-Equiv.]


(v1.09) [kg Peq]



Equiv.]


(v1.09) [Moleof N eq.]


(v1.09) [kgCFC-11 eq]

Photochemicalozone

formationmidpoint,


Resourcedepletion

water,midpoint

(v1.09) [m³eq.]

Resourcedepletion,mineral,

fossils andrenewables,

midpoint(v1.09) [kg Sb-

Equiv.]

ethylene glycol Sodium Hydroxyde water citric acid


12

Figure 6 –Results breakdown of the fermentation unit process.

0

10

20

30

40

50

60

70

80

90

100


(v1.09) [Mole ofH+ eq.]



Equiv.]


(v1.09) [kg P eq]



Equiv.]


(v1.09) [Mole ofN eq.]


(v1.09) [kg CFC-11 eq]

Photochemicalozone

formationmidpoint,


Resourcedepletion

water, midpoint(v1.09) [m³ eq.]

Resourcedepletion,

mineral, fossilsand

renewables,midpoint

(v1.09) [kg Sb-Equiv.]

nitrogen gasses emission adsorbant


13

3.4.1. Bio-fuels from Algae

Several studies, available in literature 5–11, try to assess the environmental performances, such as GHG emissions and energy balance, of micro/macro algal feedstock for fuel production. These studies found three main technological pathways:

1. Microalgal biodiesel via chemical processing; 2. Microalgal biodiesel via thermochemical processing; 3. Macroalgal biogas and bioethanol via biochemical processing.

It must be stressed the we found that all the available studies considered unreal scenarios based on experimental laboratory data mixed with unpublished experimental data. Therefore, relevant parameters affecting the life cycle results of the examined biofuel production pathways, sometimes lack of transparency and calculations could not be reproduced. Nevertheless, it was possible to identify main data necessary for the inventory and key parameters driving the results of LCA studies. In particular, it exists up to hundred thousand of different microalgal strains with different compositions, thus the selection of the species represents the first step and they are typically sire-dependent. Cultivation, growth conditions, harvesting and dewatering represent critical points for the energetic demand. Moreover, different technology options were proposed for the effective transformation of the algal to biofuels, however, these technologies have been tested only at the laboratory/pilot-scale. The main issues related to the large-scale production and commercialization are the high demands of resources for algal cultivation (energy, nutrients, water and carbon dioxide), low productivity and low lipids yield in outdoor culture, high energy requirement for biomass production and conversion, availability of suitable land with particular climatic conditions and technical challenges of scale up scale. Furthermore, it must be also considered that the biofuels production from algae pathways is strictly linked to the co-existence of four factors: sufficient irradiance, land, carbon dioxide (at concentration larger than the atmospheric value), and water in the same location. This issue makes the industrial development of algal biofuels not only a technical challenge but also very dependent on the site location for the production plant and infrastructures.

3.4.2. Microalgal bio-diesel energy demand vs Agro-food

The early stage of research state of biofuels production from microalgae (biomass of third generation) together with the assumption made and the utilization of unpublished data make difficult to perform a proper comparison with the process developed within this project. Nevertheless, in the following an attempt of comparison is proposed in terms of energy consumption and Global Warming Potential. The studies proposed by Lardon et al. 5, Quinn et al. 10 and Sander et al. 8 were used to compare the microalgal biodiesel production via chemical processes and butanol production from agro-food wastes. While the papers of Handler et al. 7 and Bennion et al. 11 were used as base for the biofuel production from microalgae via thermochemical processes. In order to allow the comparison, the functional unit used for the comparison is 1 MJ of biofuel. The energy densities considered for biodiesel and biobutanol are 38 MJ/kg and 36 MJ/kg respectively. Table 9 reports the key parameters and assumption used in each study. It must be stressed that since several of the data used to obtain the final values of the energy consumption and GWP are unpublished it was to possible at this stage to create an inventory and then, a model representing the whole process for the biofuel


14

production from algae. Moreover, the large deviations in the energy and GHG emissions balances depend, beside the specific technologies adopted, on the system boundaries, modelling parameters and how multifunctionality was solved. Especially the credits considered for co-products management play fundamental role. Sensitivity analysis results are thus very important and these are almost missing for all pathways.

Lardon et al. 5 focus their study on the comparison of two fertilization conditions for microalgae growth, and of dry/wet methods for extraction of lipids from microalgae. They considered a “Cradle to grave” System Boundaries of the biodiesel production system and they used 1 kg of biodiesel as functional unit. Quinn et al. 10 tried to assess the effects of microalgae biomass productivity, extraction technologies (hexane vs. supercritical CO2) and integration of anaerobic digestion unit (allowing to nutrients recycling and CHP unit for on-site energy supply) on net energy ratio and life cycle GHG emissions. The System Boundaries includes: microalgae production; dewatering; lipids extraction and end-use of lipids-extracted biomass. They used 1 MJ of biofuel as functional unit. Sander et al. 8 focused on the comparison of microalgae dewatering methods (filter press and centrifuge) and use of wastewater for algal growth. The System boundaries and functional unit used were “well to wheels” and 1MJ of biofuel. Regarding the thermochemical route, Handler et al. 7 and Bennion et al. 11 estimated the life cycle (fossil) energy demand and GHG emissions of microalgae biofuels . The models include input data for cultivation, harvesting- dewatering, drying, bio-oil recovery through pyrolysis, bio-oil stabilization, bio-oil hydro-processing and co-products use and the functional unit is 1MJ of biofuel.


15

Microalgae

strain

Cultivation

conditions

Harvesting/dewaterin

g

Drying Extraction Conversion Site location

Lardon 5 Chlorella v. Open Raceway Pond, N-normal growth and N-low growth

Flocculation/filtration

=90%

Dryer -Hexane extraction

(dry route)

-Hexane extraction

(wet route);

=70%

Transesterification Mediterranean

Quinn 10 Nannochloropsis

salina Open Raceway Pond, three

stages of

reactor, N-normal growth and N-low growth

Bioflocculation/

Dissolved Air

Flotation/centrifugation

=95%

Dryer;

=90%

-Pressure

homogeneization;

hexane extraction

(dry route)

-Pressure

homogeneization;

ScCO2 (dry route);

=90%

Not defined Not defined

Sander 8 Mixed strains Open Raceway Pond, N-normal growth

Centrifugation;

Chamber filter press

Solar drying Hexane extraction Transesterification Not defined

Handler 7 Mixed strains,

Nannochloropsis

sp.

Open Raceway Pond

Flocculation;

Settling or Dissolved

Air Flotation (10%

solids); Centrifugation

(26% solids)

Thermal

drying (90%

solids)

Pyrolysis (RTP

green fuel);

Stabilization;

Catalytic

Hydroprocessing


Bennion 11 Scenedesmus d. Open Raceway Pond

Membrane filtration (4%solids);

Lyophilizatio

n (90%

solids);

Pyrolysis

Stabilization;

Hydroprocessing



16

Centrifugation (22% solids)

Bio-flocculation (1% solids); Dissolved Air

Flotation (1.5% solids); Centrifugation (24% solids)

=85%

Rotary drum

drying (80%

solids)

=89%

Pyrolysis;

Stabilization;

Hydroprocessing

=51%

Table 9– key parameters and assumption used in each study for the production of biofuel from algae


17

Figure 7 shows the comparison of the Life-cycle energy consumed/energy produced (NER <1 is desirable) of biofuel pathways from algae and Agro-food wastes.

Figure 7 – Life-cycle energy consumed/energy produced (NER <1 is desirable) of biofuel pathways.

The results show that the most favourable case for biofuel from microalgae were obtained by the studies in which large credits were assigned to lipids-extracted microalgal biomass, such as considering the displacement of corn for ethanol production (Sander ww dry route), while all other cases show higher energy demand (blue line) compared to the energy produced (orange line). Only few pathways have NER<1. These results highlight that the management of co-products is actually critical point for evaluating the overall energy balance of biofuel pathways. However, these credits are "numerically" essential to obtain positive NER for many pathways both from algae and agro-food wastes and this should be critically analyzed since may not accurately represent what may happen in reality.

Figure 8 shows GHG emissions of microalgal biofuel pathways referred to 1MJ biofuel for both microalgae and agro-food wastes. The graph suggests that the displacement method used for the management of by-products plays a relevant role for the biofuel from algae, whereas its role is less significant in the case of agro-food wastes. However, it can be noted that the values

-12

-10

-8

-6

-4

-2

0

2

4

6

8

MJ/

MJ

bio

fuel

energy demand MJ/MJ fuel energy produced MJ/MJ fuel NER=1 NET value


18

of CO2 equivalent of the biofuel from agro-food wastes fall in the range of variability of the Algae cases.

Figure 8 – GHG emissions of microalgal biodiesel pathways referred to 1MJ biofuel.

-2

-1

0

1

2

GW

P, C

O2

eq

./M

J b

iofu

el

GWP CO2 eq./MJ fuel GWP CO2 eq./MJ fuel Series3


19

4. Life Cycle Cost Analysis Life cycle costing (LCC) represents a sustainability tools that focus on flows in connection with

production/consumption of goods and services. LCC is an economic approach that sums “total

costs of a product, process or activity discounted over its lifetime”12. It is associated with cost

in general rather than just environmental costs. The first step to assess progress toward

sustainability is that these "money driven" decisions can be assessed in terms of the physical

limits of natural systems. Therefore, LCC is seen alongside LCA as two of the three pillars in an

evaluation of sustainability, with the third, social assessment, still in development. A robust

LCC framework should be able to link life cycle assessment (LCA) studies to the monetary cost

systems used by business decision-makers.

According to the SETAC working group on LCC, there are three different types of LCC13:

• Conventional LCC.

• Environmental LCC.

• Societal LCC.

Environmental LCC was chosen for this analysis. The conceptual framework of environmental

LCC is based on the physical product life cycle of LCA. Cost-of-magnitude estimation seems to

be the appropriate cost estimation technique at this stage.

Capital costs and variable operating costs for AFWs butanol production can be found in

deliverable D7.2. They were assessed mainly based on the data from biochemical design

model for corn stover ethanol14, literature data and online database.

Figure 9 shows the variable cost distribution. The fermentation and pre-treatment areas are

the stages with highest operational costs, 35% and 27% respectively. In particular, the

adsorbent used for adsorption/desorption for the in-situ butanol separation causes the high

cost in the fermentation unit, while the costs in pre-treatment unit are credited to ethylene

glycol (61%), citric acid (25%) and sodium hydroxide (13%) productions. Moreover, the cost of

the biomass, in this case apple pomace, also play a relevant role with around 26% of the total

variable operating costs.

Figure 10 which plots the overall LCC against the Global Warming Potential as one of the LCA

result. It is possible to notice that the butanol production from Brewer Spent Grain show the

highest cost and GWP emissions, while Potato Peel shows lowest cost but higher GWP

emissions compared with the Apple Pomace.


20

Figure 9 – Variable cost distribution of the BuOH production from Apple pomace.

Figure 10 – Life cycle costing vs GWP

However, Figure 11 reports in parallel the hotspots (in percentile) for the cost production and

the Global Warming Potential for all the agro-food wastes in Case 1. According to figure 9, the

hotspots are similar for the economic (LCC) and environmental (LCA) pillars of sustainability

where, both diagrams of all AFWs show that the chemicals necessary for the pre-treatment

(in orange) and fermentation (in grey) stages are the main actors for GWP and cost scores. On

the other hand, the figure also shows some differences. For example, while the wastewater

treatment and boiler/combustion stages (violet and green bars, respectively) contribute

approximately the same for both the cost and the GWP, pre-treatment stage and enzyme

apple pomace26%

pretreatment27%

fermentation/adsorption

35%

enzyme production

5%

wastewater treatment

2%

boiler/combustion1%

credits4%

0

5

10

15

20

25

30

35

0.00 5.00 10.00 15.00 20.00 25.00

GW

P, C

O2

eq

uiv

alen

t/kg

LCC, E/kg

Apple Pomace Potato Peel Brewer Spent Grain


21

production stage have a much larger impact share environmentally than economically. The

contrary happens for the fermentation stage and the biomass, in the case of Apple Pomace,

where the costs have a larger percentage than the global warming score. Both potato peel

and brewer spent grain have a very low price compared with the apple pomace (see table A1

and A2 in the appendix). Furthermore, the credits have larger impact share in the

environmental categories than economical ones.

Figure 12 – Hotspot identification (LCA and environmental LCC) for butanol production from agro-

food wastes. Costs are in euros, and “GWP” refers to global warming potential in kg of CO2

equivalents.

5. Social Life Cycle Analysis

The first question to address what social impacts a product or service has throughout its entire

life cycle. The UNEP/SETAC life cycle initiative attempts in their definition to add another

dimension; that is, the role of the stakeholder, and they also relate the concept of social

impacts to the product’s life cycle. They define of social impacts as follows: Consequences of

social relations (interactions) weaved in the context of an activity (production, consumption

or disposal) and/or engendered by it and/ or by preventive or reinforcing actions taken by

stakeholders (ex. enforcing safety measures in a facility) Therefore social impacts are

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

Climate changemidpoint, excl biogeniccarbon (v1.09) [kg CO2-

Equiv.]

direct cost+variablecost


Equiv.]



Equiv.]


Feedstok pretreat ferment enzyme separation wwt boiler/comb storage disposal credit

Apple Pomace Potato Peel Brewer Spent Grain


22

dimensions of stakeholders relations affected positively or negatively by one of the stages in

the life cycle of a product. At this stage, social effect has been identified and qualitatively

discussed of the butanol production from agro-food wastes in Europe as in relation of Figure

13. A detailed discussion can be found in deliverable 7.3.

Figure 13 – Scheme of the processes involved

6. Integrated frameworks: results and conclusions

The butanol production process has been updated according with all the data provided by the

consortium relating feedstock, pre-treatment, enzymatic hydrolysis, fermentation and

separation (see D1.3, D6.2). Simulation of bio-butanol production via ABE fermentation

showed that even under optimized process conditions and using an adapted upgrading

process the environmental burdens are still an issue. With the valorisation of the lignin

content, available in the feedstock through combustion, the plant is self-energy. Most of this

thermal energy is used to satisfy the requirements of the core units, around 20 kW/kg BuOH

(pre-treatment, fermentation and separation), whereas it is around 6 kW/kg BuOH when using

Corn Stover as feedstock for the butanol production. Suggested pre-treatment procedures

have a high-water demand making them partially responsible for both the low sugar

concentration during the fermentation as well as the low butanol concentration at the end of

the innovative fermenter setup build in the W2Fs project. The high amount of water necessary

for kg of AFW passed through most of the process including each recovery process and the

full distillation sequence, therefore increasing heating and cooling demand.

A comparative life cycle assessment has been undertaken to compare the environmental

burdens associated with the butanol production routes from agro-food wastes and different

separation options. The comparison shows that, at this stage, Apple Pomace has the lowest

environmental burdens compared with other agro-food wastes. All AFWs show the majority

of the impacts depend on the chemicals used in the pre-treatment and the adsorbent used

for the in-situ butanol recovery in the fermentation. In detail, the citric acid production and

LOCAL PROCESSES

Food productionAgro-food wastes collection

Agro-food transportation

Infrastructures chemicals Heat, electricity and diesel

Butanol production plant

BuOH

b)


23

the Amberlyte XAD-7 are the process that mostly influence the environmental burdens of the

production process simulated within the W2Fs project.

Similar results were found when applying the Environmental life cycle costing methodology.

It suggests the majority of both cost and GWP emissions can be attributed to the chemicals

used in the pre-treatment stage and fermentation stage (citric/nitric acid, PEEG 7000 and

Amberlyte XAD-7). Furthermore, Apple Pomace and Potato Peel have lower cost and GWP

emissions compared to Brewer Spent Grain.

Social LCA is still an early stage methodology that need further research to become

standardized as for LCA. However, biobutanol production from agro-food wastes should

suggest that new job opportunities on regional level, new income creating additional market

and additional valuable chains. On the other hand, the high amount of feedstock needed for

a large production plant would make supply logistics more challenging. Furthermore, other

aspects that need to be counted are land occupation and income losses for traditional buyers

of these residues. Despite all the challenges in developing an SLCA approach, a survey

questionnaire (D 7.3) was designed for the context-specific of biofuels in European countries

for addressing some key points: (i) what people from different countries, age and social class

knows about biofuels; (ii) public’s awareness and knowledge about biofuels; (iii) how to move

forward to make the shift to biofuels happen. It would create an European map that will

provide information on public acceptance and social issues related to biofuels in different

countries and from different perspectives.

The integration of the frameworks clearly shows the critical influence of pre-treatment step

and adsorption/desorption step when using AWFs as fermentation feedstock. Moreover, it

showed that upgrading of ABE fermentation broth to butanol still need improvement on

multiple levels and further research is mandatory. In particular, the results suggest on one

hand, focusing on enhancement of the pre-treatment efficiency in order to have higher

amount of fermentable sugars with lower amount of chemicals used in this step and on the

other hand it suggests finding of valuable alternative to the chemicals used such as citric acid

and Amberlyte XAD-7.


24

7. References 1. Raganati F, Procentese A, Olivieri G, Russo ME, Salatino P, Marzocchella A. Bio-butanol

separation by adsorption on various materials: Assessment of isotherms and effects of other ABE-fermentation compounds. Sep Purif Technol. 2018;191:328-339.

2. Pretreatment D. Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol. Renew Energy. 2011;303(May):147. doi:10.2172/1013269.

3. Pant R, Sala S, Bersani R, et al. Recommendations for life cycle impact assessment in the European context-based on existing environmental impact assessment models and factors (ILCD Handbook). 2011.

4. Hauschild MZ, Goedkoop M, Guinée J, et al. Identifying best existing practice for characterization modeling in life cycle impact assessment. Int J Life Cycle Assess. 2013;18(3):683-697.

5. Bernard O. Policy Analysis Life-Cycle Assessment of Biodiesel Production from Microalgae. 2009:6475-6481.

6. Benoît-norris C, Vickery-niederman G, Valdivia S, et al. Introducing the UNEP / SETAC methodological sheets for subcategories of social LCA. 2011:682-690. doi:10.1007/s11367-011-0301-y.

7. Handler RM, Shonnard DR, Kalnes TN, Lupton FS. Life cycle assessment of algal biofuels : In fl uence of feedstock cultivation systems and conversion platforms. ALGAL. 2014;4:105-115. doi:10.1016/j.algal.2013.12.001.

8. Sander K, Murthy GS. Life cycle analysis of algae biodiesel. 2010;2008:704-714. doi:10.1007/s11367-010-0194-1.

9. Batan L, Quinn J, Willson B, Bradley T. Net Energy and Greenhouse Gas Emission Evaluation of Biodiesel Derived from Microalgae. 2010;44(20):7975-7980.

10. Quinn JC, Smith TG, Meghan C, Quinn C. Microalgae to biofuels lifecycle assessment — Multiple pathway evaluation. ALGAL. 2014;4:116-122. doi:10.1016/j.algal.2013.11.002.

11. Bennion EP, Ginosar DM, Moses J, Agblevor F, Quinn JC. Lifecycle assessment of microalgae to biofuel : Comparison of thermochemical processing pathways q. Appl Energy. 2015;154:1062-1071. doi:10.1016/j.apenergy.2014.12.009.

12. Swarr TE, Hunkeler D, Klöpffer W, et al. Environmental life-cycle costing : a code of practice. 2011:389-391. doi:10.1007/s11367-011-0287-5.

13. Hunkeler D, Lichtenvort K, Rebitzer G. Environmental Life Cycle Costing. (Press C, ed.).; 2008. 14. Humbird D, Davis R, Tao L, et al. Process Design and Economics for Biochemical Conversion

of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover. National Renewable Energy Lab.(NREL), Golden, CO (United States); 2011.


25

Appendix A

Figure A1 – Internal plan distribution of direct CO2 emissions, heating, cooling and electricity requirement for BuOH from potato peel.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

CO2emissions

heatingcase 1

heatingcase 2

coolingcase 1

coolingcase 2

electricitydemand

%

boiler/combustion


enzyme production

separation

Fermentation/adsorption

pretreatment


26

Figure A2 – Internal plan distribution of direct CO2 emissions, heating, cooling and electricity requirement for BuOH from brewer spent grain.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

CO2emissions

heatingcase 1

heatingcase 2

coolingcase 1

coolingcase 2

electricitydemand

%

boiler/combustion


enzyme production

separation

Fermentation/adsorption

pretreatment


27

Figure A3 – different contributors to each impact categories for apple pomace, case 2.

0

20

40

60

80

100

120

Acidificationmidpoint (v1.09)[Mole of H+ eq.]



Equiv.]

Eutrophicationfreshwater

midpoint (v1.09)[kg P eq]

Eutrophicationmarine midpoint

(v1.09) [kg N-Equiv.]

Eutrophicationterrestrial midpoint(v1.09) [Mole of N

eq.]

Ozone depletionmidpoint (v1.09)[kg CFC-11 eq]

Photochemicalozone formationmidpoint, humanhealth (v1.09) [kg

NMVOC]

Resource depletionwater, midpoint(v1.09) [m³ eq.]

Resource depletion,mineral, fossils and

renewables,midpoint (v1.09)

[kg Sb-Equiv.]

pretreat ferment enzyme separation wastewater treatment boiler/combustion


28

Figure A4 – different contributors to each impact categories for potato peel, case 1.

0

20

40

60

80

100

120




Equiv.]






eq.]



NMVOC]




[kg Sb-Equiv.]



29

Figure A5 – different contributors to each impact categories for potato peel, case 2.

0

20

40

60

80

100

120




Equiv.]






eq.]



NMVOC]




[kg Sb-Equiv.]



30

Figure A6 – different contributors to each impact categories for brewer spent grain, case 1.

0

20

40

60

80

100

120




Equiv.]






eq.]



NMVOC]




[kg Sb-Equiv.]



31

Figure A7 – different contributors to each impact categories for brewer spent grain, case 2.

0

20

40

60

80

100

120




Equiv.]






eq.]



NMVOC]




[kg Sb-Equiv.]



32

Figure A8 – breakdown results of the pre-treatment unit process in case of Potato peel.

0

20

40

60

80

100

120




Equiv.]





Eutrophicationterrestrial

midpoint (v1.09)[Mole of N eq.]



NMVOC]

Resourcedepletion water,midpoint (v1.09)

[m³ eq.]

Resourcedepletion,

mineral, fossilsand renewables,midpoint (v1.09)

[kg Sb-Equiv.]

Sodium Hydroxyde water citric acid


33

Figure A9 – breakdown results of the pre-treatment unit process in case of brewer spent grain.

0

20

40

60

80

100

120




Equiv.]


midpoint (v1.09) [kgP eq]




eq.]

Ozone depletionmidpoint (v1.09) [kg

CFC-11 eq]


NMVOC]



renewables,midpoint (v1.09) [kg

Sb-Equiv.]

Nitric Acid Sodium hydroxide water citric acid


34

Table A1 – Variable operating costs in case of Potato Peel.

Variable operating cost

process unit material usage cost

kg/h E/kg E/hour ME/year

row material

Potato peel 500 0.01 5 0.04

Pre-treatment

fresh water 117 0.0014 0.164 0.0013

ethylene glycol 0 0.825 0 0

citric acid 48 0.7 33.6 0.2688

sodium hydroxide 25 0.3 7.5 0.06


phenolic resin 15.88 10 158.8 1.27

nitrogen 590 0 0 0

enzyme production

glucose 85.3 0.45 38.385 0.30708

fresh water 420 0.0014 0.588 0.0047

ammonia 4.08 0.3 1.22 0.0098

nutrients 2.39 0.82 1.96 0.016


sodium hydroxide 29 0.3 8.7 0.0696

boiler/combustion

lime 13.9 0.3 4.17 0.0333

fresh water 55.6 0.0014 0.0778 0.00062

waste disposal

boiler/combustion

ash disposal 37.25 0.1 3.725 0.0298

by-products and credits

ethanol 2.52 0.75 1.89 0.0151


35

acetone 3.54 0.99 3.50 0.028

Electricity, kW 213.24 0.0675(kWh) 14.39 0.12

total variable cost 244.11 1.95

Table A2 – Variable operating costs in case of Brewer Spent Grain.

Variable operating cost

process unit material usage cost

kg/h E/kg E/hour ME/year

row material

Brewer spent grain 500 0.01 5 0.04

Pre-treatment

fresh water 57.6 0.0014 0.08064 0.00064

nitric acid 104 0.36 37.44 0.30

citric acid 48 0.7 33.6 0.27

sodium hydroxide 60 0.3 18 0.14


phenolic resin 16.87 10 168.7 1.35

nitrogen 601 0 0 0

enzyme production

glucose 87.1 0.45 39.19 0.314

fresh water 427 0.0014 0.598 0.0048

ammonia 4.14 0.3 1.24 0.0099

nutrients 2.43 0.822 1.99 0.0156


sodium hydroxide 26.8 0.3 8.04 0.064

boiler/combustion

lime 14.3 0.3 4.29 0.034

fresh water 57.3 0.0014 0.080 0.00064


36

waste disposal

boiler/combustion

ash disposal 18.9 0.1 1.89 0.01512

by-products and credits

ethanol 2.28 0.75 1.71 0.01368

acetone 3.54 0.99 3.50 0.028

Electricity, kW 222.21 0.0675(kWh) 14.99 0.119

total variable cost 299.94 2.39

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