“CFD study of a coal direct chemical looping pilot plant”

A DISSERTATION

Submitted in the partial fulfillment of the

requirements for the award of the degree

of

INTEGRATED DUAL DEGREE

(Bachelor of Technology and Master of Technology)

in

CHEMICAL ENGINEERING

(With specialization in Hydrocarbon Engineering)

By

RAHUL WADHWANI

DEPARTMENT OF CHEMICAL ENGINEERING

INDIAN INSTITUTE OF TECHNOLOGY, ROORKEE

ROORKEE-247667 (INDIA)

June, 2014

CANDIDATE’S DECLARATION

I hereby declare that the work, which is being presented in this dissertation, entitled “CFD study

of a coal direct chemical looping pilot plant” submitted in partial fulfillment of the

requirements for the award of Integrated Dual Degree (Bachelor of Technology and Master of

Technology) in Chemical Engineering with specialization in Hydrocarbon Engineering, is an

authentic record of my own work carried out during the period from May 2013 to June 2014,

under the supervision of Dr. Bikash Mohanty, Professor, Department of Chemical Engineering,

Indian Institute of Technology Roorkee.

Date: June 1st, 2014 Place: Roorkee (RAHUL WADHWANI)

CERTIFICATE

This is to certify that the above statement made by the candidate is correct to the best of my

knowledge.

Dr. Bikash Mohanty

Professor Department of Chemical Engineering

Indian Institute of Technology Roorkee

ACKNOWLEDGEMENT

I wish to express my deep sense of gratitude and sincere indebtedness to my supervisor Dr.

Bikash Mohanty, Professor, Department of Chemical Engineering, Indian Institute of

Technology, Roorkee; for his kind cooperation and encouragement that he provides me for

developing new ideas and tackle the various situations which come across while doing the

dissertation work. His undying determination to get the best out of his students served as

inspiration for completion of this report.

I wish to express my profound gratitude to Dr. V. K. Agrawal, Professor and Head, Department

of Chemical Engineering, Indian Institute of Technology Roorkee for providing all the facilities

which have made it possible for me to complete this work. I am also indebted to the faculty

members of Department of Chemical Engineering, who have taught me during last five years.

In addition to it, I am also thankful of Department of Chemical Engineering, IIT Roorkee

administration whose help and resources I have used from time to time, I thank the brains behind

the scientific publications, chemical engineering books and research papers whose guidance I

have drawn on to make this report.

I am thankful to Mr. Amit Rai, Mr. Gajendra K. Gaurav, and Mr. Tejbir Singh, for support

and help during different stages of my dissertation work.

Lastly, I will be always grateful to God and my parents whose blessings are always with me and

acts as a beacon in finding the right path.

i

Carbon emission from fossil fuel, estimated by IPCC has thrown considerable challenge for

researchers and scientists in the past decade. For obvious reasons, the applications of clean

technologies such as chemical looping combustion, oxy-fuel combustion, fuel cells and similar

technologies are becoming an attractive proposition in foreseeable future. Traditional

technologies that generate electricity from fossil fuel via combustion or gasification process

generate flue gas from which separation of carbon dioxide is costly and technically cumbersome.

However, the chemical looping technology, in which carbonaceous materials such as coal can be

oxidize as fuel to generate pure sequestration ready carbon dioxide and heat to generate

electricity appears to be a befitting solution to carbon emission problem.

Abstract

Various studies on gas based fuel for chemical looping combustion have been the major focus in

the past decade while, solid based fuel for chemical looping combustion is relatively a new

concept and very little research efforts have been directed towards this field more specifically

towards CFD simulation of the complete system. The present work entitled as “CFD study of a

coal direct chemical looping pilot plant” is related to the modeling of the pilot plant developed

by Ohio State University, USA for coal direct chemical looping process using Iron (III) oxide on

alumina support as an oxygen carrier for which experimental data are available. For this purpose,

a two dimensional model of two interacting reactors (moving bed reactor and fluidized bed

reactor) is developed using quadrilateral cell on Fluent 6.3.26 and Gambit 2.3.16. The present

models uses Species Transport model and assumes fluid system to study volumetric reactions

between gases and solids. The model takes in to account eighteen homogeneous reactions (coal

Devolatilization, char gasification, oxygen carrier reductions and oxidations, char combustion)

taking place inside two reactors and their inter-connecting parts. The results are verified with the

published pilot plant results. Further, the verified model is used to study the suitability of Indian

coal for coal direct chemical looping process and to identify the possible bottlenecks.

The model predictions of the present work are in good agreement with that of the pilot plant data.

The results of fuel conversion (based on dry ash free coal) of present model for sub-bituminous

coal and metallurgical coke are 91.52% and 85.12% respectively, whereas, respective fuel

conversion for pilot plant are in the range of 97-99% and 70-99%. Thus, the predicted fuel

conversion results have a maximum error of 7.55% and 14.02%. Furthermore, the purity of CO2

ii

(on dry and free nitrogen basis) in reducer exhaust stream is 89.62% and 93.56% for sub-

bituminous coal and metallurgical coke respectively, while, the pilot plant result shows purity

levels of CO2

Further, the developed model is used for studying the suitability of four different grades of coals

found in the region of Asia-Pacific and Australia having considerable amount of ash and are

denoted as “A”, “B”, “C” and “D”. The conversion (based on dry ash free coal) for coal “A” is

65.27% , for coal “B” is 87.82%, for coal “C” is 93.8%, and for “D” is 87.79% while purity of

CO

(on dry and free nitrogen basis) in reducer exhaust stream to be 99% and 99.9%

respectively.

2

From the simulation study it has been identified that for coal with high ash content the

consumption of coal is about 2-4.5 times than that of metallurgical coke. Further, it has been

seen that the reactor bed temperature falls appreciably (5% to 40%) when high ash content coal

is used. For some coals the reactor bed temperature also quenches to a limit that makes process

inoperable. It has been observed that for high ash coals the exhaust CO

(on dry and nitrogen free basis) in reducer exhaust is 70.27%, 80.27%, 89.2% and 90.72%

respectively for coals “A”, “B”, “C” and “D”.

2 gas from fuel reactor

contains small amount (3-7%) of silica which may cause problem in CO2

In addition to it, it has been found that the pressure of the chemical looping combustion process

has considerable effect on fuel conversion and CO

separation.

2

purity. It is thus recommended that chemical

looping combustion should be operated at about 10-15 atmosphere. However, the exact pressure

will be based on economic evaluation of the process.

iii

Abstract

Content of Thesis

i

Contents of Thesis iii

List of Figures vi

List of Tables viii

Chapter 1 Introduction 1

Objective 4

Chapter 2 Literature Review 5

2.1 Experimental Approach 5

2.2 Computational Approach 13

2.3 Oxygen Carriers 19

2.4 Carbonaceous Fuels 26

2.5 Properties Estimation by Neural Network 28

Chapter 3 Problem Description 30

Geometry Parameters 31

Fuel Properties 31

Oxygen Carrier Properties 34

Chapter 4 Model Development 35

Model Assumptions 35

4.1 Mass Conservation Equations 36

4.2 Momentum Conservation Equations 36

4.3 Energy Conservation Equations 37

4.4 Species Transport Equations 38

4.5 Laminar Finite Rate Model 38

iv

4.6 Reaction Kinetics 41

4.7 Effect of Pressure 46

4.8 Standard k-ε model 47

4.9 Group Method of Data Handling (GMDH) model 48

Chapter 5 Solution Techniques 51

Grid Independence Test 51

Computational & Simulation Parameters of study 52

Discretization 53

5.1 Finite Volume Method 53

5.2 Spatial Discretization Method 53

5.3 Temporal Discretization 54

Algorithm of Present study 56

Chapter 6 Results & Discussion 57

6.1 Results of First Segments (Model Validation) 57

6.1.1 Comparison of Simulation Results of the present model and that of pilot plant when Metallurgical coke (MC) is used

57

6.1.2 Comparison of Simulation Results of the present model and that of pilot plant when Sub-bituminous coal (SBC) is used

69

6.1.3 Effect of operating pressure 82

6.2 Results of Second Segments 83

6.2.1 Simulation Results of the present model when Coal “A” is used 84

6.2.2 Simulation Results of the present model when Coal “B” is used 95

6.2.3 Simulation Results of the present model when Coal “C” is used 105

6.2.4 Simulation Results of the present model when Coal “D” is used 116

6.2.5 Comparison between coals 126

6.2.6 Effect of operating pressure 129

v

Chapter 7 Conclusion & Recommendations 132

Conclusion from First Segment of study 132

Conclusion from Second Segment of study 133

Recommendations 133

List of Publications 135

References 136

vi

Fig. 1.1: Chemical looping process outline

List of Figures

2

Fig. 1.2: World proven coal reserves history 3

Fig. 2.1: Oxygen transport capacity of some important oxygen carrier combinations 21

Fig. 2.2: Classification of carbonaceous fuels used for chemical looping combustion 26

Fig. 2.3: Variation in coal compositions found in regions of Asia-Pacific and Australia 27

Fig. 2.4: Variation in Ash composition of coals found in regions of Asia-Pacific & Australia 28

Fig. 3.1: Pilot plant of present problem 31

Fig. 5.1: Mass Fraction of Reducer Exhaust Species vs. Number of Cells (For MC) 51

Fig. 6.1: Rate of Reactions profiles for First part of model validation having mass weighted average rate of reaction greater than 10-10

61-62 for MC

Fig. 6.2: Rate of Reactions profiles for Second part of model validation having mass weighted average rate of reaction greater than 10-10

62-64 for MC

Fig. 6.3: Contours of Velocity & Temperature for MC for second part of model validation 65

Fig. 6.4: Molar concentration contour of important species for MC for second part of model validation

66-67

Fig.6.5: Molar concentration of MC with time for both models along the pathline trajectory 68

Fig. 6.6: Rate of Reactions profiles for first part of model validation having mass weighted average rate of reaction greater than 10-10

73-74 for SBC

Fig. 6.7: Rate of Reactions profiles for second part of model validation having mass weighted average rate of reaction greater than 10-10

75-77 for SBC

Fig. 6.8: Contours of Velocity & Temperature for SBC for second part of model validation 78

Fig. 6.9: Molar concentration contour of important species for SBC for second part of model validation

78-80

Fig. 6.10: Molar concentration of SBC with time along the pathline trajectory for both models

80

Fig. 6.11: Effect of operating pressure on MC for chemical looping combustion 83

Fig. 6.12: Effect of operating pressure on SBC for chemical looping combustion 83

Fig. 6.13: Rate of Reactions profiles having mass weighted average rate of reaction greater 88-91

vii

than 10-12 for Coal “A”

Fig. 6.14: Contour profiles of Velocity & Temperature for Coal “A” 91

Fig. 6.15: Molar concentration profile of various species for coal “A” 92-94

Fig. 6.16: Molar concentration of Coal “A” with time along the pathline trajectory 94

Fig. 6.17: Rate of Reactions profiles having mass weighted average rate of reaction greater than 10-12

99-101 for Coal “B”

Fig. 6.18: Contour profiles of Velocity & Temperature for Coal “B” 101

Fig. 6.19: Molar concentration profile of various species for coal “B” 102-104

Fig. 6.20: Molar concentration of Coal “B” with time along the pathline trajectory 104

Fig. 6.21: Rate of Reactions profiles having mass weighted average rate of reaction greater than 10-10

109-111 for Coal “C”

Fig. 6.22: Contour profiles of Velocity & Temperature for Coal “C” 112

Fig. 6.23: Molar concentration profile of various species for coal “C” 112-114

Fig. 6.24: Molar concentration of Coal “C” with time along the pathline trajectory 115

Fig. 6.25: Rate of Reactions profiles having mass weighted average rate of reaction greater than 10-10

119-122 for Coal “D”

Fig. 6.26: Contour profiles of Velocity & Temperature for Coal “D” 122

Fig. 6.27: Molar concentration profile of various species for coal “D” 123-125

Fig. 6.28: Molar concentration of Coal “D” with time along the pathline trajectory 125

Fig. 6.29: Comparative mass weighted average rate of reactions for four coals “A”, “B”, “C”, “D” on log10

127 scale for Fuel Reactor and inter-connecting part

Fig. 6.30: Comparative mass weighted average rate of reactions for four coals “A”, “B”, “C”, “D” on log10

128 scale for Air Reactor and Riser section

Fig. 6.31: Effect of operating pressure on coal “A” for chemical looping combustion 129

Fig. 6.32: Effect of operating pressure on coal “B” for chemical looping combustion 130

Fig. 6.33: Effect of operating pressure on coal “C” for chemical looping combustion 130

Fig. 6.34: Effect of operating pressure on coal “D” for chemical looping combustion 131

viii

Table 2.1: Types of oxygen carriers researched for chemical looping combustion

List of Tables

21-24

Table 2.2: Comparison of Fe-. Ni- and Cu- based oxygen carriers 24-25

Table 3.1: Geometry Parameters 31

Table 3.2: Proximate Analysis of fuels for first segment of problem 31-32

Table 3.3: Ultimate Analysis of fuels for first segment of problem 32

Table 3.4: Proximate Analysis of fuels for second segment of problem 32-33

Table 3.5: Ultimate Analysis of fuels for second segment of problem 33

Table 3.6: Ash compositions of Fuels for second segment of problem 33-34

Table 3.7: Properties of oxygen carrier used for both segment of study 34

Table 4.1: List of reactions proposed by [14] for first part of model validation in first segment of study

42

Table 4.2: Reaction Kinetics parameters for reactions discussed in Table 4.1 43

Table 4.3: Other significant reactions incorporated in second set of model validation in first segment of study

43-44

Table 4.4: Reaction Kinetics parameters for reactions discussed in Table 4.3 44

Table 4.5: Reactions of reactive ash components and fuels for second segment of study 44-45

Table 4.6: Reaction kinetic parameters for reactions discussed in Table 4.5 45-46

Table 4.7: Developed correlation between Cp (J/kg-K) and T (K) using group model data handling

50

Table 5.1: Grid Independence Test Details 51

Table 5.2: Computational and Simulation Parameters for the Present Study 52

Table 6.1: Process parameters for MC for Model Validation 57-58

Table 6.2: Mass weighted average rate of reactions for MC for the first part of model validation and second part of model validation in different section of the pilot plant

58-60

Table 6.3: Verification of present CFD model for MC 68-69

Table 6.4: Process parameters for SBC for Model Validation 69-70

ix

Table 6.5: Mass weighted average rate of reactions for SBC for the first and second part of model validation in different section of the pilot plant

70-72

Table 6.6: Verification of present CFD model for SBC 81-82

Table 6.7: Process parameters for Coal “A” 84-85

Table 6.8: Mass weighted average rate of reactions for coal “A” 85-87

Table 6.9: Predicted results of coal “A” for present CFD model 95

Table 6.10: Process parameters for Coal “B” 95-96

Table 6.11: Mass weighted average rate of reactions for coal “B” 97-98

Table 6.12: Predicted results of coal “B” for present CFD model 105

Table 6.13: Process parameters for Coal “C” 105-106

Table 6.14: Mass weighted average rate of reactions for coal “C” 107-108

Table 6.15: Predicted results of coal “C” for present CFD model 115-116

Table 6.16: Process parameters for Coal “D” 116

Table 6.17: Mass weighted average rate of reactions for coal “D” 117-118

Table 6.18: Predicted results of coal “D” for present CFD model 126

Page | 1

Chapter 1 INTRODUCTION

Energy and global warming are two intertwined planetary issues of significant

magnitude in the current era. Oil price crossing $110/barrel mark and atmospheric

CO2 level recently reaching to 400 ppm [1] level mark it has become imperative to

develop clean and cost effective energy conversion processes. Renewable energy like

hydro, solar, biomass and wind are unlikely to meet the energy demand in foreseeable

future. Nuclear energy due to its constraint on its spent fuel management and

susceptibility to catastrophic hazards makes it implausible to play a vital role in

meeting future energy demand. Despite, current developments in the fields of

renewable energy, nuclear power and other sources, fossil based fuels provide around

85% of world’s energy demand. This makes the fossil fuels as the most imminent

source of energy in near future. [2]- [3]

The carbon emission from fossil fuel estimated by IPCC [4] has posed

considerable challenge for researchers and scientists in the past decade. For obvious

reasons, the applications of clean technologies such as chemical looping combustion,

oxy-fuel combustion, fuel cells and similar technologies are becoming an attractive

proposition in foreseeable future. Researches [5-6] in these fields are gaining

momentum to tackle above stated planetary issues and to provide clean and efficient

technologies to meet the present and future energy demands. Most of the

conventional oil and gas fields have already been exploited and rate of crude oil

production worldwide peak around 2004 which was roughly 72 million barrels per

day. However, the demand for energy is continuously increasing especially in

developing countries like China and India [2]. Therefore, the availability of abundant

reserves of coal to provide energy for 200+ years is to be utilized properly which are

abundant in countries like India and China. In addition to it, cost of coal is much

cheaper in comparison to other fossil fuels, and its pricing is also regionally

controlled which act as one of its economic drivers.

Page | 2

Traditional technologies that generate electricity from fossil fuel via combustion or

gasification process generate flue gas from which separation of carbon dioxide is

costly and technically cumbersome. In the chemical looping technology, carbon

dioxide is available as a directly sequestration ready stream, and thus significantly

increases its cost effectiveness. The history of chemical looping process dates back to

1951 when Lewis and Gilliland [7] proposed a patented process in which

carbonaceous materials can be oxidize as fuel to generate pure carbon dioxide. In the

chemical looping combustion process, carbonaceous fuel, such as coal; first reacts in

a fuel reactor with a metal oxide which acts as an oxygen carrier and subsequently

gets reduced to metal. The above reaction yields carbon dioxide and steam as

products from which carbon dioxide is readily separable by condensing steam. The

reduced metal in the fuel reactor is oxidized again by air in air reactor for its

regeneration to metal oxide. The metal oxide is then recycled back to the fuel reactor

for reuse. The cyclic process is shown in Fig. 1.1.

Fig. 1.1: Chemical looping process outline

In early years, the adoption of a chemical looping strategy was ineffective as

commercial process lacked effective chemical conversion and the absence of cost

Fuel

Reactor

Air

Reactor

Depleted

O2 air

Atmospheric

air

Fuel

Injection

Regenerated

oxygen carrier

Reduced oxygen

carrier

Page | 3

effective separation techniques compounded the problem. In the last decade, chemical

looping process has gained momentum and has been targeted mainly towards

efficient carbon capturing, hydrogen and power generation, etc. Researchers [8] have

worked with different segments of this process. They have adopted experimental as

well as simulation based approaches to study this process with a variety of gaseous

and solid fuels and nearly more than 700 different type of oxygen carrier have been

tested so far.

According to United States Energy Information Administration (EIA) estimated

total world coal reserves equals to 948 billion short tons by 2009. According to BP

statistical review of 2013 [2], amount of coal found in proven reserve up to 2012 will

meet the demand for 109 year to come which is the highest for any fossil fuel.

World’s largest coal reserves are in USA, Russia, China, Australia, India and

Germany. At the end of 2012, respective shares estimated in global coal reserves are:

USA- 28.6% Russia- 18.2% China-13.3%

Australia- 8.9% India- 7% Germany-4.7%

Fig. 1.2: World proven coal reserves history [2]

Page | 4

India has the fourth largest coal reserve in the world and is the fourth largest

producer of coal. Coal is one of the primary sources of energy, accounting for nearly

65% of total energy consumption in the county. Coal deposits in India occur mostly

in thick seams and at shallow depths. However, Indian coals have high ash content

(15-45%) and low calorific value. With the present Reserve to production (R/P) ratio

of 100, estimated coal reserves in India will last nearly for 100 years. The energy

derived from coal (~65%) in India is more than twice of world’s energy derived from

coal (~30%).

Recent rapid development of chemical looping combustion to seek for an

alternative process for efficient and clean technology for carbon capture is the major

driver of the present study. Various studies on gas based fuel for chemical looping

combustion have been the major focus in the past decade while, solid based fuel for

chemical looping combustion is relatively a new concept and very little research

efforts have been directed towards this field more specifically towards CFD

simulation of complete system.

Under the backdrop of the above facts, the present work has been formulated with

following objectives:

To develop CFD simulation of complete coal direct chemical looping plant

including fuel and air reactors and their interconnecting parts.

Development of simulation model for coals having high ash content to study the

effect of ash in feed with respect to operating parameters, fuel conversion and

purity of CO2 in fuel reactor exhaust.

To study the effect of variation in operating pressure of the system.

Page | 5

Chapter 2 LITERATURE REVIEW

The objective of the present work is detailed in Chapter-1. Based on the above

objective a detailed literature review on different aspects of chemical looping

combustion has been conducted and subsequently reported in this Chapter. Although,

the technology has been proposed by Lewis and Gilliland [7] back in 1950 for

beverage industry to produce pure carbon dioxide using ferric oxide as an oxygen

carrier and methane as a fuel but the most significant development of this technology

took place in the last decade. During this period various aspects of chemical looping

combustion have been investigated such as design and layout of chemical looping

system, fuel and air reactors, oxygen carriers, carbonaceous fuels, optimal operating

parameters like bed height, feed rate, pressure, etc. As the present work is on

computational fluid dynamics (CFD) study, this Chapter also includes detailed

literature review on the CFD study of chemical looping process carried out for

gaseous and solid fuels as well. Further, as it deals with the validation of simulation

results with pilot plant data and then extension of the CFD model for coal with high

ash content (such as Asia Pacific coal and Australian coal) a literature review in this

regard has also been conducted. In addition to it, as the present work requires

properties of oxygen carrier and ash components such as heat capacity which are

dependent on temperature, a literature review to this effect has also be carried out.

Further, the literature review Chapter has been segmented into following sections:

experimental based study, computational based study, types of oxygen carriers, types

of fuel for chemical looping combustion, effect of ash, properties estimation via

neural network, etc.

2.1. Experimental approach:

The experimental studies has played an important role in the development of

chemical looping combustion through pilot plant study, lab scale study, TGA

Page | 6

analysis, XRD analysis, etc. to determine the dependence of various parameters and

suitable operating conditions for chemical looping combustion for carbonaceous

fuels. Various such studies for chemical looping combustion have been outlined and

discussed by Wadhwani and Mohanty (2013) [6], Moghatedri (2012) [5], Adanez

(2012) [9] and Lyngfelt (2014) [10].

For experimental studies, various pilot plants have been developed at different

locations across the globe like at Chalmers 10 kW & 100 kW (Lyngfelt (2014) [10],

Lyngfelt (2011) [8]), Nanjing 1 kW & 10 kW (Wu et al. (2010) [11], Wu et al. (2009)

[12]), Ohio 2.5kW & 25kW (Fan & Li (2010) [13], Kim et al. (2013) [14]) and

Hamburg (Thon et al. (2012) [15]), etc. The pilot plant studies have been reported in

various literatures to impart knowledge about the design criteria of the reactors, study

on reactor kinetics, and optimal operating parameters of the system.

Abad et al. (2006) [16] published results pertaining to continuous operation of a

chemical looping combustion (CLC) unit for Natural gas/syngas as fuel and Mn3O4 as

an oxygen carrier of 300W and also tested the feasibility of manganese based oxygen

carrier. In their study they found that higher temperature and lower fuel flow,

enhances fuel combustion efficiency which varied from 0.88 to 0.99. Lyngfelt (2011)

[8] discusses the operational experience of chemical looping combustion processes

developed at various locations so far. Further, Lyngfelt (2014) [10] discussed the

status of development of chemical looping combustion for solid fuels from the

experiences gained and performance observed from the pilot plants developed till

date. Gu et al. (2011) [17] have carried out experimental study on a 1 kWth

continuous chemical looping reactor using biomass/coal as fuel and Australian iron

ore as oxygen carrier. However, they investigated the effect of temperature on gas

composition of fuel and air reactors, carbon capturing efficiency, etc.

Kim et al. (2013) [14] have reported the design criteria and operating conditions of

a 25 kWth coal direct chemical looping sub pilot plant developed at Ohio State

University for two coals (metallurgical coke & sub-bituminous coal) as fuel & Fe2O3

as oxygen carrier. Their study showed 81% and 97% conversion of fuels i.e.

metallurgical coke and sub-bituminous coal respectively. For both type of coal, purity

Page | 7

of CO2 in output stream was more than 99%. Leion et al. (2009) [18] have carried out

batch chemical looping combustion with iron ore and scales of iron oxide as oxygen

carriers with solid fuels such as Petroleum coke, charcoal, lignite and two bituminous

coals. The experiments were run at cyclically operated lab based fluidized bed reactor

with alternating oxidation and reduction phases. Their results showed that both

oxygen carriers passed their suitability as oxygen carrier and also their reactivity

increased with time.

Kolbitsch et al. (2009) have investigated the performance of 120 kW dual

circulating fluidized bed chemical looping combustor. They used a very simple

structure of circulating fluidized bed to develop the model in which the reacting gas is

only in contact with a defined fraction of well mixed solid particles. Thus, different

parameters that characterized gas-solid interaction are merged into a single parameter

i.e. the fraction of solid exposed to gas passing in plug flow. The 120 kW pilot plant

used Ni based oxygen carrier and natural gas as fuel. Further, they concluded that

oxygen carrier is fully oxidized in air reactor when the air reactor solid inventory is

much larger than fuel reactor or when both reactors are very large. Pröll et al. (2011)

[20] carried out experimental study using methane as fuel and NiO as oxygen carrier

for a 120 kW pilot plant. The system consists of two inter-connected circulating

fluidized bed reactors. They observed that fuel conversion for methane has been in

the range of 99.8% while carbon dioxide yield is 92%.

Xiao et al. (2010) [21] have investigated the pressurized CLC by using Chinese

bituminous coal in a medium-pressure, high temperature fixed bed reactor & with

iron (Companhia Valedo Rio Doce iron ore) ore as oxygen carrier. They also

estimated the effect of operating pressure and concluded that pressurized condition

suppresses the initial reaction of coal pyrolysis while it enhances the coal char

gasification and reduction of iron ore in steam. Hence, limited pressurized chemical

looping combustion shows a promising process. Scott et al. (2006) [22] have carried

out experimental study on chemical looping combustion using solid coal as fuel to

test the suitability of using solid coal as carbonaceous fuel for chemical looping

combustion. Their study concluded that the in-situ gasification of solid fuel in

chemical looping combustion is feasible provided the gasification agent like CO2 is

Page | 8

fed to the reactor which converts coal to CO which is then oxidized by oxygen

carrier. Bayham et al. (2013) [23] have discussed the 25 kWth coal direct chemical

looping pilot plant developed at Ohio State University which used two solid fuels

sub-bituminous coal and lignite. Their test showed more than 90% coal conversion

with 99.5% carbon dioxide purity from reducer exhaust for both fuels.

Arias et al. (2013) [24] discussed the results of calcium looping 1.7 MWth plant for

circulating fluidized bed combustors and demonstrated the concept of MWth scale

plant to facilitate the scale-up methods. Azis et al. (2013) [25] have experimentally

studied the effect of bituminous and lignite ash on the performance of chemical

looping combustion where in ilmenite as oxygen carrier is used. They concluded that

addition of ash has decreased fuel conversion while it doesn’t show any negative

effect on fluidizability of the bed material. Bao et al. (2014) [26] studied

experimentally the interaction between iron-based oxygen carried and four different

types of coal ash in a lab-scale fluidized bed reactor. They concluded that few

components of ash such as CaSO4 and Fe2O3 helped in the reduction of reaction time

by acting as an extended oxygen carrier while other components hindered the

reduction rate. Thus, the existence of ash with solid fuels such as coal for chemical

looping combustion have considerable impact on oxygen carrier reactivity while it

also hindered the solid fluidization.

Källén et al. (2014) [27] carried out experimental study using Fe0.66Mn1.33SiO3 and

FeMnSiO3 as oxygen carrier in fluidized bed reactor system designed for a 300W

thermal power. Their study used natural gas as fuel with Fe0.66Mn1.33SiO3 as oxygen

carrier and showed that a conversion reaching up to 100% around 950⁰C is

achievable. Mendiara et al. (2014) [28] focused their study on in-situ gasification in

chemical looping combustion using high reactive iron ore as oxygen carrier for

various coals for a 500Wth chemical looping combustion unit. Velazquez-Vargas et

al. (2012) [29] discussed the fundamental concept of coal direct chemical looping

process using pulverized coal as fuel and ferric oxide as oxygen carrier and published

their latest experimental data generated from a 25 kWth pilot facility. Abandes et al.

(2004) [30] carried out experimental study on pilot scale fluidized bed reactor to

investigate the carbonation reaction of CaO to capture carbon dioxide from flue gases

Page | 9

at high temperature. Their result show that the extent of carbon dioxide capturing

efficiency to be very high while the same reduces with number of carbonation-

calcination cycles.

Xiao et al. (2010) [31] studied the performance of a pressurized chemical looping

combustion combine cycle system for solid fuel coal and Companhia Valedo Rio

Doce iron ore on a lab scale fixed bed reactor. Their study showed that pressurized

chemical looping combustion of coal is feasible and a low-cost oxygen carrier adds to

its suitability. Siriwardane et al. (2009) [32] carried out their investigation on

combustion and re-oxidation properties of direct coal based chemical looping

combustion on CuO, NiO, Fe2O3 and Mn2O3 as oxygen carrier using thermo-

gravimetric analysis on bench scale fixed bed flow reactor. Their study concluded

that CuO showed best reaction properties among other carriers. Song et al. (2008)

[33] have proposed the concept of CLC using CaSO4 based oxygen carrier. Further,

they have also performed reduction tests of CaSO4 with simulated coal gas on lab

scale fluidized bed reactor at 890-950⁰C and studied its kinetics using shrinking core

model.

Berguerand and Lyngfelt (2008) [34] have carried out their study on petroleum-

coke based 10 kWth chemical looping combustor using ilmentie, and iron titanium

oxide as oxygen carriers. The fuel reactor has been fluidized by steam. They studied

the effects of particle circulation and carbon stripper operation on petroleum coke,

conversion of gas from the reducer reactor and CO2 capture. Kronberger et al. (2004)

[35] discussed and outlined the conceptual design of large scale (200MW) chemical

looping combustion system which uses refinery gases as a source of fuel carried out

experimental study on small scale model.

Apart from pilot plant and lab-scale studies various investigators have assessed the

effect of various operation parameters of chemical looping combustion. Adanez et al.

(2012) [9] have undertaken a comprehensive study on chemical looping combustion

and chemical looping reforming and discussed significant advances in these

technologies till 2010 which comprises experimental experiences, computational

models, pilot plant developments, etc.

Page | 10

Liu et al. (2004) [36] studied the effect of inorganic materials present in coal on

reactivity and kinetics of coal pyrolysis using TGA. Keller et al. (2013) [37] studied

the interaction between mineral materials common in coal with oxygen carrier by

experimental and thermodynamic equilibrium calculations. Their study concluded

that the CuO/MgAl2O4 and Mn3O4/ZrO2 have quite a tendency to react with mineral

materials while ilmenite has been found to be the most robust oxygen carrier. In

addition to it, sulfur can clearly deactivate Ni-, Cu-, and Mn- based oxygen carriers.

Kang et al. (2014) [38] carried out experimental study using thermo-gravimetric

analyzer for reactions between Fe2O3/ZrO2 and methane, hydrogen, carbon monoxide,

steam and oxygen was studied. Through their study they determined kinetic

parameters for each reduction and oxidation reactions.

Coppola et al. (2014) [39] have conducted experimental investigation to study the

effect in hydration induced reactivation of spent sorbent for a calcium looping

process. They studied the changes in sorbent properties due to induced by hydration,

regeneration of CO2 capture capacity and attrition tendency of the material on looping

cycles. Abad et al. (2011) [40] determined the kinetics of redox reactions of ilmenite

for chemical looping combustion. They carried out their study on pre-oxidized

ilmenite and activated ilmenite as oxygen carrier for the chemical looping system.

They carried out TGA analysis to deduce kinetics for reducing gases such as

hydrogen, methane and carbon monoxide as well as oxygen for oxidation step. The

system used natural gas, syngas and coal as fuel.

Yu et al. (2012) [41] have studied the effects of C/Fe2O3 molar ratio and

impregnated oxygen carrier with alkali carbonates on reduction rate of coal char. In

addition to it, they also studied the feasibility of using coal char for direct chemical

looping combustion with alkali carbonated impregnated oxygen carrier using TGA,

XRD, SEM, and similar techniques. They concluded that impregnated alkali

carbonate increases the reduction rate in the following order K2CO3>Na2CO3>

Li2CO3. Yu et al. (2013) [42] have examined the effect of CO2 atmosphere and

K2CO3 addition (as the reaction between char and ferric oxide is slow which is

enhanced by impregnation of alkali carbonates) on the reduction rate of char for

Page | 11

chemical looping process using ferric oxide as an oxygen carrier. Yang et al. (2008)

[43] have carried out experimental study the feasibility of three processes using

fluidized bed reactor- these are: direct reduction of iron oxide by char, hydrogen gas

production from steam-iron process and oxidation of reduced oxygen carrier to

generate Fe2O3. The fuel for combustion was Chinese lignite coal and the oxygen

carrier was doped with K2CO3.

Cao et al. (2006) [44] have evaluated the reduction of CuO-Cu as an oxygen

carrier with solid fuels such as coal for chemical looping combustion for circulating

fluidized bed fuel reactor. They carried out test on the reduction of CuO by

differential scanning calorimetry and thermo-gravimetric analysis. Further, they

carrier mass spectrometer analysis for evolved gas analysis and X-ray diffraction and

scanning electron microscope for characterization of solid residues. Shen et al. (2007)

[45] have carried out thermodynamic analysis for chemical looping combustion using

coal as fuel in inter-connected fluidized beds with inherent carbon dioxide separation.

Their study indicates that NiO/Ni oxygen carrier showed the optimal results out of

various oxygen carriers such as Co3O4/CoO, CuO/Cu2O, Cu2O/Cu, Fe3O4/FeO,

Fe2O3/Fe3O4, Mn2O3/Mn3O4, etc.

Xiao and Song (2011) [46] have proposed the concept of using CaSO4 based

oxygen carrier for CLC. Further, they have performed tests such as X- ray diffraction

(XRD), scanning electron microscopy with energy dispersive X-ray (SEM-EDX) and

N2 adsorption-desorption techniques to study the kinetics modeling and physical-

chemical characterization analysis for CaSO4. Their experimental study results were

theorized by using gas-solid shrinking un-reacted core model in which both chemical

reaction control and product layer diffusion were considered.

Siriwardane et al. (2010) [47] have worked on the reaction mechanism of chemical

looping combustion between coal & CuO. They demonstrated that the solid/ solid

reactions can be completed at a much lower temperature with rates that are

technically adequate for reaction between fuel & metal oxide. They also studied

potential interaction between the two solids through various techniques like TGA,

Page | 12

XPS, XRD, etc. Gnanapragasm et al. (2009) [48] have utilized operating conditions

for coal direct chemical looping (CDCL) and syngas chemical looping (SCL) to

produce hydrogen directly from coal. Their study showed that CDCL process has

higher H2/CO2 ratio than SCL process and thus proved to be advantageous.

Additionally, CDCL process required fewer resources (steam & air) & generates few

intermediates in process.

Jheng et al. (2010) [49] discussed the thermodynamic and kinetics aspect of CaSO4

as an oxygen carrier for coal based chemical looping combustion. They conducted

experiments on chemical looping process for CaSO4 as oxygen carrier and coal

syngas as fuel were carried out in fluidized bed reactor at different reaction

temperature resulting different intermediates. The products were further analyzed and

characterized by gas chromatograph, gas analyzers and scanning electron microscope.

Luo et al. (2013) [50] have investigated direct chemical looping combustion using

Yimin coal and biomasses using CuO as oxygen carrier. In addition to it, they also

conducted thermo-gravimetric analysis to simulate direct chemical looping

combustion using solid fuels and their co-combustion. Their result proved the

suitability of CuO as oxygen carrier for CLC of solid fuels.

Shen et al. (2009) [51] carried out experimental investigations to analyze gas

compositions of fuel and air reactors, carbon conversion efficiency, carbon dioxide

capturing efficiency and carbon of fly ash in fuel reactor for inter-connected fluidized

bed reactors which consists of high velocity fluidized bed as air reactor, and spout-

fluidized bed as fuel reactor. The two reactors are connected to each other via cyclone

separator. Orr (2012) [52] carried out experimental study to measure the solid

circulation rate in the two inter-connecting reactors developed at Ohio State

University (OSU). He studied iso-kinetic to measure the circulation by determining

the effect of angle of entry of fuels in fuel reactor. He found that all angles that have

been tested showed direct proportionality between the velocity in the iso-kinetic

device and the reactor column.

Page | 13

Zhao et al. (2008) [53] have experimentally observed the direct reduction reaction

of NiO/NiAl2O4 oxygen carriers by coal char. They prepared NiO/NiAl2O4 particles

by sol-gel method rather than dissolution method. TGA was used to evaluate the

reduction reaction through analyzing the weight of mixture as a function of time &

temperature; XRD, SEM & N2 adsorption–desorption methods were utilized to

characterize the solid residues. Jin and Ishida (2004) [54] have experimentally

examined coal gas and natural gas fueled chemical looping combustion and found

that coal gas based chemical looping combustor shows a better reactivity than natural

gas based combustor when NiO is used as oxygen carrier. Labino et al. (2006) [55]

have analyzed the effects of reactor parameters on Cu, Fe, and Ni based oxygen

carrier in syngas fueled chemical looping combustion and concluded that the

dependence of reaction rates on temperature has been low while total pressure has a

negative effect on oxygen carrier reactions.

Saha and Bhattacharya (2011) [56] carried out their experiments using thermo-

gravimetric analyzer for NiO and CuO as oxygen carriers using Victorian brown coal

for five alternative cycles of reduction and oxidation. They concluded a weight loss of

4.4-7.5% for NiO as oxygen carrier while for CuO as an oxygen carrier there was no

such weight loss. Moreover, the percentage combustion of coal using NiO and CuO

as oxygen carriers is 96% and 67% respectively. Further, Saha et al. (2011) [57] have

discussed the same work of Saha and Bhattacharya (2011) [56] for NiO as oxygen

carrier. Hamers et al. (2014) [58] carried out their study on the performance of

oxygen carrier (13 wt. % CuO/Al2O3) in a packed bed reactor with periodic switching

between oxidizing and reducing conditions for syngas as fuel. The experimental

results were well described by a 1D reactor model. Wang et al. (2014) [59] studied

the effect of HCl on cyclic calcium based sorbent in calcium looping process. The

effect of presence of HCl in carbonation atmosphere, carbonation temperature,

calcinations temperature, HCl concentration and particle size on chemical looping

combustion using dual fixed bed reactor was thus investigated.

2.2. Computational approach:

Page | 14

In the development of chemical looping combustion various researchers opted for

computational and numerical approach to study the behavior of chemical looping

combustion and to study the role of various parameters in the operation of chemical

looping process. Various investigation carried out in this field have been outlined and

discussed in detail by Singh et al. (2013) [60], Wadhwani and Mohanty (2013) [6],

Lyngfelt (2014) [10] and Adanez (2012) [9].

The computational approach have mainly targeted through CFD based simulation,

numerical simulation, thermodynamic computation, ASPEN Plus based simulation,

etc. to model the process and to study the behavior of variation of design & operating

parameters. Singh et al. (2013) [60] have reviewed the use of CFD modeling

technique to study combustion and gasification in fluidized beds and discusses the

fundamental equations used in the development of the CFD models. Anheden and

Svedberg (1998) [61] carried exergy analysis on two different chemical combustion

gas turbine systems. The first system utilized methane as fuel and NiO as oxygen

carrier while in the second system utilized a fuel gas mixture primarily composed of

CO and H2 with NiO and Fe2O3 as oxygen carriers. They concluded that the two

systems are comparable in their conventional combustion. The exergy analysis shows

that irreversibility generated during combustion of fuel can be minimized. The net

power efficiency of the chemical looping-gas turbine system is almost similar or

higher than their corresponding gas turbine system with conventional combustion.

Shuai et al. (2011) [62] developed a 2D CFD model for chemical looping combustion

using inter-connected fluidized beds. They used Eulerian continuum two fluid models

for both gas phase and solid phase. Using their model they; successfully described the

hydrodynamics of gas and solid particles in chemical looping combustion process.

Wang et al. (2011 a&b) [63-64] developed a 3-D numerical model to simulate the

chemical looping combustion process of the fuel reactor. They used a bubbling

fluidized bed fuel reactor, with 14 wt% of CuO on Al2O3 as oxygen carrier and coal

gas as fuel which comprises of 55 vol. % CO, 30 vol. % of H2 and 15 vol. % of CO2.

They studied the flow patterns, distribution of gaseous component and profiles of

bubbles, conversion of fuel, effect of particle diameter and superficial gas velocity of

Page | 15

the oxygen carrier particle. They concluded that the fuel conversion will increase if

the gas residence time and surface to volume ratio of particles are increased. Peng et

al. (2013) [65] carried out a numerical study to investigate the mixing and segregation

behavior of binary mixtures of particles in bubbling fluidized bed of a 10 kWth

chemical looping combustor. They used discrete element model to track the motion of

particles and gas flow was modeled by CFD. Gas-particle interactions were

considered by a two-way coupling method.

Abad et al. (2013) [66] developed a mathematical model for the fuel reactor to

assess the key parameters such as reactor temperature, solids circulation rate and solid

inventory on the efficiency of carbon dioxide capture. They validated their simulated

results against a 100 kWth chemical looping combustion unit. Their result showed

carbon dioxide capture efficiency as 98.5% when operating temperature of fuel

reactor was 1000⁰C. Jafarian et al. (2014) [67] carried out a thermal analysis for a

hybrid solar chemical looping combustion to identify energetic performance of

various combinations of oxygen carriers and fuels. Their study showed that the

highest system Carnot efficiency was with using Co- as an oxygen carrier which was

followed by Ni- and Fe- as an oxygen carrier, while the highest solar share was

achieved with Fe- as an oxygen carrier.

Medrano et al. (2014) [68] proposed a novel hybrid reactor that utilized chemical

looping reforming technology and membrane reactor system. The thermodynamic

studies of the above new reactor are carried out to determine the hydrogen recovery,

methane conversion, etc. Schwebel et al. (2014) [69] carried out experimental study

to obtain apparent reaction kinetics of Norway ilmenite as an oxygen carrier with CO,

H2 and CH4 as fuel gases. Their obtained results were modeled to analyze the effect

of different parameters and reported that reaction order with respect to gas is close to

the reported values in the literature.

Deng et al. (2009) [70] developed a multi-phase CFD model in FLUENT to model

chemical lopping combustion using CaSO4 as oxygen carrier and H2 as fuel. They

concluded that the conversion of hydrogen was about 34% and was a probably due

Page | 16

large bubble rising rapidly throughout the reactor, low reactor bed temperature and

large diameter of oxygen carrier particles. Further, Deng et al. (2008) [71] developed

a multiphase CFD model for bubbling fluidized fuel reactor of chemical looping

combustion. Their CFD model incorporated the complex gas-solid hydrodynamics

and chemical reaction for CaSO4 as oxygen carrier and hydrogen as fuel. Wang et al.

(2013) [72] developed a 3D numerical model for a pressurized circulating fluidized

bed fuel reactor for coal fired chemical looping combustion using ilmenite as oxygen

carrier. Their model predicted the complex gas-solid flow behavior in terms of

velocity and voidage contour profiles and also incorporated the reactions between

steam gasification of coal and reduction of oxygen carrier.

Schöny et al. (2011) [73] have discussed a 3-D model for the fuel reactor of a large

scale chemical looping combustion unit. Their model was based on the validated

model available in literature i.e. Thunman et al. (2004) [74] for large scale fluidized

beds along with kinetic data obtained from chemical looping experiments at lab-scale.

Their model was used to evaluate the performance of large scale fuel reactor

including the effect of variation in different inputs, operation strategies such as

feeding point for oxygen carriers and fuels, physical properties of oxygen carriers and

fuel, and operating condition such as fluidization velocity and pressure drop.

Sharma et al. (2011) [75] have demonstrated possible configuration of a 200 MW

CLC system with methane as fuel & iron oxide as an oxygen carrier. They analyzed

various parameters for the design of the process such as mass of the reactor bed,

pressure drop, solid mass flow rate, residence time, etc. Marx et al. (2012) [76]

evaluated the fluidized bed dimensions for a dual circulating fluidized bed system for

a 10 MW fuel power system incorporating heat integration setup and scaling law.

Kronberger et al. (2003) [77] developed a mathematical model for a chemical

looping combustion system with integrated mass and energy balance. They evaluated

effect of number of design and operating parameter i.e. fuel gas composition a reactor

cooling arrangement. Mahalatkar et al. (2011) [78] have developed a computational

fluid dynamics based model for chemical looping combustion fuel reactor for gaseous

Page | 17

fuel and analyzed two experimental cases of literature. They used methane as fuel and

ferric oxide as oxygen carrier.

Kruggel-Emden et al. (2010) [79] have studied an interconnected multiphase CFD

model which was capable of describing the transient response of coupled CLC using

methane as fuel & Mn3O4 supported on Mg-ZrO2 as oxygen carrier. In this case, fuel

reactor was a bubbling fluidized whereas, air reactor was a high velocity riser.

Further, Kruggel-Emden et al. (2011 a) [80] have also carried out CFD based

investigation for three different oxygen carrier materials (CaSO4, NiO and Mn3O4)

with the gaseous fuels (methane) in batch type reaction vessel. They used four

reaction models on case by case basis namely, linear shrinking core, spherical core,

Avarami-Erofeev, and multi-parameter model. Additionally, Kruggel-Emden et al.

(2011 b) [81] have advanced their earlier work by carrying out CFD based

investigation for four different oxygen carrier materials (CaSO4, CuO, NiO and

Mn3O4) for methane by taking above described four reaction models and compared

the results with published data.

Jin et al. (2009) [82] developed CFD model for chemical looping combustion

using hydrogen as fuel and CaSO4 as an oxygen carrier incorporating reaction kinetic

model. They studied the effects of partial pressure of hydrogen on the system

performance and concluded that higher partial pressure accelerated the reaction rate.

Lygfelt et al. (2001) [83] discussed the design of a boiler with chemical looping

combustion process with Fe2O3 and NiO as oxygen carriers. Their system comprises

two inter-connected fluidized bed reactors (one high velocity riser while the other is

low velocity bed).

Brahimi et al. (2012) [84] simulated chemical looping combustion for pure

methane as fuel and NiO as an oxygen carrier under various operating conditions.

Their mathematical model was based on the reaction kinetics and population balance

of oxygen carrier in each air and fuel reactor and derived proper operating condition

for complete utilization of fuel. Anthony (2008) [85] has examined the potential of

Page | 18

chemical looping combustion and lime-based carbon dioxide looping cycles in which

calcined limestone is used to capture in-situ carbon dioxide.

Han et al. (2013) [86] carried out their investigation using heterogeneous modeling

to study the intra-particle diffusion, temperature fluctuation and dispersion on fixed

bed reactor for chemical looping combustion. Their results were validated with the

Zhou et al. (2013) literature for methane as fuel and NiO as oxygen carrier. Though

in their study, they concluded that for an optimal particle size there is an enhancement

to overall reaction rate and reactor temperature. Wadhwani and Mohanty (2014) [87]

developed an approximate 2-D CFD model for a complete coal direct chemical

looping process for a 25 kWth pilot plant for sub-bituminous coal as fuel and ferric

oxide as oxygen carrier. Their study assumed gaseous flow of particles using

volumetric reaction in FLUENT. Their study verified the pilot plant data found in

Kim et al. (2013) [14] literature and estimated fuel conversion and carbon dioxide

purity in fuel reactor exhaust to be 89.81% and 88.98% respectively while in the

published literature the corresponding values were 97% and 99.6% respectively.

Li and Fan (2008) [88] discussed the background of the historical utilization of

coal as a source of energy and documented the progress and challenges of clean

conversion processes. They also discussed & illustrated the technology with ASPEN

Plus simulation data. Sarofim et al. (2011) [89] have carried out their ASPEN based

simulation study of chemical looping system for CuO and Fe2O3 as oxygen carriers

and optimized design considerations and also develop economic model. Xiang et al.

(2010) [90] analyzed a novel process comprising three reactors to generate hydrogen

and electricity from coal, based on chemical looping combustion. They carried out

ASPEN Plus based simulation using Fe2O3/FeAl2O4 as oxygen carrier and showed

carbon capture efficiency of 89.62% with CO2 emission of ~239 g/kWh.

Zhou et al. (2014) [91] carried out ASPEN Plus based analysis for chemical

looping combustion comprising entire process. The model showed that large amount

of energy is liberated in fuel reactor while there is a need to supply energy in air

reactor and thus evaluated the performance and efficiency of the modeled chemical

Page | 19

looping system. Gopaul (2014) [92] has carried out ASPEN Plus based study for the

chemical looping gasificaion of biomasss for syngas production and utilization of it in

solid-oxide fuel cell system. Their work was divided into three sections, the first

section of the process uses chemical looping gasification of biomass using CaO as an

oxygen carrier for carbon dioxide capturing and generation of lower yields of high

purity syngas, in the second section of the process, their work which used iron-based

oxygen carrier to produce higher yield of syngas with low purity did not involve

carbon dioxide capture.

Zeng et al. (2012) [93] have carried out ASPEN Plus reactor simulation model

based on kinetic and thermodynamic equilibrium limitations to analyze individual

reactors of coal direct chemical looping reactors. They estimated the performance of

coal direct chemical looping process under various mass and energy management

schemes. Guo et al. (2012) [94] have analyzed the behavior of CaSO4 as an oxygen

carrier for chemical looping combustion with gaseous fuel (i.e. CO, H2 and CH4) and

solid fuel (i.e. coal and biomass). Further, they carried out ASPEN Plus based

simulation study for a chemical looping process using CaSO4 as an oxygen carrier.

Li et al. (2010) [95] have carried out ASPEN Plus based study of biomass direct

chemical looping (BDCL) process based on solid biomass as fuel and Fe2O3 as an

oxygen carrier to generate hydrogen and electricity with high efficiency. In addition

to it, the facility for total capture of CO2 produced made it a carbon negative process.

Kobayashi and Fan (2011) [96] provided a perspective for the use of biomass as a

fuel in a similar technology to chemical looping combustion known as biomass direct

chemical looping. They discussed about the preliminary design of the process and its

potential problems and carried out feasibility assessment using ASPEN Plus.

2.3. Oxygen Carriers:

Since the proposal of chemical looping combustion was put forward by Lewis and

Gilliland for production of carbon dioxide in 1950 and by Richter and Knoche in

1983 for enhancement of power station efficiency, the major drawback identified for

the process is the slow reaction kinetics of metal oxide and carbonaceous fuel which

Page | 20

affects the performance of fuel reactor and ultimately the whole process. Oxygen

carriers during its evolution have passed through stages, in the first stage transition

metal oxides were used while, in the second stage of it development their oxygen

carrying capacity is significantly enhanced by doping with different compounds

forming doped oxygen carriers and bi-metallic oxygen carriers. The aboveefforts

have been discussed by Liu (2013) [97], Fan and Siriwardane (2014) [98],

Moghatedri (2012) [5], Cormos (2010) [99], Ciferno et al. (2009) [100], Hossain and

Lasa (2008) [101] and Rezvani et al. (2009) [102]. One of the most important

characteristics of a suitable oxygen carrier for chemical looping combustion is its

reactivity in both fuel and air reactor where both reduction and oxidation cycle take

place. Further, its ability to combust a carbonaceous fuel is an additional selection

criterion along with the following characteristics that are required for an effective

oxygen carrier:

High oxygen carrying capacity

Stable under repeated reduction and oxidation cycles at high temperature

Low attrition rate and Good mechanical strength

Eco-friendly and cost effective

High heat capacity and melting point

Fluidizable and resistant to agglomeration

There are several methods to produce oxygen carriers, out of these spray drying,

impregnation and granulation are the most popular approaches. The synthetic oxygen

carrier prepared from this approach are generally costly than the natural oxygen

carrier such as ilmenite which is cheap in comparison to synthetic oxygen carrier. The

synthetic oxygen carriers are provided with inert binding support such as alumina

(Al2O3), silica (SiO2), Yttria stabilized Zirconia (YSZ), Zirconia (ZrO2), etc. which

enhances the surface area of oxygen carriers.

One key parameter for oxygen carrier suitability is oxygen transport capacity, R0,

which is defined as the mass fraction of usable oxygen in oxygen carrier between air

reactor and fuel reactor:

Page | 21

Where, moxy is the mass of oxygen carrier in oxidized state and mred is the mass of

oxygen carrier in reduced state. The oxygen transport capacity of a few important

oxygen carrier combinations are shown in Fig. 2.1 and in Table 2.1 various type of

oxygen carrier used for study is detailed. From Table 2.1, it can be clearly seen that

Fe-, Ni- and Cu- based oxygen carriers have been prominently used in the study for

the development of chemical looping combustion. Table 2.2 compares suitability of

the most prominent used oxygen carriers i.e. Ni-, Fe- and Cu- based oxygen carriers.

Fig. 2.1: Oxygen transport capacity of some important oxygen carrier combinations

Table 2.1: Types of oxygen carriers researched for chemical looping combustion

Oxygen carrier

Authors

Iron

based

Nickel

based

Copper

based

Calcium

based

Cobalt

based

Manganese

based

During 1994

Ishida and Jin [103]

During 1996

Ishida et al. [104]

Ishida & Jin [105]

During 1997

Hatanaka et al. [106]

During 1998

Anheden & Svedberg [61]

Jin et al. [107]

0

0.1

0.2

0.3

0.4

0.5

Oxygen carrying capacity, Ro

Page | 22

Ishida et al. [108]

During 1999

Jin et al. [109]

Ishida et al. [110]

During 2001

Lygfelt et al. [83]

During 2003

Kronberger et al. [77]

During 2004

Jin & Ishida [54]

Kronberger et al. [35]

During 2006

Abad et al. [16]

Cao et al. [44]

Labino et al. [55]

Scott et al. [22]

During 2007

Shen et al. [45]

During 2008 Berguerand & Lyngfelt [34]

Berguerand & Lyngfelt [111]

Deng et al. [71]

Li & Fan [88]

Song et al. [33]

Tian et al. [112]

Yang et al. [43]

Zhao et al. [53]

During 2009

Deng et al. [70]

Gnanapragasam et al. [48]

Jin et al. [82]

Kolbitsch et al. [19]

Laihong et al.

Lieon et al. [18]

Rezvani et al. [102]

Shen et al. [51]

Siriwardane et al. [32]

During 2010

Balaji et al.

Cormos [99]

Dennis & Scott

Jheng et al. [49] Kruggel-Emden et al. [79]

Page | 23

Li et al. [95]

Siriwardane et al. [47]

Xiang et al. [90]

Xiao et al. [21]

Xiao et al. [31]

During 2011

Abad et al. [40]

Eyring et al. [113]

Gu et al. [17] Kruggel-Emden et al. [80]

Kruggel-Emden et al. [81]

Kobayashi & Fan [96]

Mahalatkar et al. [78]

Orcajo [114]

Penthor et al. [115]

Pröll et al. [20]

Saha et al. [57] Saha & Bhattacharya [56]

Schöny et al. [73]

Sharma et al. [75]

Shuai et al. [62]

Wang et al. [64]

Wang et al. [63]

Xiao & Song [46]

During 2012

Brahmi et al. [84]

Marx et al. [76]

Orr [52]

Guo et al. [94] Velazquez-Vargas et al. [29]

Yu et al. [41]

Zeng et al. [93]

During 2013

Arias et al. [24]

Azis et al. [25]

Bayham et al. [23]

Han et al. [86]

Kim et al. [14]

Liu [97]

Luo et al. [50]

Tong et al. [116]

Wang et al. [72]

Wang et al. [117]

Yu et al. [42]

Zaabout et al. [118]

Page | 24

During 2014

Bao et al. [26]

Bhavsar et al. [119]

Coppola et al. [39]

Daza et al. [120]

Duelli et al. [121]

Edrisi et al. [122]

Fan & Siriwardane [98]

Garderen et al. [123]

Gopaul [92]

Hamers et al. [58]

Jafarian et al. [67]

Källén et al. [27]

Kang et al. [38]

Ksepko [124]

Ma et al. [125]

Medrano et al. [68]

Mendiara et al. [28]

Schwebel et al. [69] Wadhwani & Mohanty [87]

Wang et al. [126]

Wang et al. [127]

Wang et al. [59]

Yahom et al. [128]

Zhang et al. [129]

Zhao et al. [130]

Zhao et al. [131]

Zhao & Ghoneim [132]

Zheng et al. [133]

Zheng et al. [134]

Zhou et al. [91]

Table 2.2: Comparison of Fe-. Ni- and Cu- based oxygen carriers

Oxygen Carrier

Parameters

Fe- based Ni- based Cu- based

Toxic effect No Very high No

Strength Good Low Fair

Cost Cheap Costly Costly

Melting point High Fair Low

Page | 25

Attrition rate Moderate Low High

Reactivity Slow Fast Fast

Further, Zhao et al. (2008) [53] have experimentally observed the direct reduction

reaction of NiO/NiAl2O4 oxygen carriers by coal char. They prepared NiO/NiAl2O4

particles by sol-gel method rather than dissolution method. TGA was used to evaluate

the reduction reaction through analyzing the weight of mixture as a function of time

& temperature; XRD, SEM & N2 adsorption–desorption methods were used to

characterize the solid residues. Tian et al. (2008) [112] have studied CuO/bentonite &

CuO-BHA nanocomposites as oxygen carrier using coal derived syngas for CLC and

found that reduction reactions were always faster than the oxidation reactions. They

observed excellent reaction performance and thermal stability for both Cu-based

oxygen carriers for chemical looping combustion at 700-900⁰C.

Ishida and Jin (1996) [105] have reported the results of a novel combustor by

kinetics and crystallographic study. They concluded that NiO as oxygen carrier mixed

with Yttria Stabilized Zirconia (YSZ) shown promising results as oxygen carrier with

respect to its reaction rate, conversion and physical strength. Daza et al. (2014) [120]

synthesized a new type of oxygen carrier for water-gas shift chemical looping using

Pechini method and examined it with XRD. They developed five different strontium-

doped lanthanum cobaltites, La1-xSrxCoO3-δ (0≤x≤1 in steps of 0.25). Their result

indicated that the suitability of perovskite-type of oxygen carrier for reverse water gas

shift chemical looping process.

Liu (2013) [97] has studied the doping of CeO2 in ferric oxide oxygen carrier for

chemical looping combustion. The reactivity of oxygen carriers was studied using

TGA-MS on a bench scale chemical looping combustion setup. The oxygen carriers

were analyzed by using XRD and Raman spectroscopy to model the promoting role

of CeO2 in ferric oxide reactivity for chemical looping combustion. The reaction

kinetics of oxygen carrier was studied using shrinking core model. Tong et al. (2013)

[116] have discussed the properties of oxygen carrier, reactor design and modeling,

Page | 26

results of bench scale model, and integrated energy optimization of the pilot plant

developed at Ohio State University. Fan and Siriwardane (2014) [98] carried out their

study to analyze several bimetallic oxygen carriers, such as- CoFe2O4, NiFe2O4,

CuFe2O4, MgFe2O4, CaFe2O4, SrFe2O4, BaFe2O4, and MnFeO3 using thermo-

gravimetric analysis. Their study concluded that all bimetallic ferrites have better

reduction rate than Fe2O3 while Group 2 elements ferrites showed better reduction

and oxidation rates than the transition-metal ferrites. In Group 2 element ferrites,

BaFe2O4 has highest reduction and oxidation rate which is comparable to CuO at

higher reaction temperatures.

2.4. Carbonaceous fuels:

The carbonaceous fuels are materials that can create usable amount of energy

through chemical reaction in controlled manner. They primarily burn off to give

carbon dioxide, steam and heat as major products. As shown in Fig. 2.2, chemical

looping combustion mainly uses solid and gaseous fuels. However, the use of liquid

fuels in chemical looping combustion is under progress.

Fig. 2.2: Classification of carbonaceous fuels used for chemical looping

combustion

Since past decade, gaseous fuels such as methane, natural gas, hydrogen and

syngas have been mainly targeted for chemical looping combustion. Even conversion

Chemical looping combustion

Solid fuels

Coal and coke

Petcoke and Char

Biomass

Gaseous fuels

Hydrogen Methane and Natural gas

Syngas

Page | 27

of coal to syngas for this purpose is not uncommon. The recent development in

chemical looping combustion utilizes coal as a fuel directly due to abundant coal

reserves which is sufficient for 200+ years to satisfy future energy demands. Coals

have wide variation in its properties from reserves to reserves even within the same

reserve the properties can vary. Further, the properties of coals vary over the time in a

same reserve. Various major coal reserves in the world have high ash content and

contents of ash also vary significantly which can be seen in Fig. 2.3 & 2.4 which

shows the variation in coal composition in Asia-Pacific and Australia. The depleting

reserves of low ash and low sulfur coal are forcing users to shift towards the coal

reserves that have high ash content and sulfur content. The ash affects the system by

melting due to high temperature in the reactor and this affects reactor performance. In

addition to it, amount of ash also affects the fuel reactor and cyclone exhaust gas

composition, cleaning cost and carbon dioxide capturing efficiency.

Fig. 2.3: Variation in coal compositions found in regions of Asia-Pacific and Australia

0 5

10 15 20 25 30 35 40 45 50 55 60 65

Moisture Ash % C % O % H % S % N

We

igh

t p

erc

en

tage

Page | 28

Fig. 2.4: Variation in Ash composition of coals found in regions of Asia-Pacific &

Australia

2.5. Properties Estimation by Neural Network:

For simulation of chemical looping combustion using FLUENT accurate properties

of material such as heat capacity which is a temperature dependent function is

required. Such correlations for non-database species are developed using neural

network and fit into Volterra functional series. Piotrowski et al. (2005) [135]

investigated the kinetics of reduction of ferric oxide to ferrous oxide using TGA to

obtain experimental data and then utilizing it developed an artificial neural network

model to obtain correct parameters for the complex heterogeneous reaction kinetics.

Gharbi et al. (1999) [136] carried out their study to predict the PVT properties of

crude oil sample using universal neural network, their study utilizes largest data set in

developing PVT predictions. Their study showed that the prospect of utilizing ANN

in predicting the PVT properties of crude once fully trained gives better result than

correlations.

Guo and Sha (2004) [137] developed ANN model for analysis and simulation of

the correlation between the properties of maraging steels and composition, processing

0 5

10 15 20 25 30 35 40 45 50 55 60

We

igh

t P

erc

en

tage

Page | 29

and working conditions. Their results were in agreement with the experimental data.

Malinov and Sha (2001) [138] have developed a model for the analysis and prediction

of correlation between heat treatment parameters and mechanical properties in

titanium alloys using artificial neural network technique. The model they used was

multilayer feed-forward neural network which was trained using comprehensive data

collected from literatures. Their model used to predict properties of titanium alloy at

different temperature as function of heat treatment and processing parameters.

Page | 30

CHAPTER 3 PROBLEM DESCRIPTION

The present study is divided into two segment, in the first segment CFD model

development is carried out using commercial software FLUENT 6.3.2 and mesh

generation using GAMBIT 2.3.26 for validation of the pilot plant data while in the

second segment the verified CFD model is utilized to model and study the behavior of

coals of Asia-Pacific and Australian origin. Further, the first segment of the study is

further sub-divided by using two parts consisting of two different set of reactions; the

first part of model validation utilizes eleven set of reactions as proposed by [14] while

the second part of model validation incorporates seven other significant reaction in

addition to those proposed by [14].

The geometrical as well as operating parameters of the 25 kWth pilot plant

developed by Ohio State University, USA and described by [14] has been considered

for the present CFD simulation. The pilot plant parameters and its geometry are

shown in Fig. 3.1 with dimension of different section in Table 3.1. For first segment

of the study has been used for model validation purpose, two fuels namely sub-

bituminous coal (SBC) and metallurgical coke (MC), whose properties are given in

Table 3.2 & 3.3, are used one at a time in the pilot plant along with ferric oxide as an

oxygen carrier whose properties are given Table 3.7. For second segment of the

study, four different coals, labeled as “A”, “B”, “C” and “D” and whose properties

are given in Table 3.4 & 3.5, of Asia-Pacific and Australia origin are used one at a

time with the same ferric oxide as an oxygen carrier. In Table 3.6 ash composition of

different coals labeled as “A”, “B”, “C” and “D” are given.

Page | 31

Fig. 3.1: Pilot plant of present problem

Table 3.1: Geometry Parameters

Fuel Reactor Height 3.37m

Fuel Reactor Diameter 0.34m

Air Reactor Height 1.88m

Air Reactor Diameter 0.33m

Tube Diameter 0.11m

Riser Section Height 4.68m

Cyclone Separator Total Height 0.62m

Cyclone Separator Diameter 0.28m

Table 3.2: Proximate Analysis of fuels for first segment of problem

Proximate Analysis (Dry Basis)

MC SBC

Page | 32

Ash 16.99% 11.38%

Volatile Matter 8.55% 39.57%

Fixed Carbon 74.47% 49.05%

Energy Value 28,108 kJ/kg 26,047 kJ/kg

Energy Value1 33,857 kJ/kg 29,391 kJ/kg

Average Particle Size 36.5 μm 89.8 μm

Moisture 2.69% 10.53%

1 moisture and ash free

Table 3.3: Ultimate Analysis of fuels for first segment of problem

Ultimate Analysis (Ash free Basis)

MC SBC

Carbon 75.89% 65.5%

Hydrogen 1.62% 4.41%

Nitrogen 0.78% 0.78%

Sulfur 0.5% 0.77%

Oxygen 4.22% 17.16%

Table 3.4: Proximate Analysis of fuels for second segment of problem

Proximate Analysis (Dry Basis)

Page | 33

A B C D

Ash 17.56% 48.9% 25.87% 31.5%

Volatile Matter 8.55% 20.4% 29.87% 7.5%

Fixed Carbon 74.47% 23.6% 42.86% 59.9%

Energy Value (kJ/kg) 7,981 12,189 26,120 23,398

Energy Value1 (kJ/kg) 9,997 15,236 30,729 27,527

Average Particle Size 45 μm 80 μm 125 μm 100 μm

Moisture 48.77% 11.99% 1.4% 1%

1 moisture and ash free

Table 3.5: Ultimate Analysis of fuels for second segment of problem

Ultimate Analysis (Dry Ash free Basis)

A B C D

Carbon 20.4% 30.82% 61.76% 56.7%

Hydrogen 1.89% 1.9% 4.16% 3.2%

Nitrogen 0.7% 0.6% 0.76% 0.9%

Sulfur 1.02% 0.24% 0.91% 0.6%

Oxygen 9.66% 5.55% 5.14% 6.1%

Table 3.6: Ash compositions of Fuels for second segment of problem

Page | 34

Components Ash Composition

A B C D

SiO2 50.01% 62.28% 54.18% 48.34%

Al2O3 10.58% 27.56% 32.84% 28.12%

Fe2O3 5.15% 4.89% 5.35% 11.88%

TiO2 0.78% 1.28% 2.27% 1.6%

CaO 25% 1.92% 1.57% 7.17%

SO3 2.8% 0.54% 1.47% 0.68%

MgO 4.26% 0.68% 0.53% 1.13%

Na2O 0.69% 0.17% 0.41% 0.6%

K2O 0.73% 0.66% 1.39% 0.48%

Table 3.7: Properties of oxygen carrier used for both segment of study

Reactive oxygen carrier Fe2O3

Weight content of reactive oxygen carrier 40-60%

Average particle size of oxygen carrier 120 μm

Supporting oxygen carrier Al2O3

Density of oxygen carrier 4724 kg/m3

Page | 35

CHAPTER 4 MODEL DEVELOPMENT

A 2-D CFD model for inter-connected fuel and air reactor is developed using

commercial computational software Fluent 6.3.2 and mesh for above assembly has

been developed using GAMBIT 2.3.16. The mixture (fuel and oxygen carrier)

containing solid particles in the range of 36-120 μm along with amount of gases

injected in the system as well as created from the reaction which amounts to about

75% by volume are assumed to flow as a fluid inside both the reactors and their inter-

connecting parts.

This assumption has been used for the development of an approximate CFD

model. The present study is carried out in two segments, while the first segment of

the study is divided using two parts consisting of two different set of reactions for

validation. In first segment of study, the first part of model validation is carried out by

considering first set of reactions which consists eleven reactions, as given in Table

4.1 and reported by [14], which occur inside the reactors and inter-connecting parts

and results thus obtained are subsequently used for first part of model validation. The

second part of model validation which is carried out using second set of reactions

which comprises above stated eleven reactions added with seven more significant

reactions (which are not considered by [14]), as given in Table 4.2. The second part

of model validation offered better agreement of pilot plant data than the first part of

model validation in the first segment of study. Before a complicated two phase CFD

model is selected for the analysis for the present problem, it is thought logical to use

the least complicated model, the Species-Transport model with volumetric reaction

for the present study and to find out the extent of agreement it offers to the pilot plant

data.

Once the CFD model is validated, in the first segment of study it is used in second

segment of study to investigate the effects of ash content in different coals

(designated as A, B, C and D). For this purpose eighteen reactions as used in second

part of model validation in the first segment of study, five more reactions of ash

Page | 36

components (discussed in Table 4.5) which become important due to high amount of

ash present in fuels used for chemical looping combustion components are used in the

present CFD model. Following governing equations are solved on commercial

available software Fluent 6.3.2 for the present model:

4.1: Mass Conservation Equation:

The equation for mass conservation/continuity equation can be written as:

..(4.1)

The mass conservation equation 4.1 is valid for compressible and incompressible

flows. The term Sm denotes the mass added to continuous phase from second phase or

any user-defined sources.

4.2: Momentum Conservation Equations:

In an inertial frame, the momentum conservation equation is described as below

equation 4.2:

..(4.2)

Where,

p is the static pressure,

is the stress tensor (described below),

and are the gravitational body force and external body forces, respectively. In

addition to it, term also contains other model-dependent source terms such as

porous-media and user-defined sources.

The stress tensor is given by equation 4.3

..(4.3)

Page | 37

Where, μ is the molecular viscosity, I is the unit tensor, and the second term on the

right hand side of equation 4.3 is the effect of volume dilation.

4.3: Energy Conservation Equation:

The conservation of Energy is defined by the following equation 4.4:

..(4.4)

Where,

keff is the effective conductive (k+kt , where kt is the turbule thermal conductivity,

defined according to the turbulence model being used),

is the diffusion flux of species j.

Sh is the heat of chemical reaction and any other volumetric source by used defined

function

In Equation 4.4,

..(4.5)

The sensible enthalpy is defined as:

For ideal gases as:

..(4.6)

and, for incompressible flows as:

..(4.7)

In equations (4.6) and (4.7), Yj is the mass fraction of species j and is hj at Tref =

298.15K is defined as:

Page | 38

..(4.8)

4.4: Species Transport Equations:

The local mass fraction of each species, Yi, through the solution of a convection-

diffusion equation for the ith

species is solved. It takes the following general form:

..(4.9)

Where,

Ri = net rate of production of species i by chemical reaction

Si = rate of creation by addition from dispersed phase plus any user defined

sources

Mass Diffusion in Laminar Flows:

In the above equation, 4.9, the term is the diffusion flux of the ith

species, which

arises due to concentration gradients. In the present model, dilute approximation is

assumed, under which it is defined as follows:

..(4.10)

Where, Di,m is defined as diffusion coefficient for the ith

species in the mixture

4.5: Laminar Finite-Rate Model:

The net source of chemical species ith

due to reaction is computed as the sum of the

Arrhenius reaction sources over the NR reactions that the species participate in:

..(4.11)

Where, Mw,i is the molecular weight of ith

species, is the Arrhenius molar rate

of creation/destruction of species ith

in reaction r.

Page | 39

Consider the rth

reaction written in general form as follows in equation 4.12 which

is valid for both reversible and non reversible reactions. For non-reversible reactions

the backward rate constant is omitted.

..(4.12)

where,

N = number of chemical species in the system

v’i,r = stoichiometric coefficient for reactant i in reaction r

v”i,r = stoichiometric coefficient for product i in reaction r

Mi = symbol denoting species i

kf,r = forward rate constant for reaction r

kb,r = backward rate constant for reaction r

The summations in Equation 4.12 are for all chemical species in the system, but

only species that appear as reactants or products will have non-zero stoichiometric

coefficients. Hence, species that are not involved will drop out of the equation.

For a non-reversible reaction, the molar rate of creation/destruction of species i in

reaction r is given by

.. (4.13)

where,

Cj,r = molar concentration of species j in reaction r (kmol/m3)

η'j,r = rate exponent for reactant species j in reaction r

η”j,r = rate exponent for product species j in reaction r

Page | 40

For a reversible reaction, the molar rate of creation/destruction of species i in

reaction r, is given by

..(4.14)

The rate exponent for the reverse reaction part in equation 4.14 is always the

product species stoichiometric coefficient (v”j,r).

Γ represents the net effect of third bodies on the reaction rate. This term is given by

..(4.15)

Where, γj,r is the third-body efficiency of the jth

species in the rth

The forward rate constant for reaction r, kf,r is computed using the Arrhenius

expression

..(4.16)

Where,

Ar = pre-exponential factor

Βr = temperature exponent

ER = activation energy for the reaction

R= universal gas constant

Values of v’i,r , v”i,r , η’j,r ,η”j,r , βr ,Ar and ER are provided to solve equation 4.13

For reversible reactions, the backward rate constant for reaction r, kb,r , is

computed from the forward rate constant using the following relation:

..(4.17)

Page | 41

Where, Kr is the equilibrium constant for the rth

reaction. The value of Kr is

computed from the following equation 4.18

..(4.18)

Where, patm denotes atmospheric pressure (101.325 kPa) and the term within the

exponential function represents the change in Gibbs free energy, and its components

are computed as follows:

..(4.19)

..(4.20)

Where, S0

i and h0

i are the standard-state entropy and standard-state enthalpy (heat

of formation) which are specified as properties for every species.

4.6: Reaction Kinetics:

It is carried out in two segments. The first segment is devoted to model validation

in two parts, the first part of model validation for fuel and air reactors and their inter-

connecting parts taking into account eleven reactions proposed by [14] and detailed in

Table 4.1 & 4.2, with that of pilot plant data. In second part of first segment model is

validated taking into account the eleven reactions discussed in first part along with

seven other significant reactions (detailed in Table 4.3 & 4.4) with that of pilot plant

data. In the later section of first segment of the study it is proved that the result of the

model with eighteen reactions agrees better with pilot plant data. In the second

segment of the investigation, the effects of ash on the chemical looping combustion

are incorporated through the reactions (presented in Table 4.5 & 4.6) of the

compounds present in ash. The ash components, discussed in Table 3.6, are divided

into two types, i.e. reactive ash and non-reactive ash. The reactive ash consists of

SiO2, CaO and Fe2O3 whereas non-reactive ash consists of inert alumina and very

Page | 42

small quantities of other reactive components whose reactions are ignored in the

present study.

Table 4.1: List of reactions proposed by [14] for first part of model validation in

first segment of study

Reaction Nos. Reactions

4.1 Coal Devolitilization:

4.1.1 For MC:

4.1.2 For SBC:

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

Page | 43

Table 4.2: Reaction Kinetics parameters for reactions discussed in Table 4.1

Reaction

Nos.

Activation Energy of

reactions (J/kmol)

Pre-exponential

Factor

βk References

4.1.1 7.74 × 107 3.82 0.5 Anthony & Howard (1976) [139]

4.1.2 1.14 × 108 111.3 1 Anthony & Howard (1976) [139]

4.2 3.0124 × 108 8.2 1 Rao (1971) [140]

4.3 1.352 × 108 9.8 0.5 Abad et al. (2011) [40]

4.4 8.07 × 107 0.1 0.5 Schwebel et al. (2014) [69]

4.5 6.5 × 107 0.062 0.5 Abad et al. (2011) [40]

4.6 1.205 × 107 7.44 0.5 Chatterjee (1993) [141]

4.7 2.151 × 107 9.5 0.5 Chatterjee (1993) [141]

4.8 2.11 × 108 0.8593 0.5 Roberts & Harris (2000) [142]

4.9 2.31 × 108 8.593 0.5 Roberts & Harris (2000) [142]

4.10 2.025 × 107 6.3 0.5 Grosvenor et al. (2005) [143]

4.11 2.55 × 107 0.0019 0.5 Abad et al. (2011) [40]

Table 4.3: Other significant reactions incorporated in second set of model validation in

first segment of study

Reaction Nos. Reactions

4.12

Page | 44

4.13

4.14

4.15

4.16

4.17

4.18

Table 4.4: Reaction Kinetics parameters for reactions discussed in Table 4.3

Reaction

nos.

Activation Energy of

reaction (J/kmol)

Pre-exponential

factor

βk References

4.12 1.5 × 108 2.337 × 10

-6 1 Tomita & Mahajan (1977) [144]

4.13 1.26 × 107 2780 0 Di Blasi (2000) [145]

4.14 3 × 107 312 0 Govind & Shah (1984) [146]

4.15 1.794 × 108 2.503 × 10

17 1 Perkins et al. (2003) [147]

4.16 1.674 × 108 3.98 × 10

19 0 Westbrook & Dryer (1984) [148]

4.17 7.79 × 107

1.2 0 Go et al. (2008) [149]

4.18 2.852 × 107

5.159 × 1020

0 Li et al. (2004) [150]

Table 4.5: Reactions of reactive ash components and fuels for second segment of study

Page | 45

Reaction nos. Reaction

4.1.3 For Coal ‘A’:

4.1.4 For Coal ‘B’:

4.1.5 For Coal ‘C’:

4.1.6 For Coal ‘D’:

4.19

4.20

4.21

4.22

4.23

4.24

Note: The reaction of Fe2O3 which is present in reactive ash as well as act as an oxygen

carrier has not been included in Table 4.5 as it is already in Table 4.1

Table 4.6: Reaction kinetic parameters for reactions discussed in Table 4.5

Reaction

Nos.

Activation Energy of

reactions (J/kmol)

Pre-exponential

factor

βk References

4.1.3 9.67 × 107

111.3 0.5 Anthony & Howard (1976) [139]

Page | 46

4.1.4 1.17 × 108 23.34 1 Strezov et al. (2000) [151]

4.1.5 8.5 × 107

0.7 1 Xiao et al. (2010) [21]

4.1.6 9.7 × 107

8.04 1 Strezov et al. (2000) [151]

4.19 1.59 × 108

0.52 0 Abanades et al. (2004) [152]

4.20 1.744 × 107

23.08 0 Nikulshina et al. (2007) [153]

4.21 9.92 × 106 0.001 0 Nikulshina et al. (2007) [153]

4.22 3.28 × 108 1.9 0 Weimer et al. (1993) [154]

4.23 3.82 × 108

2.4 0 Weimer et al. (1193) [154]

4.24 2.741 × 108

18.7 0 Vannice et al. (1986) [155]

4.7: Effect of pressure:

Lindemann form [156] is used in the present model, to represent the rate

expression in pressure dependent reactions which makes a reaction dependent of both

pressure and temperature. In Arrhenius form, the parameters for high pressure limit

(k) and low pressure limit (klow) are described as follows:

..(4.16)

..(4.21)

The net rate constant at any pressure is given by,

..(4.22)

While, pr is defined as,

Page | 47

..(4.23)

[M] is conc. of gas mixture, and function F is unity for Lindemann form.

4.8: Standard k-ε turbulence model:

The standard k-ε turbulence model described by Launder and Spalding in 1974 is

used for the present study.

Eq. 4.24 is described for turbulent kinetic energy k

..(4.24)

And equation 4.25 is described for the rate of dissipation ε

..(4.25)

Where, Gk represents generation of turbulence kinetic energy due to mean velocity

gradients which is calculated by Eq. 4.26

Gb represents the generation of turbulence kinetic energy due to buoyancy which is

calculated by Eq. 4.27

YM represents the contribution of the fluctuating dilation in compressible

turbulence to the overall dissipation rate, which is calculated by Sarkar and described

as in Eq. 4.28

C1ε, C2ε, C3ε are the constants (C1ε = 1.44, C2ε =1.92)

σk and σε are the turbulent prandtl number for k and ε respectively (σk =1, σε =1.3)

Sk and Sε are user defined source terms

..(4.26)

Page | 48

..(4.27)

Where, Prt is turbulent prandtl number for energy = 0.85

gi is gravitational vector in the ith

direction

β is coefficient of thermal expansion

..(4.28)

Where Mt is turbulent Mach number

and

Modeling the Turbulent Viscosity

The turbulent viscosity μt is calculated from Eq. 4.29

..(4.29)

Where, Cμ is a constant and Cμ = 0.09

4.9: The Group Method of Data handling based model

For development of temperature dependent correlation for heat capacity of species,

group model data handling based model is used. In this method the connection

between input-output variables can be approximated by Volterra functional series

[157] as shown below:

y = a0 + ∑ ai xi + ∑ ∑ aij xi xj + ∑ ∑ ∑ aijk xi xj xk +..... ..(4.30)

Where, x=(x1, x2... xm) the input variables vector, ai the vector of weights and value

of i,j,k respectively varies from 1 to m while y is the output variable(s).

During the modeling procedure, GMDH algorithm involves four heuristics rules as

described below:

Page | 49

(i) The first heuristic is to collect a set of observations that seems to be

relevant to the object.

(ii) Then, it divides the observations into two groups. The first will be used to

estimate the coefficients of model while the second will separate the

information embedded in the data into either useful or harmful.

(iii) After that it creates a set of elementary functions where complexity will

increase through an iterative procedure producing different models.

(iv) According to Gödel’s incompleteness theorem discussed in [158], which

applies an external criterion to choose the optimum model.

Ivakhnenko (1968) [159] claims that the self-organization is necessary if it is

impossible to trace all the input-output relationships through an entire system which

is too complex. This ability made GMDH algorithms appropriate modeling procedure

for real world systems.

The wide development in GMDH theory has led to a wide range of algorithms

with each one of them corresponding to specific conditions of a particular application.

The choice of the algorithm depends on the level of noise in the data, their type (e.g.

continuous or discrete). The rapid development of GMDH theory and the broad

spectrum of its algorithms have resulted to a different classification approach where

GMDH methods are grouped into two main categories, the parametric and non-

parametric algorithms. Parametric algorithms are recommended to describe systems

characterized by either exact or low variance noisy data. On the other hand, in the

case of ill-defined systems and high variance noisy data, the application of non-

parametric algorithms is justified. The details of GMDH algorithm can be found in

Anastasakis et al. (2001) [157].

Using above model, correlation between heat capacity (Cp in J/kg-K) and absolute

temperature (T in Kelvin) is developed from the data obtained from NIST database

for the non-database species of FLUENT and are discussed in Table 4.7.

Page | 50

Table 4.7: Developed correlation between Cp (J/kg-K) and T (K) using group

model data handling

Species Correlation RMSE

CaO

12.76

Ca(OH)2

0.933

Fe

8.653

FeO

0.083

Fe2O3

8.957

SiC

12.83

SiO

0.711

SiO2

0.905

Page | 51

CHAPTER 5 SOLUTION TECHNIQUES

In this Chapter, solution technique adopted for the present study is described. The

plant dimensions are taken from the mechanical drawing of the pilot plant described

in [14]. The boundary condition for air inlet and coal inlet are defined as velocity inlet

and mass flow inlet and for fuel reactor exhaust and cyclone exhaust as pressure

outlets. Unsteady state simulations are carried out for present study and a time step of

0.001s is chosen for mesh grid size obtained from grid independency test which is

0.01(m).

Table 5.1: Grid Independence Test Details

Grid Size (m) Number of cells Number of nodes Quality

0.05 1221 1549 0.8143

0.01 31826 33460 0.9287

0.005 127703 130970 0.9303

Fig. 5.1: Mass Fraction of Reducer Exhaust Species vs. Number of Cells (For MC)

0.00

0.10

0.20

0.30

0.40

0.50

1,221 31,826 127,703

H2O

CO2

Fe2O3

C

CO

Page | 52

Table 5.2: Computational and Simulation Parameters for the Present Study

Parameters Value

Air Inlet Boundary Condition Velocity Inlet

Coal Inlet Boundary Condition Mass Flow Inlet

Reducer Exhaust Boundary Condition Pressure Outlet

Cyclone Exhaust Boundary Condition Pressure Outlet

Grid Size 0.01(m)

Under Relaxation Factors

Pressure 0.1 Energy 0.1

Momentum 0.1 Density 0.1

Species 0.1 Turbulent Kinetic Energy 0.1

Body Forces 0.1 Turbulent Dissipation Rate 0.1

Model Parameters

Solver Unsteady State, 2nd order implicit

Discretization Scheme Second order Upwind

Pressure Velocity Coupling SIMPLE

Time step 0.01s

Iteration per time step 30

Convergence Criterion 10-5

Page | 53

Discretization

In computational mathematics, discretization means the process of transferring

continuous model and equations into discrete counterparts. This process is usually

carried out as a first step toward making them suitable for numerical evaluation and

implementation on computers. In present study, following discretization methods are

being used:

5.1Finite Volume method

Finite volume method (FVM) is one of the standard approach used often in

commercial software such as ANSYS Fluent, OpenFOAM, etc. and research codes.

The governing equations are solved on discrete control volumes which refer to the

small volume surrounding each node point on a mesh. In this method, partial

differential equations (PDEs) of Navier-Strokes equations are recasts into

conservation equations and then these equations are discretized.

In this method, volume integrals in a PDE that contains a divergence term are

converted to surface integrals using divergence theorem. These terms are evaluated as

fluxes at the surface of each finite volume, as entering flux in a finite volume is equal

to leaving flux of the adjacent finite volume.

The advantage of using this method is that it is easily formulated to allow for

unstructured meshes which guarantee the conservation of fluxes through a particular

control volume. Though the overall solution will be conservative in nature there is no

guarantee that it is the actual solution. Moreover, FVM is sensitive to distorted

elements which can prevent convergence if such elements are in critical flow regions.

Where, Q is the vector of conserved variables, F is the vector of fluxes, V is

volume of finite element and A is the surface area of finite volume

5.2 Spatial Discretization

Page | 54

In default situation, ANSYS FLUENT stores discrete values of the scalar φ at the

cell centers. However, face values φf, are also required for the convection terms and

must be interpolated from the cell center values (φ). This is accomplished using an

upwind scheme, in which the face values φf, are derived from quantities in the cell

upstream or “upwind”, relative to the direction of the normal velocity un. FLUENT

have several upwind schemes: first-order upwind, second-upwind, QUICK, and

power-law. In the present study, second-order upwind is utilized for determination of

face values of the cells.

Second order upwind

When second-order accuracy is desired, quantities at cell faces are computed using

a multidimensional linear reconstruction approach. In this approach, higher-order

accuracy is achieved at cell faces through a Taylor series expansion of the cell-

centered solution about the cell centroid. Thus, when second-order upwind is

selected, the face value φf is computed using following equation:

Where, ϕ and ϕ are the cell-centered value and its gradient in the upstream cell,

and is the displacement vector from the upstream cell centroid to the face centroid.

5.3 Temporal Discretization

For transient simulation study, the governing equations must be discretized into

both space and time. The spatial discretization for time dependent equations is similar

to steady state case. In this discretization method, involves the discretization of every

term in differential equation over a time step of Δt which is described by following

equation:

Where, the function “F” incorporates spatial discretization

Page | 55

After the time derivatives has been discretized then the choice remains on

evaluating F (φ) to choose which time level values (i.e., implicit or explicit time

integration method) of φ should be used in evaluating F. For the present CFD model

implicit time integration method is used. The computation is said to be explicit when

a direct computation of the dependent variables can be made in terms of known

variables. When dependent variables are defined by coupled sets of equations and

either an iterative technique or a matrix technique is required to obtain solution, the

numerical method is called as implicit method.

Implicit method:

In this, we use backward difference at time t= n+1 and a second order central

difference for the space derivative at position x=j and we get the recurrence equation:

This is an implicit method for solving the 1-D heat equation. We can obtain ujn

from solving a system of linear equations:

The above discussed implicit method is always numerically stable and convergent

but usually more numerically intensive than the explicit method as it requires solving

a system of numerical equations on each time step. In implicit method, the errors are

linear over time step while they are quadratic on the space step.

j -1, n+1 j, n+1 j+1, n+1

j, n

Page | 56

Algorithm of Present study

Start Boundary conditions & Initialization of problem

Updated Data

Momentum and Continuity Equation

Species-Transport Equation

Energy Equation

Kinetic energy equation (k)

Dissipation Energy equation (ε)

Check for convergence

Solution

Yes No

Page | 57

CHAPTER 6 RESULTS & DISCUSSION

In this Chapter, results of CFD model developed in present investigation is

presented and discussed. In the present work 2D CFD simulation for a coal direct

chemical looping process based on the geometrical and operating parameters of a

25kWth pilot plant described by [14] has been carried out in two segments, first part

of the first segment is devoted to validation of data of a chemical looping pilot plant

with its published data with present CFD model and the second segment of the

investigation is an extension of present CFD model to study the effect of ash on

chemical looping combustion. The first segment of study for model validation is sub-

divided into two parts; In the first part the CFD model is validated using eleven

reactions as proposed by [14] while in the second part, model is validated using

eighteen reactions (eleven reactions proposed by [14] and seven other reactions). The

physical dimension of the pilot plant is given in Tables 3.1. The present CFD model

is described in Chapter 4 and it solution procedure is given Chapter 5.

6.1: Results of First Segment (Model Validation)

In this section the present CFD model is validated for two fuels namely MC and

SBC which are used in the pilot plant [14] and are discussed in Table 3.2 & 3.3.

6.1.1: Comparison of Simulation Results of the present model and that of pilot

plant when Metallurgical coke (MC) is used:-

The process parameters used in the present study for MC are discussed in Table

6.1.

Table 6.1: Process parameters for MC for Model Validation

Fuel flow rate 1.18 kg/hr

Carrier CO2 gas flow rate 10 LPM

Air flow rate 0.0005m/s

Page | 58

Inlet Fuel and Air Temperature 320K

Operating Pressure 10 atm

In Table 6.2, mass weighted average rate of reactions that are already discussed in

Table 4.1 & 4.3 are computed using the CFD model for the first part of model

validation and for second part of model validation for MC. From the Table 6.2, it

could be seen that in first part of model validation, reactions denoted by numbers

4.1.1, 4.1.1, 4.10, and 4.7 are prevailing in fuel reactor, inter-connecting parts, air

reactor and riser section of the process respectively whereas, for the second part of

model validation, reactions denoted by numbers 4.1.1, 4.14, 4.15 and 4.13 which are

altogether different than previous except reaction 4.1.1 are prevailing in fuel reactor,

inter-connecting parts, air reactor and riser sections respectively. The predominance

of reaction nos. 4.14 (steam reforming) at inter-connecting parts, 4.15 (combustion of

left over carbon) at air reactor and 4.13 (reverse water gas-shift reaction) at riser

section of the process are due to presence of higher temperature at these locations in

comparison to first model validation.

Table 6.2: Mass weighted average rate of reactions for MC for the first part of model

validation and second part of model validation in different section of the pilot plant

Reaction

number

Mass weighted

average Rate of

Reaction in Fuel

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in inter-

connecting pipe

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in Air

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in riser

section (kmol/m3-

s)

4.1.1 8.16 × 10-4

(1.03 × 10-2

)*

2.68 × 10-8

(7.448 × 10-5

)*

0

(0)*

0

(0)*

4.2 7.19 × 10-13

1.42 × 10-12

0 0

Page | 59

(1.09 × 10-11

)* (2.35 × 10-13

)* (0)* (0)*

4.3 1.45 × 10-9

(6.10 × 10-8

)*

0

(0)*

0

(0)*

0

(0)*

4.4 9.59 × 10-10

(6.95 × 10-9

)*

0

(0)*

0

(0)*

0

(0)*

4.5 1.26 × 10-15

(3.07 × 10-8

)*

9.34 × 10-15

(0)*

0

(0)*

0

(0)*

4.6 0

(1.83 × 10-9

)*

9.79 × 10-10

(1.56 × 10-10

)*

5.76 × 10-13

(2.92 × 10-11

)*

1.49 × 10-19

(3.89 × 10-20

)*

4.7 0

(2.31 × 10-9

)*

3.31 × 10-15

(1.47 × 10-9

)*

8.36 × 10-18

(2.78 × 10-10

)*

4.82 × 10-19

(1.32 × 10-11

)*

4.8 4.80 × 10-13

(3.09 × 10-11

)*

1.18 × 10-12

(8.46 × 10-13

)*

3.45 × 10-21

(2.11 × 10-13

)*

1.57 × 10-33

(8.94 × 10-30

)*

4.9 3.01 × 10-12

(1.52 × 10-10

)*

0

(0)*

0

(0)*

0

(0)*

4.10 0

(0)*

0

(0)*

3.81 × 10-10

(3.58 × 10-12

)*

2.13 × 10-17

(6.80 × 10-12

)*

4.11 0

(0)*

0

(0)*

7.49 × 10-14

(2.01 × 10-14

)*

3.62 × 10-16

(2.17 × 10-13

)*

4.12 -

(3.22 × 10-14

)*

-

(1.53 × 10-14

)*

-

(8.66 × 10-16

)*

-

(3.64 × 10-36

)*

Page | 60

4.13 -

(4.29 × 10-5

)*

-

(1.21 × 10-5

)*

-

(-3.27 × 10-4

)*

-

(-6.31 × 10-5

)*

4.14 -

(5.40 × 10-4

)*

-

(1.22 × 10-4

)*

-

(5.06 × 10-5

)*

-

(1.92 × 10-5

)*

4.15 -

(0)*

-

(0)*

-

(3.24 × 10-4

)*

-

(1.29 × 10-23

)*

4.16 -

(0)*

-

(0)*

-

(4.87 × 10-7

)*

-

(7.76 × 10-26

)*

4.17 -

(7.21 × 10-13

)*

-

(4.05 × 10-13

)*

-

(6.91 ×10-14

)*

-

(3.60 × 10-15

)*

4.18 -

(0)*

-

(0)*

-

(2.63 × 10-6

)*

-

(2.49 × 10-29

)*

*Simulated results of second part of model validation are shown in parentheses ()

The contours of rate of reactions for first part of model validation and second part

of model validation for MC having mass weighted average rate of reactions greater

than 10-10

are shown in Fig. 6.1 and Fig.6.2. From Fig, 6.1 and Table 6.1 for first part

of model validation for MC, it is clear that coal devolatilization (Reaction no. 4.1.1) is

the major reaction taking in fuel reactor and inter-connecting part while oxidation of

iron (Reaction no. 4.10) is the major reaction taking place in air reactor and oxidation

of ferrous oxide (Reaction no. 4.11) is the major reaction taking place in the riser

section of the present coal direct chemical looping pilot plant. In addition to it, from

Table 6.1 it can be concluded that formation of hydrogen (through Reaction no. 4.9)

in very small quantity in fuel reactor section which significantly effects the reduction

reaction by hydrogen (Reaction nos. 4.5 & 4.7) in the same section.

Page | 61

From Table 6.1 and Fig. 6.2, for second part of model validation for MC, it can be

seen that the coal devolatilization (Reaction no. 4.1.1) is the major reaction taking

place in fuel reactor while steam reforming (Reaction no. 4.14) is the major reaction

taking place in the inter-connecting part which joins the two reactors where due to

higher temperature methane & water reacts. In the air reactor reverse water-gas shift

reaction and burning of left over carbon (Reactions 4.13 & 4.15) are predominant

reactions in comparison to oxidation of iron/ferrous oxide to ferric oxide (Reaction

Nos. 4.10 & 4.11) as can be seen from the mass weighted average rate of reactions

values given for above reactions in Table 6.1. In addition to it, from Fig 6.2 (j), (k),

(l), (m), (n) and (o) it is clear that burning of carbon, oxidation of carbon monoxide

and reaction of hydrogen and oxygen are instantaneous reactions and take place at

entrance of air reactor.

(a) Contour for Reaction 4.1.1

(b) Contour for Reaction 4.3

(c) Contour for Reaction 4.4

Page | 62

(d) Contour for Reaction 4.6

(e) Contour for Reaction 4.10

Fig. 6.1: Rate of Reactions profiles for First part of model validation having mass weighted

average rate of reaction greater than 10-10

for MC

(a) Contour of Reaction 4.1.1

(b) Contour of Reaction 4.3

(c) Contour of Reaction 4.4

Page | 63

(d) Contour of Reaction 4.5

(e) Contour of Reaction 4.6

(f) Contour of Reaction 4.7

(g) Contour of Reaction 4.9

(h) Contour of Reaction 4.13

(i) Contour of Reaction 4.14

Page | 64

(j) Contour of Reaction 4.15

(k) Zoom of Reaction 4.15

(l) Contour of Reaction 4.16

(m) Zoom of Reaction 4.16

(n) Contour of Reaction 4.18

(o) Zoom of Reaction 4.18

Fig. 6.2: Rate of Reactions profiles for Second part of model validation having mass

weighted average rate of reaction greater than 10-10

for MC

In Fig. 6.3, velocity and temperature profiles of the system for second part of

model validation are shown. The temperature profiles (Fig. 6.3 (b)) exhibits some

Page | 65

discontinuity in the fuel reactor which is due to the presence of fuel inlet, marked as

"A" in the Fig. 3.1. Fig. 6.4 shows the molar concentration of important species in

second part of model validation when eighteen reactions are employed. The presence

of very low quantity (almost absence) of nitrogen and oxygen in fuel reactor meets

the objective of chemical looping process which avoids the mixing of nitrogen and

carbon dioxide to reduce the energy penalty in separation of fuel reactor exhaust. The

presence of slight amount of carbon and carbon monoxide in the air reactor due to

seepage of left over carbon, carbon monoxide and hydrogen from the fuel reactor via

inter-connecting pipe justifies the inclusion of reaction Nos. 15, 16 and 18. The mass

average velocities for fuel reactor exhaust for first part of model validation and that of

second part of model validation are 8.463 m/s and 5.871 m/s respectively. Fig. 6.5

shows the molar concentration of MC with time along the pathline trajectory from

fuel inlet to fuel reactor outlet for both model validations and shows that though the

outlet concentrations of MC for both models are almost equal, the decreased velocity

of MC through fuel reactor exhaust indicates that the conversion of MC in the later

model (model used in second part) is higher.

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.3: Contours of Velocity & Temperature for MC for second part of model validation

Page | 66

(a) Contour of water

(b) Contour of oxygen

(c) Contour of Nitrogen

(d) Contour of Iron (e) Contour of ferrous oxide (f) Contour of ferric oxide

Page | 67

(g) Contour of methane (h) Contour of hydrogen (i) Contour of carbon

(j) Contour of carbon

monoxide

(k) Contour of carbon

dioxide

(l) Contour of MC

Fig. 6.4: Molar concentration contour of important species for MC for second part of model

validation

Page | 68

Fig.6.5: Molar concentration of MC with time for both models along the pathline

trajectory

From Fig. 6.5, it can be computed that the predicted conversion of fuel (MC) is

higher (87.07%) for the second set of reactions (used in second part of model

validation) whereas for the first set of reactions it is 83.24% demonstrating an

improvement of about 4%. Table 6.3, shows the comparison between the model

predictions when both sets of reactions (second part of model validation) are

considered and that of pilot plant experimental results which clearly shows that for

second model which consists of eighteen reactions, predictions are within an error

band of +12% to -12% whereas, for the first model which consists of eleven reactions

these are within +16% to -13%. It clearly indicates that the second model which

involves eighteen reactions predicts the pilot plant data better.

Table 6.3: Verification of present CFD model for MC

Parameter Predicted Values

for First part of

model validation

Predicted Values

for Second part of

model validation

Pilot plant

values

Error in

First

model

Error in

Second

model

Fuel

Conversion 1

83.24% 87.07% 70-99% 15.91% 12.05%

0

0.02

0.04

0.06

0 1 2 3 4 5 6 7 Mo

lar

Co

nce

ntr

atio

n o

f M

C

Time (seconds)

Molar Concentration of MC for first model validation

Molar concentration of MC for second model validation

Page | 69

Fuel Reactor

Exhaust Mole

Fraction2

CO2 86.27% 90.19% 99.8% 13.56% 9.63%

CO 0.153 0.146% 0.14% -9.29% -4.28%

CH4

0.053 0.058% 0.06% 11.66% 3.33%

Cyclone

Exhaust

Mole

Fraction

O2 21.89% 21.02% 19.5% -12.26% -7.79%

CO2 0.065% 0.079% 0.07% 7.14% -11.39%

CH4 0.0134% 0.0167% 0.015% 10.66% -11.33%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.1.2: Comparison of Simulation Results of the present model and that of pilot

plant when Sub-bituminous coal (SBC) is used:-

The process parameters used in the present study for SBC are discussed in Table

6.4.

Table 6.4: Process parameters for SBC for Model Validation

Fuel flow rate 1.3 kg/hr

Carrier CO2 gas flow rate 10 LPM

Air flow rate 0.0005m/s

Page | 70

Inlet Fuel and Air Temperature 320K

Operating Pressure 10 atm

In Table 6.5, mass weighted average rate of reactions discussed in Table 4.1 & 4.2

are computed from the CFD model for the first part of model validation and second

part of model validation for SBC. It should be noted that for the first part of model

validation the model which uses 11 reaction will be henceforth called first model and

that for the second part the model which uses 18 reactions will be called second

model. From Table 6.5, it could be seen that for first part of model validation,

reaction nos. 4.1.2, 4.6, 4.10 and 4.6 are prevailing in fuel reactor, inter-connecting

parts, air reactor and riser section of the process respectively whereas, when the

second part of model validation is considered then reaction number 4.1.2, 4.8, 4.15

and 4.14 which are altogether different than previous except reaction 4.1.2 are

prevailing in fuel reactor, inter-connecting parts, air reactor and riser section

respectively. In the inter-connecting part steam reforming (Reaction no. 4.14) is

taking place and at the same place due to the presence of excess carbon monoxide

reduction of iron oxide (Reaction No. 4.8) is favored. Further, burning of left over

carbon on oxygen carrier (Reaction No. 4.15) dominates steam reforming reaction by

order of magnitude in the air reactor while steam reforming plays dominance role in

the riser section.

Table 6.5: Mass weighted average rate of reactions for SBC for the first and second part of

model validation in different section of the pilot plant

Reaction

number

Mass weighted

average Rate of

Reaction in

Fuel reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in inter-

connecting pipe

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in Air

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in riser

section

(kmol/m3-s)

4.1.2 1.3 × 10-4

(3.48 × 10-3

)*

2.8 × 10-7

(9.73 × 10-6

)*

0

(0)*

0

(0)*

Page | 71

4.2 1.25 × 10-5

(4.94 × 10-5

)*

4.94 × 10-7

(2.25 × 10-5

)*

0

(0)*

0

(0)*

4.3 3.37 × 10-7

(7.04 × 10-9

)*

0

(0)*

0

(0)*

0

(0)*

4.4 3.04 × 10-7

(1.91 × 10-9

)*

2.58 × 10-7

(0)*

0

(0)*

0

(0)*

4.5 3.22 × 10-14

(1.65 × 10-8

)*

4.54 × 10-14

(0)*

0

(0)*

0

(0)*

4.6 0

(9.17 × 10-5

)*

5.38 × 10-3

(4.59 × 10-5

)*

5.01 × 10-5

(3.26 × 10-7

)*

1.87 × 10-6

(7.09 × 10-10

)*

4.7 0

(2.98 × 10-11

)*

2.00 × 10-10

(2.66 × 10-5

)*

1.47 × 10-11

(1.79 × 10-6

)*

3.13 × 10-14

(5.51 × 10-9

)*

4.8 3.31 × 10-5

(3.77 × 10-4

)*

1.43 × 10-3

(1.97 × 10-4

)*

0

(7.84 × 10-9

)*

0

(3.34 × 10-21

)*

4.9 2.91 × 10-10

(8.34 × 10-12

)*

0

(0)*

0

(0)*

0

(0)*

4.10 0

(0)*

0

(0)*

5.52 × 10-5

(2.08 × 10-9

)*

5.34 × 10-7

(1.45 × 10-10

)*

4.11 0

(0)*

0

(0)*

5.65 × 10-8

(1.50 × 10-10

)*

5.91 × 10-9

(8.43 × 10-11

)*

4.12 -

(3.12 × 10-15

)*

-

(5.04 × 10-15

)*

-

(9.72 × 10-17

)*

-

(9.75 × 10-39

)*

4.13 -

(7.57 × 10-4

)*

-

(-1.09 × 10-5

)*

-

(-6.34 × 10-5

)*

-

(-1.41 × 10-6

)*

4.14 - - - -

Page | 72

(2.71 × 10-4

)* (1.07 × 10-4

)* (9.36 × 10-5

)* (2.56 × 10-6

)*

4.15 -

(0)*

-

(0)*

-

(8.76 × 10-4

)*

-

(2.04 × 10-30

)*

4.16 -

(0)*

-

(0)*

-

(6.89 × 10-7

)*

-

(1.22 × 10-15

)*

4.17 -

(2.58 × 10-9

)*

-

(5.03 × 10-9

)*

-

(6.46 × 10-11

)*

-

(1.04 × 10-9

)*

4.18 -

(0)*

-

(0)*

-

(3.6 × 10-4

)*

-

(1.04 × 10-9

)*

*Simulated results of second part of model validation are shown in parentheses ()

The contours of rate of reactions for first part of model validation and second part

of model validation for SBC having mass weighted average rate of reactions greater

than 10-10

are shown in Figs. 6.6 and 6.7. In first part of model validation, from Table

6.5 & Fig. 6.6, it can be concluded that coal devolatilization (Reaction no. 4.1.2) is

the major reaction taking in fuel reactor, reduction of ferric oxide & ferrous oxide are

the dominating reaction in inter-connecting pipe while oxidation of iron is the major

reaction in the air reactor. In the second part of model validation, it indicates that coal

devolatilization reaction (Reaction no. 1.2) is the major reaction that is taking place in

the fuel reactor, while, carbon monoxide reduction of ferrous oxide as well as steam

reforming reaction is taking place in the inter-connecting part joining two reactors

due to the presence of good amount of methane & water at favorable high

temperature. In the air reactor, reaction no. 15 (burning of left over carbon) becomes

predominant reaction in comparison to oxidation of iron/ferrous oxide to ferric oxide

(Reaction Nos. 10 & 11) as can be seen from the mass weighted average rate of

reactions values given for above reactions in Table 6.5. For the second part of model

validation, Fig. 6.8 shows velocity & temperature profiles of the chemical looping

system while, Fig. 6.9, shows the molar concentration of important species. The

presence of very low quantity of nitrogen & oxygen in fuel reactor meets the

Page | 73

objective of chemical looping process which inhibits mixing of nitrogen with carbon

dioxide to avoid energy penalty in separation fuel reactor exhaust gases. The presence

of slight amount of carbon & carbon monoxide in the air reactor, due to seepage of

left over carbon & carbon monoxide from the fuel reactor, justifies the inclusion of

reaction nos. 15 & 16.

(a) Contour of Reaction 4.1.2

(b) Contour of Reaction 4.2

(c) Contour of Reaction 4.3

(d) Contour of Reaction 4.4

(e) Contour of Reaction 4.6

(f) Contour of Reaction 4.7

Page | 74

(g) Contour of Reaction 4.8

(h) Contour of Reaction 4.9

(i) Contour of Reaction 4.10

(j) Contour of Reaction 4.11

Fig. 6.6: Rate of Reactions profiles for first part of model validation having mass weighted

average rate of reaction greater than 10-10

for SBC

Page | 75

(a) Contour of Reaction 4.1.2

(b) Contour of Reaction 4.2

(c) Contour of Reaction of 4.3

(d) Contour of Reaction 4.4

(e) Contour of Reaction 4.5

(f) Contour of Reaction 4.6

Page | 76

(g) Contour of Reaction 4.7

(h) Contour of Reaction 4.8

(i) Contour of Reaction 4.10

(j) Contour of Reaction 4.11

(k) Contour of Reaction 4.13

(l) Contour of Reaction 4.14

Page | 77

(m) Contour of Reaction 4.15

(n) Contour of Reaction 4.16

(o) Contour of Reaction 4.17

(p) Contour of Reaction 4.18

Fig. 6.7: Rate of Reactions profiles for second part of model validation having mass

weighted average rate of reaction greater than 10-10

for SBC

Page | 78

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.8: Contours of Velocity & Temperature for SBC for second part of model validation

(a) Contour of water

(b) Contour of oxygen

(c) Contour of nitrogen

Page | 79

(d) Contour of iron

(e) Contour of ferrous oxide

(f) Contour of ferric oxide

(g) Contour of methane

(h) Contour of hydrogen

(i) Contour of carbon

Page | 80

(j) Contour of carbon

monoxide

(k) Contour of carbon dioxide

(l) Contour of SBC

Fig. 6.9: Molar concentration contour of important species for SBC for second part of

model validation

Fig. 6.10: Molar concentration of SBC with time along the pathline trajectory for both

models

0

0.002

0.004

0.006

0.008

0.01

0 1 2 3 4 5 6 7 8 Mo

lar

Co

nce

ntr

atio

n o

f SB

C

Time (seconds)

Molar concentration of SBC for first part of model validation

Molar concentration of SBC for second part of model validation

Page | 81

The mass average velocities for fuel reactor outlet for first and second part of

model validation are 3.044 m/s and 7.787 m/s respectively. Fig. 6.10 shows the molar

concentration of SBC with time along the pathline trajectory for both models from

fuel inlet to fuel reactor outlet and shows that though the outlet concentrations of SBC

for both sets are different, the conversion of fuel (SBC) in the later model (model

used in second part) is higher. From Fig. 6.10, it can be computed that the predicted

conversion of fuel (SBC) is higher (95.39%) for the second model whereas for the

first model it is found to be 89.81 % indicating an improvement of about 6%. Table

6.6, shows the comparison between the model predictions when both sets of reactions

are considered with that of pilot plant results and reveals that for second model

comprising eighteen reactions predictions are within an error of +3% to +11%

whereas, for the first model which includes eleven reactions it is within +9% to

+11%. However, considerable errors are observed between simulation results and that

of pilot plant data especially for species having minor concentrations (<0.3%) in fuel

reactor exhaust and cyclone exhaust. It clearly indicates that the second model of

present investigation which considers eighteen reactions predicts the pilot plant data

better.

Table 6.6: Verification of present CFD model for SBC

Parameter Predicted Values

for first part of

model validation

Predicted Values

for second part of

model validation

Pilot

plant

values

Error

in first

model

Error in

second

model

Fuel Conversion1 89.81% 95.39% 97-99% 9.28% 3.64%

Fuel Reactor Exhaust

Mole Fraction2

CO2 88.98% 92.34% 99.6% 10.66% 7.28%

CO 0.067% 0.091% 0.08% 16.25% -13.75%

Page | 82

CH4

0.219% 0.241% 0.25% 12.4% 3.6%

Cyclone Exhaust

Mole Fraction

O2 16.82% 16.49% 18.5% 9.08% 10.86%

CO2 0.12% 0.11% 0.1% -20% -10%

CH4 0.023 0.017% 0.02% -15% 15%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.1.3: Effect of operating pressure:-

To study the effect of operating pressure on rate of reaction in chemical looping

combustion the operating pressure of the system is varied from 5 atm to 30 atm at a

step of 5 atm. Through Fig. 6.11 and 6.12, the effect of operating pressure on reactors

temperature, carbon dioxide purity in fuel reactor exhaust on dry nitrogen free basis

and fuel conversion on dry ash free basis of the system is shown for MC and SBC.

The figures show that fuel conversion decreases with the rise in operating pressure,

while purity of carbon dioxide in fuel reactor exhaust increases. In addition to it, the

fuel and air reactor temperatures decrease slightly at the start but this decrease

becomes significant at higher pressure.

Page | 83

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sion/P

uri

ty

70

75

80

85

90

95

100

Rea

ctor

Tem

per

ature

(K

)

950

1000

1050

1100

1150

1200

1250

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.11: Effect of operating pressure on MC for chemical looping combustion

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sion/P

uri

ty

70

75

80

85

90

95

100

Rea

cto

r T

emper

ature

(K

)

950

1000

1050

1100

1150

1200

1250

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.12: Effect of operating pressure on SBC for chemical looping combustion

6.2: Results of the present model when it is extended for high ash coals

Page | 84

In this segment, the developed 2-D CFD model in the First segment of Chapter 6 is

extended to study the effect of ash components in coal which is used as a fuel in coal

direct chemical looping process. The previously developed model incorporating

eighteen reactions for MC and SBC showed a better agreement with the pilot plant

data. For the present study, four different types of coals found in the regions of Asia-

Pacific and Australia and designated as “A”, “B”, “C” and “D” are used. Properties of

these coals are discussed in Table 3.4 and 3.5, while ash compositions are described

in Table 3.6. In present study only reaction kinetic aspect of ash is studied, the

melting of ash and its associated effects, oxygen carrier activity deactivation due to

presence of ash, etc. are not incorporated. In the present CFD model for “A”, “B”,

“C” and “D”, six more reactions of reactive ash components are incorporated over

and above eighteen reactions discussed in previous segment. While the present model

is extended for different types of ash bearing coals, the original dimensions of the

pilot plant is not modified according to the coal type. This has presented some

constraints for the study as far as the variation of the operating parameters are

concerned and thus considered as the limitations of the study. In fact the limiting

operating parameters like pressure drop, reactor bed temperature of the pilot plant

have been kept in the limits fixed for the pilot plant [14].

6.2.1: Simulation Results of the present model when Coal “A” is used:-

The process parameters used in the present study for coal “A” are discussed in

Table 6.7.

Table 6.7: Process parameters for Coal “A”

Fuel flow rate 4.5 kg/hr

Carrier CO2 gas flow rate 10 LPM

Air flow rate 0.001m/s

Inlet Fuel and Air Temperature 320K

Page | 85

Operating Pressure 15 atm

Table 6.8, discussed the mass weighted average rate of reactions for coal “A” in

four sections (i.e. fuel reactor section, inter-connecting pipe section, air reactor

section and riser section) of coal direct chemical looping pilot plant. From the Table

6.8, it is concluded that coal devolatilization (Reaction no. 4.1.3) is dominant reaction

in fuel reactor section and from Fig. 6.13 (a) it can be seen that it is also dominant in

lower section which shows low reactivity of fuel in comparison to SBC and MC

which raises the temperature at bottom section of fuel reactor as is clearly visible

from the temperature profile shown in Fig. 6.14 (b). In addition to it, the values of

mass weighted average rate of coal devolatilization reaction is nearly 1/100th

time of

the values obtained for sub-bituminous coal (SBC) in previous segment of study, this

is primarily due to high moisture content in the fuel (coal "A"). In the inter-

connecting part, four reactions i.e. coal devolatilization-, Boudouard-, water-gas shift-

and steam reforming-reaction (Reaction nos. 4.1.3, 4.8, 4.13 & 4.14) are dominant

and are in competition to each other due to higher temperature in that section of the

plant. In the air reactor, combustion of left over carbon (Reaction no. 4.15) is

dominant as can be seen from Fig. 6.13 (l). From the figure it is clear that left over

carbon reacts instantaneously with air as it enters the reactor. Additionally, the

reactive effect of silica in ash on chemical looping combustion is almost negligible

which can be seen from its mass weighted average rate of reaction (Reaction nos.

4.22, 4.23 and 4.24) which are of the magnitude lower than 10-20

. This is because the

above reactions require very high temperature (1500-1600C) which is not feasible

under present operating conditions. Further, calcium oxide (in ash) showed

substantial reactivity with water (Reaction no. 4.20) in fuel reactor and inter-

connecting part of the pilot plant which can be seen from the Table 6.8.

Table 6.8: Mass weighted average rate of reactions for coal “A”

Reaction Mass weighted Mass weighted Mass weighted Mass weighted

Page | 86

number average Rate of

Reaction in Fuel

reactor

(kmol/m3-s)

average Rate of

Reaction in inter-

connecting part

(kmol/m3-s)

average Rate

of Reaction in

Air reactor

(kmol/m3-s)

average Rate

of Reaction in

riser section

(kmol/m3-s)

4.1.3 4.47 × 10-4

3.93 × 10-5

0 0

4.2 1.69 × 10-6

4.45 × 10-6

0 0

4.3 5.61 × 10-10

0 0 0

4.4 4.36 × 10-11

0 0 0

4.5 1.05 × 10-16

0 0 0

4.6 3.65 × 10-9

3.52 × 10-9

2.44 × 10-19

9.74 × 10-24

4.7 1.62 × 10-15

7.51 × 10-10

1.11 × 10-21

8.28 × 10-26

4.8 1.5 × 10-5

4.26 × 10-5

1.79 × 10-15

4.64 × 10-23

4.9 5.42 × 10-13

0 0 0

4.10 0 0 4.45 × 10-12

9.42 × 10-17

4.11 0 0 2.7 × 10-11

4.28 × 10-14

4.12 0 5.95 × 10-18

4.02 × 10-38

0

4.13 0 3.32 × 10-5

4.36 × 10-15

3.96 × 10-17

4.14 0 3.22 × 10-5

7.19 × 10-6

2.52 × 10-7

4.15 0 0 3.43 × 10-3

6.77 × 10-11

4.16 0 0 2.34 × 10-5

2.41 × 10-7

4.17 1.53 × 10-11

1.86 × 10-11

1.98 × 10-12

3.02 × 10-15

Page | 87

4.18 0 0 2.06 × 10-5

3.55 × 10-7

4.19 5.77 × 10-15

2.47 × 10-15

0 0

4.20 5.54 × 10-6

1.04 × 10-6

0 0

4.21 4.99 × 10-13

1.16 × 10-13

0 0

4.22 -2.60 × 10-15

5.54 × 10-16

0 0

4.23 3.75 × 10-26

3.39 × 10-26

0 0

4.24 -4.92 × 10-21

-1.66 × 10-23

0 0

In Figs. 6.13 (a)-(s), profiles of reactions for coal “A” are shown having mass

weighted average rate of reaction greater than 10-12

. In Figs. 6.14(a)-(b), velocity and

temperature profile are shown, from Fig. 6.14 (a) it can be seen that maximum

velocity in the system is 31.9 m/s which is due to high value of fuel flow rate required

to maintain the process feasible. In addition to it, from Fig. 6.14 (b) it can be seen that

most of the fuel reactor part is having lower temperature which is due to the low

energy content of the fuel (due to low carbon content) and substantial high amount of

moisture which requires heat (to be given by fuel) for evaporation and thus affects the

process operability significantly. It can be concluded from Fig. 6. 13 (l), (m), (n), (o),

(q) and (r) that burning of left over carbon, oxidation of carbon monoxide and

reaction between hydrogen & oxygen are instantaneous reactions and are completed

at the entrance of species from inter-connecting part into air reactor. In Fig. 6.15,

molar concentration contour of various important species are shown, from Fig. 6. 15

(b) and (c) it can be seen that oxygen and nitrogen in fuel reactor is present in small

quantities meeting the objective of chemical looping combustion. From the Fig. 6.15

(k), it is clear that there is some quantity of carbon dioxide in air reactor due to

burning of left over carbon and carbon monoxide.

Page | 88

(a) Contour of Reaction 4.1.3

(b) Contour of Reaction 4.2

(c) Contour of Reaction 4.3

(d) Contour of Reaction 4.4

(e) Contour of Reaction 4.6

(f) Contour of Reaction 4.7

Page | 89

(g) Contour of Reaction 4.8

(h) Contour of Reaction 4.10

(i) Contour of Reaction 4.11

(j) Contour of Reaction 4.13

(k) Contour of Reaction 4.14

(l) Contour of Reaction 4.15

Page | 90

(m) Zoom of Reaction 4.15

(n) Contour of Reaction 4.16

(o) Zoom of Reaction 4.16

(p) Contour of Reaction 4.17

(q) Contour of Reaction 4.18

(r) Zoom of Reaction 4.18

Page | 91

(s) Contour of Reaction 4.20

Fig. 6.13: Rate of Reactions profiles having mass weighted average rate of reaction greater

than 10-12

for Coal “A”

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.14: Contour profiles of Velocity & Temperature for Coal “A”

Page | 92

(a) Contour of water

(b) Contour of oxygen

(c) Contour of nitrogen

(d) Contour of iron

(e) Contour of ferrous oxide

(f) Contour of ferric oxide

Page | 93

(g) Contour of methane

(h) Contour of hydrogen

(i) Contour of carbon

(j) Contour of carbon

monoxide

(k) Contour of carbon

dioxide

(l) Contour of Coal “A”

Page | 94

(m) Contour of non-reactive

ash

(n) Contour of calcium oxide

(o) Contour of silica

Fig. 6.15: Molar concentration profile of various species for coal “A”

Fig. 6.16: Molar concentration of Coal “A” with time along the pathline trajectory

The mass average velocities at fuel inlet and fuel reactor outlet are 30.28 m/s and

13.48 m/s respectively. Fig. 6.16 shows the variation in molar concentration of coal

“A” with time at the inlet of the fuel reactor along its pathline trajectory. From Fig.

0.002

0.0025

0.003

0.0035

0.004

0 1 2 3 4 5 6 7 8 9

Mola

r C

on

cen

trati

on

of

Coal

"A

"

Time (seconds)

Molar Concentration of coal A

Page | 95

6.16, it can be computed that the predicted conversion of fuel (coal “A”) is 65.27% on

dry ash free basis, while carbon dioxide purity in fuel reactor exhaust is 70.27%.

Table 6.9: Predicted results of coal “A” for present CFD model

Parameter Predicted Values for Coal “A”

Fuel Conversion1 65.27%

Fuel Reactor Exhaust Mole Fraction2

CO2 70.27%

SiO2 5.067%

CH4

0.196%

Cyclone Exhaust Mole Fraction

O2 16.08%

CO2 0.63%

CH4 0.012%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.2.2: Simulation Results of the present model when Coal “B” is used:-

The process parameters used in the present study for coal “B” are discussed in

Table 6.10.

Table 6.10: Process parameters for Coal “B”

Fuel flow rate 2.9 kg/hr

Carrier CO2 gas flow rate 10 LPM

Page | 96

Air flow rate 0.001m/s

Inlet Fuel and Air Temperature 320K

Operating Pressure 10 atm

Table 6.11, discussed the mass weighted average rate of reactions for coal “B” in

four sections (i.e. fuel reactor section, inter-connecting pipe section, air reactor

section and riser section) of coal direct chemical looping pilot plant. From the Table

6.11, it is concluded that coal devolatilization (Reaction no. 4.1.4) is dominant

reaction in fuel reactor section and from Fig. 6.17 (a) it can be seen that it is mainly

dominant in lower section of the fuel reactor where the temperature at the bottom

section of fuel reactor is at higher side as is clearly visible from the temperature

profile shown in Fig. 6.18 (b). In addition to it, the value of mass weighted average

rate of coal devolatilization reaction is nearly 1/1000th

time of the values obtained for

metallurgical coke (MC) in previous segment of study; this is primarily due to the

high amount of ash in the fuel (coal “B”) and low calorific value. In the inter-

connecting part, two reactions i.e., coal devolatilization, reduction of ferrous oxide by

carbon monoxide (Reaction nos. 4.1.4, & 4.6) are dominant and are in competition to

each other due to higher temperature in that section of the plant. In the air reactor,

combustion of left over carbon and reaction between hydrogen & oxygen (Reaction

nos. 4.15 & 4.18) are dominant as can be seen from Fig. 6.17 (j) and (n). From the

figure it is clear that left over carbon and hydrogen entering in air reactor reacts

instantaneously as it enters the reactor with air. Additionally, the reactive effect of

silica (present in ash) on chemical looping combustion is almost negligible which can

be seen from its mass weighted average rate of reaction (Reaction nos. 4.22, 4.23 and

4.24) which are of the magnitude lower than 10-20

. This is because the above reactions

of silica requires very high temperature in the range of 1500-1600⁰C which is not

feasible in the present operation condition. Further, calcium oxide present in ash

shows a significant reactivity with water (Reaction no. 4.20) in fuel reactor & inter-

Page | 97

connecting part of the pilot plant which can be seen from the Table 6.11 and Fig. 6.17

(o).

Table 6.11: Mass weighted average rate of reactions for coal “B”

Reaction

number

Mass weighted

average Rate of

Reaction in Fuel

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in inter-

connecting part

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

Air reactor

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

riser section

(kmol/m3-s)

4.1.4 4.49 × 10-5

4.29 × 10-6

0 0

4.2 4.59 × 10-8

2.45 × 10-8

0 0

4.3 6.61 × 10-12

0 0 0

4.4 2.58 × 10-13

0 0 0

4.5 1.28 × 10-19

0 0 0

4.6 9.74 × 10-6

3.9 × 10-6

3.18 × 10-16

1.05 × 10-28

4.7 1.2 × 10-19

2.67 × 10-15

8.35 × 10-28

0

4.8 1.25 × 10-7

1.98 × 10-7

2.44 × 10-16

2.26 × 10-22

4.9 3.71 × 10-16

0 0 0

4.10 0 0 6.44 × 10-9

8.09 × 10-8

4.11 0 0 4.65 × 10-12

4.41 × 10-10

4.12 0 6.95 × 10-15

1.85 × 10-18

7.65 × 10-20

4.13 0 2.9 × 10-8

7.11 × 10-18

2.6 × 10-27

Page | 98

4.14 0 9.01 × 10-8

1.21 × 10-8

3.3 × 10-9

4.15 0 0 9.88 × 10-5

3.41 × 10-10

4.16 0 0 5.34 × 10-8

1.52 × 10-17

4.17 1.74 × 10-14

3.07 × 10-15

1.02 × 10-16

2.37 × 10-36

4.18 0 0 1.21 × 10-4

2.73 × 10-4

4.19 4.11 × 10-17

9.5 × 10-17

0 0

4.20 4.34 × 10-7

6.39 × 10-8

0 0

4.21 9.7 × 10-15

1.13 × 10-15

0 0

4.22 1.13 × 10-22

2.05 × 10-23

0 0

4.23 -3.16× 10-34

0

0 0

4.24 -1.5 × 10-24

-1.08 × 10-25

0 0

In Fig. 6.17 (a)-(o), profiles of reactions for coal “B” are shown having mass

weighted average rate of reaction greater than 10-12

. In Figs. 6.18 (a)-(b), velocity and

temperature profile are shown, from Fig. 6.18 (b) it can be seen that some of the fuel

reactor part is having lower temperature which is due to the low energy content of the

fuel (due to low carbon content) and substantially high ash content which absorbs

most of the heat and thus affects the process operability significantly. It can be

concluded from Fig. 6. 17 (j), (k), (l), (m), and (n) that burning of left over carbon,

oxidation of carbon monoxide and reaction between hydrogen & oxygen are

instantaneous reactions and are completed at the entrance of species from inter-

connecting part into air reactor. In Fig. 6.19, molar concentration contour of various

important species are shown, from Fig. 6. 19 (b) and (c) it can be seen that oxygen

and nitrogen in fuel reactor is present in small quantities meeting the objective of

Page | 99

chemical looping combustion. From Fig. 6.19 (k) it is clear that there is some quantity

of carbon dioxide in air reactor due to burning of left over carbon and carbon

monoxide.

(a) Contour of Reaction 4.1.4

(b) Contour of Reaction 4.2

(c) Contour of Reaction 4.3

(d) Contour of Reaction 4.6

(e) Contour of Reaction 4.8

(f) Contour of Reaction 4.10

Page | 100

(g) Contour of Reaction 4.11

(h) Contour of Reaction 4.13

(i) Contour of Reaction 4.14

(j) Contour of Reaction 4.15

(k) Zoom of Reaction 4.15

(l) Contour of Reaction 4.16

Page | 101

(m) Zoom of Reaction 4.16

(n) Contour of Reaction 4.18

(o) Contour of Reaction 4.20

Fig. 6.17: Rate of Reactions profiles having mass weighted average rate of reaction greater

than 10-12

for Coal “B”

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.18: Contour profiles of Velocity & Temperature for Coal “B”

Page | 102

(a) Contour of water

(b) Contour of oxygen

(c) Contour of nitrogen

(d) Contour of iron

(e) Contour of ferrous oxide

(f) Contour of ferric oxide

Page | 103

(g) Contour of methane

(h) Contour of hydrogen

(i) Contour of carbon

(j) Contour of carbon

monoxide

(k) Contour of carbon

dioxide

(l) Contour of Coal “B”

Page | 104

(m) Contour of non-reactive

ash

(n) Contour of calcium oxide

(o) Contour of silica

Fig. 6.19: Molar concentration profile of various species for coal “B”

Fig. 6.20: Molar concentration of Coal “B” with time along the pathline trajectory

The mass average velocities at fuel inlet and fuel reactor outlet are 8.13 m/s and

14.52 m/s respectively. Fig. 6.20 shows the variation in molar concentration of coal

“B” with time from fuel inlet to fuel reactor outlet along its pathline trajectory. From

Fig. 6.20, it can be computed that the predicted conversion of fuel (coal “B”) is

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 1 2 3 4 5 6 7

Mola

r C

on

cen

trati

on

of

Coal

"B

"

Time (seconds)

Molar Concentration of coal B

Page | 105

65.27% on dry ash free basis, while carbon dioxide purity in fuel reactor exhaust is

80.27%.

Table 6.12: Predicted results of coal “B” for present CFD model

Parameter Predicted Values for Coal “B”

Fuel Conversion1 87.82%

Fuel Reactor Exhaust Mole Fraction2

CO2 80.27%

SiO2 6.067%

CH4

0.182%

Cyclone Exhaust Mole Fraction

O2 17.09%

CO2 0.69%

CH4 0.01%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.2.3: Simulation Results of the present model when Coal “C” is used:-

The process parameters used in the present study for coal “C” are discussed in

Table 6.13.

Table 6.13: Process parameters for Coal “C”

Fuel flow rate 2 kg/hr

Carrier CO2 gas flow rate 10 LPM

Page | 106

Air flow rate 0.001m/s

Inlet Fuel and Air Temperature 320K

Operating Pressure 10 atm

Table 6.14, discussed the mass weighted average rate of reactions for coal “C” in

four sections (i.e. fuel reactor section, inter-connecting pipe section, air reactor

section and riser section) of coal direct chemical looping pilot plant. From the Table

6.14, it is concluded that coal devolatilization, reduction of iron oxide by carbon

monoxide and Boudouard reaction (Reaction nos. 4.1.5, 4.6 and 4.8) are dominating

reactions in fuel reactor section. In addition to it, the value of mass weighted average

rate of coal devolatilization reaction is nearly 1/10th

time of the values obtained for

metallurgical coke (MC) in previous segment of study; this is primarily due to the

high amount of ash in the fuel (coal “C”). In the inter-connecting part, four reactions

coal devolatilization, reduction of ferrous oxide by carbon monoxide, Boudouard

reaction and steam reforming (Reaction nos. 4.1.5, 4.6, 4.8 & 4.14) shows their

dominance and are in competition to each other due to higher temperature in that

section of the plant. In the air reactor, combustion of left over carbon and reaction

between hydrogen & oxygen (Reaction nos. 4.15 & 4.18) are dominant as can be seen

from Fig. 6.21 (m) and (q). From the figure it is clear that left over carbon and

hydrogen reacts quickly with air as it enters the air reactor. Additionally, the reactive

effect of silica present in ash on chemical looping combustion is almost negligible

which can be seen from its mass weighted average rate of reaction (Reaction nos.

4.22, 4.23 and 4.24) which are of the magnitude lower than 10-17

because the above

reactions of silica requires very high temperature range of 1500-1600⁰C to play a

significant role in the process which is not feasible under present operating

conditions. Further, calcium oxide (present in ash component) showed a considerable

reactivity with water (Reaction no. 4.20) in fuel reactor and inter-connecting part of

the pilot plant which can be seen from the Table 6.14 & Fig. 6.21 (r).

Page | 107

Table 6.14: Mass weighted average rate of reactions for coal “C”

Reaction

number

Mass weighted

average Rate of

Reaction in Fuel

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in inter-

connecting part

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

Air reactor

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

riser section

(kmol/m3-s)

4.1.5 3.92 × 10-3

5.52 × 10-4

0 0

4.2 9.4 × 10-4

8.73 × 10-5

0 0

4.3 3.32 × 10-7

0 0 0

4.4 8.6 × 10-7

0 0 0

4.5 1.63 × 10-11

0 0 0

4.6 2 × 10-3

1.97 × 10-4

2.05 × 10-7

5.95 × 10-17

4.7 8.01 × 10-9

3.9 × 10-5

3.57 × 10-7

2.15 × 10-19

4.8 1.52 × 10-3

6.16 × 10-4

7.24 × 10-6

7.09 × 10-31

4.9 4.34 × 10-12

0 0 0

4.10 0 0 6.59 × 10-7

1.51 × 10-7

4.11 0 0 3.68 × 10-10

4.74 × 10-10

4.12 0 1.06 × 10-14

1.85 × 10-17

0

4.13 0 1.48 × 10-2

1.35 × 10-10

1.16 × 10--21

4.14 0 5.74 × 10-4

2.13 × 10-4

2.67 × 10-5

4.15 0 0 3.88 × 10-3

2.39 × 10-19

Page | 108

4.16 0 0 3.56 × 10-4

2.52 × 10-5

4.17 4.54 × 10-9

5.69 × 10-9

2.46 × 10-9

1.77 × 10-10

4.18 0 0 1.48 × 10-3

3.8 × 10-5

4.19 2.68 × 10-13

1.99 × 10-14

0 0

4.20 3.36 × 10-6

3.39 × 10-6

0 0

4.21 1.69 × 10-11

2.24 × 10-12

0 0

4.22 6.38 × 10-18

5.47 × 10-19

0 0

4.23 -8.16× 10-30

0

0 0

4.24 -3.45 × 10-22

-1.68 × 10-23

0 0

In Fig. 6.21 (a)-(r), profiles of reactions for coal “C” are shown having mass

weighted average rate of reaction greater than 10-10

. In Fig. 6.22 (a)-(b), velocity and

temperature profile are shown, from Fig. 6.22 (b) it can be seen that most of the fuel

reactor part remains at higher temperature due to high calorific value of fuel (coal

"C"). It can be concluded from Fig. 6. 21 (m), (n), (o), (q) that burning of left over

carbon, oxidation of carbon monoxide and reaction between hydrogen & oxygen are

instantaneous reactions and are completed at the entrance of species from inter-

connecting part into air reactor. In Fig. 6.23, molar concentration contour of various

important species are shown, from Fig. 6. 23 (b) and (c) it can be seen that oxygen

and nitrogen in fuel reactor is present in small quantities meeting the objective of

chemical looping combustion. From Fig. 6.23 (k) it is clear that there is some quantity

of carbon dioxide in air reactor due to burning of left over carbon and carbon

monoxide.

Page | 109

(a) Contour of Reaction 4.1.5

(b) Contour of Reaction 4.2

(c) Contour of Reaction 4.3

(d) Contour of Reaction 4.4

(e) Contour of Reaction 4.6

(f) Contour of Reaction 4.7

Page | 110

(g) Contour of Reaction 4.8

(h) Contour of Reaction 4.10

(i) Contour of Reaction 4.11

(j) Contour of Reaction 4.13

(k) Zoom of Reaction 4.13

(l) Contour of Reaction 4.14

Page | 111

(m) Contour of Reaction 4.15

(n) Zoom of Reaction 4.15

(o) Contour of Reaction 4.16

(p) Contour of Reaction 4.17

(q) Contour of Reaction 4.18

(r) Contour of Reaction 4.20

Fig. 6.21: Rate of Reactions profiles having mass weighted average rate of reaction greater

than 10-10

for Coal “C”

Page | 112

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.22: Contour profiles of Velocity & Temperature for Coal “C”

(a) Contour of water

(b) Contour of oxygen

(c) Contour of nitrogen

Page | 113

(d) Contour of iron

(e) Contour of ferrous oxide

(f) Contour of ferric oxide

(g) Contour of methane

(h) Contour of hydrogen

(i) Contour of carbon

Page | 114

(j) Contour of carbon

monoxide

(k) Contour of carbon

dioxide

(l) Contour of Coal “C”

(m) Contour of non-reactive

ash

(n) Contour of calcium oxide

(o) Contour of silica

Fig. 6.23: Molar concentration profile of various species for coal “C”

Page | 115

Fig. 6.24: Molar concentration of Coal “C” with time along the pathline trajectory

The mass average velocities at fuel inlet and fuel reactor outlet are 6.13 m/s and

1.71 m/s respectively. Fig. 6.24 shows the variation in molar concentration of coal

“C” with time from fuel inlet to fuel reactor outlet along the pathline trajectory. From

Fig. 6.24, it can be computed that the predicted conversion of fuel (coal “C”) is

93.8% on dry ash free basis, while carbon dioxide purity in fuel reactor exhaust is

86.12%.

Table 6.15: Predicted results of coal “C” for present CFD model

Parameter Predicted Values for Coal “C”

Fuel Conversion1 93.8%

Fuel Reactor Exhaust Mole Fraction2

CO2 89.12%

SiO2 3.17%

CH4

0.15%

Cyclone Exhaust Mole Fraction

0

0.01

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

Mola

r C

on

cen

trati

on

of

Coal

"C

"

Time (seconds)

Molar Concentration of coal C

Page | 116

O2 12.09%

CO2 0.79%

CH4 0.008%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.2.4: Simulation Results of the present model when Coal “D” is used:-

The process parameters used in the present study for coal “D” are discussed in

Table 6.16.

Table 6.16: Process parameters for Coal “D”

Fuel flow rate 2.25 kg/hr

Carrier CO2 gas flow rate 10 LPM

Air flow rate 0.001m/s

Inlet Fuel and Air Temperature 320K

Operating Pressure 10 atm

Table 6.17, discussed the mass weighted average rate of reactions for coal “D” in

four sections (i.e. fuel reactor section, inter-connecting pipe section, air reactor

section and riser section) of coal direct chemical looping pilot plant. From the Table

6.17, it is concluded that coal devolatilization, and Boudouard reaction (Reaction nos.

4.1.5, and 4.8) are dominating reactions in fuel reactor section. In addition to it, the

value of mass weighted average rate of coal devolatilization reaction is nearly 1/10th

time of the values obtained for sub-bituminous coal (SBC) in previous segment of

study which is due to the high amount of ash in the fuel and high calorific value of

fuel (coal “D”). In the inter-connecting part, water-gas shift reaction (Reaction no.

Page | 117

4.13) shows its dominance due to higher value of temperature and amount of carbon

monoxide in that section of the plant. In the air reactor, combustion of left over

carbon (Reaction no. 4.15) is dominant while, from Fig. 6.25 (l) it can be seen that it

reacts readily as it enters the air reactor. Additionally, the reactive effect of silica

(present in ash) on chemical looping combustion is almost negligible which can be

seen from its mass weighted average rate of reaction (Reaction nos. 4.22, 4.23 and

4.24) which of the magnitude lowers than 10-17

. This is because the above reaction

requires very high temperature (1500-1600⁰C) which is not feasible under present

operating conditions. Further, calcium oxide (present in ash components) showed a

considerable reactivity with water (Reaction no. 4.20) in fuel reactor and inter-

connecting part of the pilot plant which can be seen from the Table 6.17 and Fig. 6.25

(r).

Table 6.17: Mass weighted average rate of reactions for coal “D”

Reaction

number

Mass weighted

average Rate of

Reaction in Fuel

reactor

(kmol/m3-s)

Mass weighted

average Rate of

Reaction in inter-

connecting part

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

Air reactor

(kmol/m3-s)

Mass weighted

average Rate

of Reaction in

riser section

(kmol/m3-s)

4.1.6 7.89 × 10-3

8.91 × 10-4

0 0

4.2 4.79 × 10-4

1.31 × 10-4

0 0

4.3 1.13 × 10-7

0 0 0

4.4 1.14 × 10-7

0 0 0

4.5 1.28 × 10-12

0 0 0

4.6 2.04 × 10-4

1.95 × 10-4

1.31 × 10-14

1.22 × 10-22

4.7 1.11 × 10-9

5.7 × 10-5

6.38 × 10-17

8.82 × 10-25

Page | 118

4.8 2.98 × 10-3

1.08 × 10-3

9.12 × 10-13

5.64 × 10-22

4.9 1.67 × 10-11

0 0 0

4.10 0 0 5.17 × 10-8

4.48 × 10-14

4.11 0 0 4.45 × 10-10

1.23 × 10-12

4.12 0 3.88 × 10-14

3.35 × 10-33

0

4.13 0 1.6 × 10-2

4.89 × 10-12

3.76 × 10--18

4.14 0 1.04 × 10-3

1.73 × 10-5

1.79 × 10-7

4.15 0 0 1.32 × 10-2

7.03 × 10-10

4.16 0 0 6.33 × 10-4

1.72 × 10-7

4.17 5.32 × 10-9

1.02 × 10-8

7.56 × 10-11

1.21 × 10-14

4.18 0 0 5.26 × 10-4

2.58 × 10-7

4.19 4.98 × 10-13

6.61 × 10-14

0 0

4.20 3.47 × 10-5

1.42 × 10-5

0 0

4.21 3.74 × 10-11

1.74 × 10-11

0 0

4.22 2.74 × 10-18

7.85 × 10-19

0 0

4.23 -1.71× 10-30

0

0 0

4.24 -2.74 × 10-23

-2.29 × 10-24

0 0

In Fig. 6.25 (a)-(r), profiles of reactions for coal “D” are shown having mass

weighted average rate of reaction greater than 10-10

. In Figs. 6.26 (a)-(b), velocity and

Page | 119

temperature profile are shown, from Fig. 6.26 (b) it can be seen that most of the fuel

reactor part is having higher temperature which is due to the fuel have high energy

content while discontinuity in temperature is due to inlet feed temperature. The higher

values of fuel flow rate and 1/10th

value of coal devolatilization rate are the effect of

having high quantity of ash. It can be concluded from Fig. 6. 25 (l), (m), (n), (o), and

(q) that burning of left over carbon, oxidation of carbon monoxide and reaction

between hydrogen & oxygen are instant reactions and are completed at the entrance

of species from inter-connecting part into air reactor. In Fig. 6.27, molar

concentration contour of various important species are shown, from Fig. 6. 27 (b) &

(c) it can be seen that oxygen and nitrogen in fuel reactor is present in very small

quantities which meets the objective of chemical looping combustion. From Fig. 6.27

(k) it is clear that there is some carbon dioxide present in air reactor due to burning of

left over carbon and carbon monoxide.

(a) Contour of Reaction 4.1.6

(b) Contour of Reaction 4.2

(c) Contour of Reaction 4.3

Page | 120

(d) Contour of Reaction 4.4

(e) Contour of Reaction 4.6

(f) Contour of Reaction 4.7

(g) Contour of Reaction 4.8

(h) Contour of Reaction 4.10

(i) Contour of Reaction 4.11

Page | 121

(j) Contour of Reaction 4.13

(k) Contour of Reaction 4.14

(l) Contour of Reaction 4.15

(m) Zoom of Reaction 4.15

(n) Contour of Reaction 4.16

(o) Zoom of Reaction 4.16

Page | 122

(p) Contour of Reaction 4.17

(q) Contour of Reaction 4.18

(r) Contour of Reaction 4.20

Fig. 6.25: Rate of Reactions profiles having mass weighted average rate of reaction greater

than 10-10

for Coal “D”

(a) Contour of Velocity

(b) Contour of Temperature

Fig. 6.26: Contour profiles of Velocity & Temperature for Coal “D”

Page | 123

(a) Contour of water

(b) Contour of oxygen

(c) Contour of nitrogen

(d) Contour of iron

(e) Contour of ferrous oxide

(f) Contour of ferric oxide

Page | 124

(g) Contour of methane

(h) Contour of hydrogen

(i) Contour of carbon

(j) Contour of carbon

monoxide

(k) Contour of carbon

dioxide

(l) Contour of Coal “D”

Page | 125

(m) Contour of non-reactive

ash

(n) Contour of calcium oxide

(o) Contour of silica

Fig. 6.27: Molar concentration profile of various species for coal “D”

Fig. 6.28: Molar concentration of Coal “D” with time along the pathline trajectory

The mass average velocities at fuel inlet and fuel reactor outlet are 6.3 m/s and

1.71 m/s respectively. Fig. 6.28 shows the variation in molar concentration of coal

“D” with time from fuel inlet to fuel reactor outlet along the pathline trajectory. From

Fig. 6.28, it can be computed that the predicted conversion of fuel (coal “D”) is

0

0.005

0.01

0.015

0.02

0.025

0 1 2 3 4 5 6

Mola

r C

on

cen

trati

on

of

Coal

"D

"

Time (seconds)

Molar Concentration of coal D

Page | 126

87.79% on dry ash free basis, while carbon dioxide purity in fuel reactor exhaust is

90.73%.

Table 6.18: Predicted results of coal “D” for present CFD model

Parameter Predicted Values for Coal “D”

Fuel Conversion1 87.79%

Fuel Reactor Exhaust Mole Fraction2

CO2 90.73%

SiO2 4.17%

CH4

0.14%

Cyclone Exhaust Mole Fraction

O2 15.09%

CO2 0.67%

CH4 0.01%

1 on dry ash free basis

2 on dry and nitrogen free basis

6.2.5: Comparison between Coals

In Fig. 6.29 & 6.30, a comparative layout between four coals namely, “A”, “B”,

“C” and “D” is shown for mass-weighted average rate of major reactions occurring in

Fuel Reactor section, inter-connecting part, Air Reactor section and Riser section.

Page | 127

Fig

. 6.2

9:

Com

par

ativ

e m

ass

wei

ghte

d a

ver

age

rate

of

reac

tions

for

four

coal

s “A

”, “

B”,

“C

”, “

D” o

n l

og

10 s

cale

for

Fuel

Rea

ctor

and i

nte

r-co

nnec

ting p

art

Page | 128

Fig

. 6.3

0:

Com

par

ativ

e m

ass

wei

ghte

d a

ver

age

rate

of

reac

tions

for

four

coal

s “A

”, “

B”,

“C

”, “

D”

on l

og

10 s

cale

for

Air

Rea

ctor

and R

iser

sec

tion

Page | 129

6.2.6: Effect of operating pressure:-

To study the effect of operating pressure on chemical looping combustion the

operating pressure of the system is varied from 5 atm to 30 atm at a step of 5 atm.

Through Fig. 6.31, 6.32, 6.33 and 6.34, the effect of operating pressure on reactors

temperature, carbon dioxide purity in fuel reactor exhaust on dry nitrogen free basis

and fuel conversion on dry ash free basis of the system is shown for coals found in

Asia-Pacific and Australia region and denoted as coal “A”, “B”, “C” and “D”. The

Figs. 6.32, 6.33 and 6.34 drawn for the case of fuel coal designated as “B”, “C” and

“D” shows that fuel conversion decreases with the rise in operating pressure, while

purity of carbon dioxide in fuel reactor exhaust increases. In addition to it, the fuel

and air reactor temperature decreases slightly at the initial operating pressure but this

decrease becomes significant at higher pressure. In case of fuel coal “A” the variation

of operating pressure shows an inconsistent behavior with respect to other coals i.e.

“B”, “C” and “D” vis-a-vis other fuels used for study due to its very high moisture

content and very low calorific value which even on higher pressure restricts the

operability by quenching the temperature of the fuel reactor as can be seen from Fig.

6.31.

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sion/P

uri

ty

65

66

67

68

69

70

71

Rea

ctor

Tem

per

ature

(K

)

200

400

600

800

1000

1200

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.31: Effect of operating pressure on coal “A” for chemical looping combustion

Page | 130

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sion/P

uri

ty

78

80

82

84

86

88

90

Rea

ctor

Tem

per

ature

(K

)

1010

1020

1030

1040

1050

1060

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.32: Effect of operating pressure on coal “B” for chemical looping combustion

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sio

n/P

uri

ty

88

89

90

91

92

93

94

95

Rea

cto

r T

emper

atu

re (

K)

1060

1080

1100

1120

1140

1160

1180

1200

1220

1240

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.33: Effect of operating pressure on coal “C” for chemical looping combustion

Page | 131

Pressure (atm)

0 5 10 15 20 25 30

% C

onver

sion/P

uri

ty

86

87

88

89

90

91

92

Rea

ctor

Tem

per

ature

(K

)

1040

1060

1080

1100

1120

1140

1160

1180

1200

1220

1240

Pressure vs Fuel Conversion

Pressure vs CO2 Purity

Pressure vs Fuel Reactor Temperature

Pressure vs Air Reactor Temperature

Fig. 6.34: Effect of operating pressure on coal “D” for chemical looping combustion

Page | 132

CHAPTER 7 CONCLUSION & RECOMMENDATIONS

The salient conclusions of the present CFD study of a coal direct chemical looping

pilot plant for different types of coals are as follows:

Conclusions from First segment of study

1. Results of present simplified CFD model taking into account 11 reactions

presented in first part of first segment are in good agreement with the results of pilot

plant developed at Ohio State University, USA. The simulated fuel conversions for

two fuels i.e. MC and SBC show errors equal to 15.91% and 9.28% respectively

when compared with maximum conversion values of above fuels reported for the

pilot plant.

2. Further, results of present simplified CFD model taking into account 18

reactions presented in second part of first segment are in better agreement with the

pilot plant results. The simulated fuel conversions for MC and SBC show maximum

errors equal to 12.05% and 3.64% respectively when compared with maximum fuel

conversion values of above fuels reported for the pilot plant amounting to

improvements equivalent to 4% and 6%.

3. The conclusions given in Sl. Nos.1 &2 clearly indicates that the present

simplified CFD model given in Sl. No.2 is the best model and can be used for coal

direct chemical looping process simulation if the above error limits can be tolerated.

4. Further, the effect of variation in operating pressure for both fuels shows that

fuel conversion decreases with the rise in operating pressure, while purity of carbon

dioxide in fuel reactor exhaust increases. In addition to it, the fuel and air reactor

temperature decreases slightly at the start of the pressure range but this decrease

becomes significant at higher pressure. This fact is in conformity to Labiano et al.

(2006) [55].

Page | 133

5. Carbon dioxide purity in fuel reactor exhaust increases with rise in fuel reactor

temperature for MC as well as SBC. This fact is in conformity to the observations of

Abad et al. (2013) [66].

Conclusions from Second segment of study

The best CFD model developed, incorporating eighteen reactions as proposed in

the second part of the First Segment and six more reactions of reactive ash

components, which has been validated with the pilot plant data is used to study the

effect of four different coals (denoted as “A”, “B”, “C” and “D”) having considerable

amount of ash and moisture on chemical looping combustion. The salient conclusions

of this study are listed below:

1. Fuel conversion on dry ash free basis for fuel coals “A”, “B”, “C” and “D” are

65.27%, 87.82%, 93.8% and 90.73% respectively 2. CaO and Fe2O3 as a part of

reactive ash component shows reactivity under the process condition while SiO2

shows mass weighted average rate of reactions are lesser than 10-20

kmol/m3-s which

are almost negligible.

3. The amount of ash present in fuel coal increases the fuel flow rate

proportionately to produce required feasible process conditions for chemical looping

combustion. The carbon capturing efficiency decreases as fuel flow rate is increased

which has also been observed by Abad et al. (2013) [66]. Further, it can be seen that

overall fuel conversion decreases as amount of non-carbonaceous species increases

such as moisture and ash in the fuel coal as also been identified by Azis et al. (2013)

[25]

4. The carbon dioxide purity in fuel reactor exhaust increases with the rise in fuel

reactor temperature for the four fuels used in the present study. This fact is in tune

with the observations of Abad et al. (2013) [66].

Recommendations:

Further, in development of CFD based model for coal direct chemical looping

technology research on following topics is recommended:

Page | 134

1. Fuel (Coal) specific process design should be developed so as to provide process

residence time for it reactions to achieve better conversion and ash handling.

2. A 3-D based CFD model should be developed and validated with the pilot plant

results to observe the effectiveness of 2D vis-a-vis 3D model predictions.

3. Attempt should be made to incorporate complex CFD model like Euler-Euler

Granular flow model, CFD- DEM method for accurate prediction of hydrodynamics

of the system and associated reactions.

4. Other newly developed metal oxides which are doped with compounds and bi-

metallic oxygen carriers should be tested through modeling for better conversion.

Page | 135

List of Publications:

R. Wadhwani, B. Mohanty, “A Review on clean & efficient technology to

generate electricity from coal”, in Proceedings of the IICBE CAMS-2013, Kuala

Lumpur, Malaysia, pp. 4-8, 2013

R. Wadhwani, B. Mohanty, “Computational study on coal direct chemical looping

combustion”, International Journal of Latest Trends in Engineering and

Technology, Vol. 4, Issue 1, Article 25, 2014

R.Wadhwani, B. Mohanty “CFD study on a complete coal direct chemical

looping pilot plant”, (Submitted in Energy Technology, April 2014)

R. Wadhwani, B. Mohanty, “Effect of ash on coal direct chemical looping

combustion”, (In Preparation)

Page | 136

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