research article analysis of hydrogen generation through...

Research ArticleAnalysis of Hydrogen Generation throughThermochemical Gasification of Coconut Shell UsingThermodynamic Equilibrium Model ConsideringChar and Tar

Shanmughom Rupesh Chandrasekharan Muraleedharan and Palatel Arun

Department of Mechanical Engineering National Institute of Technology Calicut Calicut Kerala 673601 India

Correspondence should be addressed to Shanmughom Rupesh mailtorupeshsgmailcom

Received 24 May 2014 Revised 7 September 2014 Accepted 8 September 2014 Published 4 November 2014

Academic Editor J Alfredo Hernandez

Copyright copy 2014 Shanmughom Rupesh et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This work investigates the potential of coconut shell for air-steam gasification using thermodynamic equilibrium model Athermodynamic equilibriummodel considering tar and realistic char conversionwas developed usingMATLAB software to predictthe product gas composition After comparing it with experimental results the prediction capability of the model is enhanced bymultiplying equilibriumconstantswith suitable coefficientsThemodifiedmodel is used to study the effect of key process parameterslike temperature steam to biomass ratio and equivalence ratio on product gas yield composition and heating value of syngas alongwith gasification efficiency For a steam to biomass ratio of unity the maximum mole fraction of hydrogen in the product gas isfound to be 3614 with a lower heating value of 749MJNm3 at a gasification temperature of 1500K and equivalence ratio of 015

1 Introduction

Gasification is a thermochemical process by which lowenergy density fuels like biomass can be converted intogaseous fuels with the aid of a series of chemical reactionsOne of the main components of gaseous fuel obtained fromgasification is hydrogen which can be used in internalcombustion engines and fuel cells Hydrogen is a clean fueland energy released by it on combustion is higher than anyother fuel on mass basis [1] Thus it can be considered asa suitable solution for problems associated with fossil fueldepletion and global warming if its availability is ensuredfrom a sustainable source Being a renewable energy sourcebiomass can be considered as a potential candidate forhydrogen production by gasification Hydrogen yield frombiomass gasification depends on several factors like moisturecontent feed stock composition type of gasifier amount ofgasifying agent and so forth Influence of different gasifyingagents on product gas distribution was studied by Gil et al[2] From the study it was found that compared to air lowerheating value and tar yield are high when steam was used as

gasifying agent Critical component of any gasification systemis the gasifier where the homogeneous and heterogeneousreactions take place A comparison between fixed bed andfluidised bed gasifiers was made by Warnecke [3] It wasconcluded that in spite of low ashmelting point and high dustcontent in the product gas fluidised bed gasifiers have higherheat andmass transfer compared to fixed bed which result inbetter temperature distribution

Mathematical models can be used to investigate biomassgasification especially when large scale experimental studyseems to be difficult and uneconomical A detailed review ondifferent gasification models was presented by Puig-Arnavatet al [4] andD Baruah andD C Baruah [5]The comparisonof different models showed that thermodynamic equilibriummodel (TEM) is the simplest and can be used as an effectivepreliminary tool to study the effect of process parametersand fuels on gasificationThermodynamic equilibriummodelcan be implemented through two approaches namely sto-ichiometric and nonstoichiometric [6] Compared to theformer approach the latter one is complex even though thebasic principle of both is one and the same Thus many

Hindawi Publishing CorporationInternational Scholarly Research NoticesVolume 2014 Article ID 654946 9 pageshttpdxdoiorg1011552014654946

2 International Scholarly Research Notices

researchers formulated stiochiometric equilibrium modelsfor simulating biomass gasification [7ndash13] Application ofstoichiometric models for air gasification was successfullydemonstrated by Zainal et al [14] and the modificationmethod used to augment the prediction accuracy of sim-ilar models was given by Jarungthammachote and Dutta[15] Modification of thermodynamic equilibrium model toreduce its deviation from experimental data is done mainlyby considering char conversion tar formation and introduc-ing suitable correction factors to equilibrium constants AGibbrsquos free energy minimisation model for air gasificationwas developed by Ghassemi and Shahsavan-Markadeh [16]and the prediction accuracy of the model was improvedby incorporating carbon conversion and tar formation fromAzzone et al [17] and Barman et al [18] respectively Azzoneet al [17] considered char conversion as a function ofequivalence ratio (ER) in air-steam gasification whereas tarwas incorporated by Barman et al [18] in air gasificationas a compound containing carbon hydrogen and oxygenThe deviation of stoichiometric model developed by HuangandRamaswamy [19] from thermodynamic equilibriumwasreduced by multiplying suitable coefficient with equilibriumconstants as done by Jarungammachote and Dutta [15] andLoha et al [11] for air and steam gasifications respectivelySimilar modification was applied by Lim and Lee [20] toair-steam gasification model in which char conversion wasexpressed as a function of equivalence ratio and temperatureAbuadala et al [13] included unreacted char as 5 of biomasscarbon content and tar as benzene in steam gasificationmodel A pseudoequilibrium air-steam gasification modelwith correction factors for equilibrium constants in termsof reactor temperature was developed by Ng et al [10] Thismodel considered tar as a compound containing carbonhydrogen and oxygen and char as solid carbon A threestage quasi-equilibrium model (considering pyrolysis char-gas reactions and gas phase reactions in each stage) forsteam gasification of biomass was developed by Nguyen etal [21] where empirical relations were used to reduce thedeviation from thermodynamic equilibrium Puig-Arnavatet al [22] developed a modified equilibrium model for airsteam gasification of biomass using Engineering EquationSolver (EES) Deviation of this model from pure equilibriumis minimised by considering pyrolysis heat loss in pyrolysischar and tar and particles leaving the gasifier and setting theamount of CH

4produced Review of the literatures reveals

that stoichiometric models formulated for biomass air-steamgasification considering both char and tar are limited Presentwork deals with the stoichiometric modeling of air-steamgasification considering tar and char Coconut shell a locallyavailable nonedible biomass waste is the feedstock selectedfor the study

2 Model Development

In general thermodynamic equilibrium calculations are inde-pendent of gasifier design and are suitable for analysingthe effect of fuel and process parameters [6] These modelsare more appropriate for simulating entrained flow gasifiers

in chemical process simulators or for downdraft fixed-bedgasifiers as long as high temperature and gas residence timeare achieved The objective of present work is to develop athermodynamic equilibriummodel to simulate fluidised bedbiomass gasifier

Biomass gasification being a complex process its theoret-ical modeling requires certain assumptionsThe assumptionsused to formulate the biomass gasification process are asfollows

(i) Gasifier is considered as a steady state system withuniform temperature and pressure throughout

(ii) The residence time of the gases in the gasifier is highenough to establish thermodynamic and chemicalequilibria

(iii) All the gases behave ideally(iv) Gases except H

2 CO CO

2 CH4 and N

2are consid-

ered dilute(v) N2is considered as inert in the entire process

(vi) Biomass is considered to be made up of carbonhydrogen and oxygen

(vii) Steam is supplied under superheated condition of 1bar and 300∘C

By considering chemical formula of feedstock as CH119883O119885

global gasification reaction can be written as

119899119887CH119883O119885+ 119908H

2O(119897)+ 119904H2O(119892)+ 119898O

2+ 376119898N

2

997888rarr 119899H2

H2+ 119899COCO + 119899CO

2

CO2+ 119899CH

4

CH4

+ 119899H2OH2O(119892) + (1 minus 120572)C + 119899tartar + 376119898N2

(1)

where 119883 and 119885 are the number of atoms of hydrogen andoxygen for each atom of carbon per mole of biomass119908 is theamount of moisture present in dry ash free biomass 119904 and119898are the amount of steam and oxygen supplied respectivelyOn the right hand side 119899H

2

119899CO 119899CO2

119899CH4

and 119899tar are thenumbers of moles of H

2 CO CO

2 CH4 and tar and 120572 is the

char conversion factor which can be expressed as a functionof equivalence ratio [23] given by

120572 = 032 + 082 (1 minus 119890minusER0229) (2)

Tar is incorporated in the model as a mixture of benzenetoluene and naphthalene in 1 25 65 proportions by weight[24 25] and its yield can be obtained as a weight percentageof the total gasification products using the following [20]

Tarwt = 3598 exp (minus00029119879) (3)

where 119879 is the temperatureTotal weight of the gasification product is obtained by

applying mass balance to the global reaction between thereactants and the products Somass of tar yield (119898tar) is givenby

119898tar =Tarwt100

(biomass feed + SBR lowast biomass feed

+ moisture in biomass + air supplied) (4)

International Scholarly Research Notices 3

To determine five unknown constituents of the producergas five separate equations are required These equations aredeveloped from mass balance of C H and O from (1) andequilibrium constant relations for water gas shift reaction (see(9)) and methane reaction (see (11))

carbon balance

119899CO + 119899CO2

+ 119899CH4

+ 6119899C6H6

+ 7119899C7H8

+10119899C10H8

+ (1 minus 120572) minus 119899119887= 0

(5)

hydrogen balance

2119899H2

+ 4119899CH4

+ 2119899H2O + 6119899C

6H6

+ 8119899C7H8

+8119899C10H8

minus 119909119899119887minus 2119904 minus 2119908 = 0

(6)

oxygen balance

119899CO + 2119899CO2

+ 119899H2O minus 119910119899b minus 119904 minus 119908 minus 2119898 = 0 (7)

water gas shift reaction

CO +H2O 997888rarr CO

2+H2 (8)

Considering equilibrium constant 1198701for water gas shift

reaction

1198701=

119899CO2

119899H2

119899CO119899H2O (9)

Methane Reaction is as follows

C + 2H2997888rarr CH

4 (10)

Considering equilibrium constant 1198702for methane reaction

1198702=

119899total 119899CH4

(119899H2

)

2 (11)

Considering product gas as ideal gas 1198701and 119870

2can be

expressed as a function of temperature [14] given by

1198701= exp (5878

119879

+ 186 ln119879 minus 027 times 10minus3119879 minus 582001198792minus 18)

(12)

1198702= exp(7082842

119879

minus 6567 ln119879 + 7467 times 10minus3

2

119879

minus

2167 times 10minus6

6

1198792+

00702 times 10minus5

21198792+ 32541)

(13)

Thus equilibrium composition of the product gas is obtainedby simultaneously solving three linear equations (see (5)ndash(7)) and two nonlinear equations (see (9) and see (11)) inMATLAB platform using Newton-Raphson method

Lower heating value of the dry product gas is estimatedfrom the gas composition and is expressed in volume basis as[26]

LHV = 1079119884H2

+ 1226119884CO + 3581119884CH4

(14)

Table 1 Proximate and ultimate analyses of coconut shell

Proximate analysis (wt) Ultimate analysis (wt)Moisture 8 C 4561Volatilematter 71 H 561

Ash 4 O 4816Fixed carbon 17 N 026

S 034

Gasification efficiency of the process is given by

120578gas

=

Energy content in the product gasEnergy content in biomass + Energy content in steam

(15)

The results of proximate and ultimate analyses of coconutshell are presented in Table 1

3 Model Validation

Accuracy of themodel is checked by comparing the predictedgas composition from the model with experimental results[27] The error was estimated by using the statistical parame-ter of root mean square (RMS) error

RMS = radicsum(119883119890minus 119883119901)

2

119873

(16)

where119883119890119883119901 and119873 are experimental data predicted value

and number of observations respectively An average RMSvalue of 793 is obtained when the nine sets of experimentalresults are compared with their corresponding theoreticalpredictions given in Figure 1

4 Model Modification

It is observed that H2and CO concentrations were over-

predicted and CO2and CH

4concentrations were underpre-

dicted by the present model from the experimental valuesSimilar results were observed by Melgar et al [28] whenthey compared their model predicted gas composition withthe experimental work of Jayah et al [29] Same resultswere obtained when the equilibrium models [30ndash33] werecompared with the experimental results from fluidised bedsteam gasification by Hofbauer et al [34] and Rapagna et al[35] The effect was also reported in [4 22] This deviation inconcentration may be attributed to the existence of nonequi-librium conditions in the gasifier during the experimentThe model is upgraded to match the experimental results bymultiplying with suitable coefficients 119860 and 119861 to 119870

1and 119870

2

respectively [14] The variation in RMS error is monitoredby changing the values of 119860 and 119861 and the coefficient values


0

5

10

15

20

25

30

35

40

Mol

e fra

ctio

n (

)

Average RMS error =793

H2 (E)H2 (M)CO (E)CO (M)CO2 (E)

CO2 (M)CH4 (E)CH4 (M)RMS error

T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1085SB

R=0ER

=035

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033

Figure 1 Comparison between experimental and model results Eexperimental result M model results

0

5

10

15

20

25

30

Mol

e fra

ctio

n (

)


H2 (E)H2 (M1)CO (E)CO (M1)CO2 (E)

CO2 (M1)CH4 (E)CH4 (M1)RMS error

T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033

T=1085SB

R=0ER

=035

Figure 2 Comparison between experimental and modified modelresults E experimental result M1 modified model results

corresponding to minimum RMS error are incorporated inthe model for better prediction The variation of RMS errorwith different values of 119860 and 119861 is shown in Figures 3 and 4respectively Incorporating suitable coefficients (119860 = 085 and119861 = 48) the average initial RMS error is reduced from 793to a minimum of 261 A comparison between experimental

2

22

24

26

28

3

32

34

36

38

02 04 06 08 1 12 14 16 18 2 22

RMS

erro

r

Coefficient of K1 (A)

Figure 3 Variation of RMS error with coefficient of 1198701

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70

RMS

erro

r

Coefficient of K2 (B)

Figure 4 Variation of RMS error with coefficient of1198702

results and that obtained from modified model is presentedin Figure 2

5 Model Application

Themodified quasi-equilibrium model is used to predict theinfluence of key process parameters like temperature steamto biomass ratio (SBR) and equivalence ratio (ER) on productgas composition heating value and gasification efficiencyGasification study was conducted by keeping biomass massflow rate as 10 kgh and varying temperature and SBR andER in the ranges of 800 to 1800K 0 to 2 and 015 to 04respectively

6 Results and Discussion

61 Effect of ER SBR and Temperature on Gas CompositionThe influence of ER and temperature on product gas com-position is depicted through Figures 5ndash8 It is observed thatall the gas species except CO

2are decreasing with ER This

is due to shifting of the process more towards combustionby the addition of more and more air Similar effect of ER


01

015

02

025

03

035

04

045800

10001200

14001600

1800

0

10

20

30

40

Temperature (K)

ER

H2

mol

e fra

ctio

n (

)

5

10

15

20

25

30

35

Figure 5 Effect of ER and temperature on H2mole fraction (SBR = 1)

01

02

03

04

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

ER

Temperature (K)

CO m

ole f

ract

ion

()

5

10

15

20

25

Figure 6 Effect of ER and temperature on CO mole fraction (SBR= 1)

on product gas composition was observed by Lim and Lee[20] and Puig-Arnavat et al [22] H

2mole fraction increases

with temperature to a maximum value and then shows a slowand gradual decrease The increase is more predominant atlower temperatures ranging from 800 to 1300K as shownin Figure 5 This is similar to the variation observed for H

2

concentration by Lv et al [36]This trend of H

2is mainly due to the effect of exothermic

water gas shift reactionAt higher temperature ranges reversalof the reaction as per Le-Chatelierrsquos principle is responsiblefor the decrease in H

2mole fraction The effect of shifting of

endothermic reactions like methane reformation and watergas towards the product side is less pronounced to the reversalof water gas shift reaction This may be the reason for theslight decrease of H

2concentration at higher temperature

ranges For SBR = 1 a maximum H2mole fraction of 3614

01

015

02025

03035

04

045

8001000

12001400

16001800

10

15

20

25

30

35

40

Temperature (K)

ER

CO2

mol

e fra

ctio

n (

)

15

20

25

30

35

Figure 7 Effect of ER and temperature on CO2mole fraction (SBR

= 1)

is obtained at a gasification temperature of 1500K and ER of015

From Figure 6 it is clear that CO concentration increaseswith temperature and the rate of increase is more at lowertemperature ranges This is due to the combined effectof endothermic char gasification water gas and methanereformation and reversal of water gas shift reaction

Effect of temperature on CO2and CH

4concentrations

is shown in Figures 7 and 8 respectively Throughoutdecrease of CO

2with temperature shows the dominance of

endothermic Boudouard reaction on the processThe decrease in CH

4with temperature is due to the com-

bined effect of the shifting of endothermic steam methane


01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16

Figure 8 Effect of ER and temperature on CH4mole fraction (SBR

= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35

Figure 9 Effect of SBR and temperature on H2mole fraction (ER =

015)

reformation reaction and exothermic methanation reactiontowards the product side and reactant side respectively

Figures 9ndash12 show the influence of SBR and temperatureon product gas composition H

2concentration seemed to

increase throughout with SBR but the rate of increasedecreases gradually This increase in H

2is due to the com-

bined effect ofwater gas steammethane reforming andwatergas shift reaction When the SBR is increased from 08 to12 the corresponding increase in H

2mole fraction is only

4 Thus increasing SBR beyond a value of unity will notcontribute much to hydrogen production compared to theenergy spent for steam generation

The adverse and favorable effect of steam addition onCO and CO

2mole fractions are depicted through Figures 10

and 11 respectively The higher rate of CO2increase at low

800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40

Figure 10 Effect of SBR and temperature on COmole fraction (ER= 015)

8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)

Figure 11 Effect of SBR and temperature on CO2mole fraction (ER

= 015)

temperature ranges is attributed to the effect of exothermicwater gas shift reaction Similar effect of increase in H

2and

CO2and decrease in CO molar concentrations with SBR at a

temperature of 988K and ER of 012 was observed in [37]

62 Effect of ER SBR and Temperature on Efficiency Influ-ence of process parameters on gasification efficiency is shownthrough Figures 13 14 and 15 For any fixed values of ER andtemperature efficiency is observed to decrease with SBR

The decrease in efficiency with increase in SBR is dueto the increased energy input in the form of steam whereasthe reason for efficiency degradation with ER is the reducedLHV of the product gas Product gas composition LHV andgas yield predicted using the modified model at differentoperating conditions are given in Table 2 It is observed thatirrespective of ER and SBR values H

2concentration is more

for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18

Figure 12 Effect of SBR and temperature on CH4mole fraction (ER = 015)

Table 2 Product gas composition LHV and gas yield at different operating conditions

119879

(K) ER SBR Product gas composition ( dry basis) LHV(MJNm3)

GAS YIELD(Nm3h)H2 CO CO2 CH4 N2

800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion

A thermodynamic equilibrium model was developed toanalyse air steam gasification of biomass The developedmodel was compared with experimental results for product

gas composition and its prediction accuracy is improved bymultiplying equilibrium constants with suitable coefficientsThe modified quasi-equilibrium model is used to conductparametric study and first law analysis on air steam gasifi-cation of coconut shell For an SBR of unity the maximum


015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85

Figure 13 Effect of ER and SBR on efficiency (T = 1500K)

01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70

Figure 14 Effect of ER and temperature on efficiency (SBR = 1)

800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70

Figure 15 Effect of temperature and SBR on efficiency (ER = 015)

mole fraction of hydrogen in the product gas was found tobe 3614 with a lower heating value of 749MJNm3 at agasification temperature of 1500K and ER of 015

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

Authors gratefully acknowledge the financial support pro-vided by MNRE through RampD project on ldquoInvestigation onbiohydrogen production by thermochemical method in flu-idised bed gasifier under catalytic support and its utilizationrdquo(no 1031812010-NT)

References

[1] G Marban and T Valdes-Solıs ldquoTowards the hydrogen econ-omyrdquo International Journal of Hydrogen Energy vol 32 no 12pp 1625ndash1637 2007

[2] J Gil J Corella M P Aznar and M A Caballero ldquoBiomassgasification in atmospheric and bubbling fluidized bed effect ofthe type of gasifying agent on the product distributionrdquoBiomassand Bioenergy vol 17 no 5 pp 389ndash403 1999

[3] RWarnecke ldquoGasification of biomass comparison of fixed bedand fluidized bed gasifierrdquo Biomass and Bioenergy vol 18 no 6pp 489ndash497 2000

[4] M Puig-Arnavat J C Bruno and A Coronas ldquoReview andanalysis of biomass gasification modelsrdquo Renewable and Sus-tainable Energy Reviews vol 14 no 9 pp 2841ndash2851 2010

[5] D Baruah andD C Baruah ldquoModeling of biomass gasificationa reviewrdquoRenewable and Sustainable Energy Reviews vol 39 pp806ndash815 2014

[6] P Basu Biomass Gasification and Pyrolysis Practical Design andTheory Academic Press London UK 1st edition 2010

[7] R Kempegowda S Assabumrungrat and N LaosiripojanaldquoThermodynamic analysis for gasification of thailand rice huskwith air steam andmixed airsteam for hydrogen-rich gas pro-ductionrdquo International Journal of Chemical Reactor Engineeringvol 8 article A158 2010

[8] M K Karmakar and A B Datta ldquoGeneration of hydrogen richgas through fluidized bed gasification of biomassrdquo BioresourceTechnology vol 102 no 2 pp 1907ndash1913 2011

[9] V B Silva and A Rouboa ldquoUsing a two-stage equilibriummodel to simulate oxygen air enriched gasification of pinebiomass residuesrdquo Fuel Processing Technology vol 109 pp 111ndash117 2013

[10] R T L Ng D K S Ng D H S Tay and W A W A K GhanildquoModelling and optimisation of gasification for palm kernelshell (PKS) in a fluidized bed gasifierrdquo Chemical EngineeringTransactions vol 29 pp 1123ndash1128 2012

[11] C Loha P K Chatterjee and H Chattopadhyay ldquoPerformanceof fluidized bed steam gasification of biomassmdashmodeling andexperimentrdquo Energy Conversion and Management vol 52 no3 pp 1583ndash1588 2011

[12] T Srinivas A Gupta and B V Reddy ldquoThermodynamicequilibrium model and exergy analysis of a biomass gasifierrdquo


Journal of Energy Resources Technology vol 131 no 3 ArticleID 031801 7 pages 2009

[13] A Abuadala I Dincer and G F Naterer ldquoExergy analysis ofhydrogen production from biomass gasificationrdquo InternationalJournal of Hydrogen Energy vol 35 no 10 pp 4981ndash4990 2010

[14] Z A Zainal R Ali C H Lean and K N Seetharamu ldquoPredic-tion of performance of a downdraft gasifier using equilibriummodeling for different biomass materialsrdquo Energy Conversionand Management vol 42 no 12 pp 1499ndash1515 2001

[15] S Jarungthammachote and A Dutta ldquoThermodynamic equi-librium model and second law analysis of a downdraft wastegasifierrdquo Energy vol 32 no 9 pp 1660ndash1669 2007

[16] H Ghassemi and R Shahsavan-Markadeh ldquoEffects of variousoperational parameters on biomass gasification process a mod-ified equilibrium modelrdquo Energy Conversion and Managementvol 79 pp 18ndash24 2014

[17] E Azzone M Morini and M Pinelli ldquoDevelopment of anequilibrium model for the simulation of thermochemical gasi-fication and application to agricultural residuesrdquo RenewableEnergy vol 46 pp 248ndash254 2012

[18] N S Barman S Ghosh and S De ldquoGasification of biomass ina fixed bed downdraft gasifiermdasha realistic model including tarrdquoBioresource Technology vol 107 pp 505ndash511 2012

[19] H-J Huang and S Ramaswamy ldquoModeling biomass gasifi-cation using thermodynamic equilibrium approachrdquo AppliedBiochemistry and Biotechnology vol 154 no 1ndash3 pp 193ndash2042009

[20] Y Lim and U Lee ldquoQuasi-equilibrium thermodynamic modelwith empirical equations for air-steam biomass gasification influidised -bedsrdquo Fuel Processing Technology vol 128 pp 128ndash199 2014

[21] T D B Nguyen S I Ngo Y-I Lim J W Lee U-D Lee andB-H Song ldquoThree-stage steady-state model for biomass gasi-fication in a dual circulating fluidized-bedrdquo Energy Conversionand Management vol 54 no 1 pp 100ndash112 2012

[22] M Puig-Arnavat J C Bruno and A Coronas ldquoModifiedthermodynamic equilibrium model for biomass gasification astudy of the influence of operating conditionsrdquo Energy amp Fuelsvol 26 no 2 pp 1385ndash1394 2012

[23] L Damiani and A Trucco ldquoAn experimental data based cor-rection method of biomass gasification equilibrium modelingrdquoJournal of Solar Energy Engineering Transactions of the ASMEvol 132 Article ID 031011 2010

[24] K Goransson U Soderlind and W Zhang ldquoExperimental teston a novel dual fluidised bed biomass gasifier for synthetic fuelproductionrdquo Fuel vol 90 no 4 pp 1340ndash1349 2011

[25] C C Sreejith C Muraleedharan and P Arun ldquoEquilibriummodeling and regression analysis of biomass gasificationrdquoJournal of Renewable and Sustainable Energy vol 4 Article ID063124 2012

[26] S Kaewluan and S Pipatmanomai ldquoPotential of synthesis gasproduction from rubber wood chip gasification in a bubblingfluidised bed gasifierrdquo Energy Conversion andManagement vol52 no 1 pp 75ndash84 2011

[27] M Campoy A Gomez-Barea A L Villanueva and P OlleroldquoAir-steam gasification of biomass in a fluidized bed undersimulated autothermal and adiabatic conditionsrdquo Industrial andEngineering Chemistry Research vol 47 no 16 pp 5957ndash59652008

[28] A Melgar J F Perez H Laget and A Horillo ldquoThermo-chemical equilibrium modelling of a gasifying processrdquo EnergyConversion and Management vol 48 no 1 pp 59ndash67 2007

[29] T H Jayah L Aye R J Fuller and D F Stewart ldquoComputersimulation of a downdraftwood gasifier for tea dryingrdquoBiomassand Bioenergy vol 25 no 4 pp 459ndash469 2003

[30] G Boissonnet J M Seiler T Chataing G ClaudetM Brothierand C Bertrand ldquoCEA activities on biomass gasification toproduce synfuelsrdquo in Pyrolysis and Gasification of Biomass andWaste Expert Meeting Strasbourg France 2002

[31] G Boissonnet J Bayle JM Seiler et al ldquoComparison of severalbiomass steam gasification routes and technologies to producehydrogen and synfuelsrdquo in Proceedings of the 1st EuropeanHydrogen Energy Conference Grenoble France 2003

[32] G Boissonnet and J M Seiler ldquoApproche thermodynamiquedes transformations de la biomasserdquo Report-R-6025 Commis-sariat a lrsquoenergie atomique Grenoble France 2003

[33] G Boissonnet J M Seiler C Dupont and T ChataingldquoGazeification de la biomasse seche evaluation de procedespar unemethode thermodynamiquerdquo in 90 Congres Francais deGenie des Procedes Saint Nazaire France 2003

[34] H Hofbauer R Rauch P Foscolo and D Matera ldquoHydrogenrich gas from biomass steam gasificationrdquo in Proceedings of the1st World Conference and Exhibition on Biomass for Energy andIndustry Sevilla Spain June 2000

[35] S Rapagna N Jand A Kiennemann and P U Foscolo ldquoSteam-gasification of biomass in a fluidised-bed of olivine particlesrdquoBiomass and Bioenergy vol 19 no 3 pp 187ndash197 2000

[36] PM Lv Z H Xiong J Chang C ZWu Y Chen and J X ZhuldquoAn experimental study on biomass air-steam gasification in afluidized bedrdquo Bioresource Technology vol 95 no 1 pp 95ndash1012004

[37] A K James S S Helle R W Thring et al ldquoInvestigation ofair and air-steam gasification of high carbon wood ash in afluidized bed reactorrdquo Energy and Environment Research vol 4no 1 pp 1ndash24 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



researchers formulated stiochiometric equilibrium modelsfor simulating biomass gasification [7ndash13] Application ofstoichiometric models for air gasification was successfullydemonstrated by Zainal et al [14] and the modificationmethod used to augment the prediction accuracy of sim-ilar models was given by Jarungthammachote and Dutta[15] Modification of thermodynamic equilibrium model toreduce its deviation from experimental data is done mainlyby considering char conversion tar formation and introduc-ing suitable correction factors to equilibrium constants AGibbrsquos free energy minimisation model for air gasificationwas developed by Ghassemi and Shahsavan-Markadeh [16]and the prediction accuracy of the model was improvedby incorporating carbon conversion and tar formation fromAzzone et al [17] and Barman et al [18] respectively Azzoneet al [17] considered char conversion as a function ofequivalence ratio (ER) in air-steam gasification whereas tarwas incorporated by Barman et al [18] in air gasificationas a compound containing carbon hydrogen and oxygenThe deviation of stoichiometric model developed by HuangandRamaswamy [19] from thermodynamic equilibriumwasreduced by multiplying suitable coefficient with equilibriumconstants as done by Jarungammachote and Dutta [15] andLoha et al [11] for air and steam gasifications respectivelySimilar modification was applied by Lim and Lee [20] toair-steam gasification model in which char conversion wasexpressed as a function of equivalence ratio and temperatureAbuadala et al [13] included unreacted char as 5 of biomasscarbon content and tar as benzene in steam gasificationmodel A pseudoequilibrium air-steam gasification modelwith correction factors for equilibrium constants in termsof reactor temperature was developed by Ng et al [10] Thismodel considered tar as a compound containing carbonhydrogen and oxygen and char as solid carbon A threestage quasi-equilibrium model (considering pyrolysis char-gas reactions and gas phase reactions in each stage) forsteam gasification of biomass was developed by Nguyen etal [21] where empirical relations were used to reduce thedeviation from thermodynamic equilibrium Puig-Arnavatet al [22] developed a modified equilibrium model for airsteam gasification of biomass using Engineering EquationSolver (EES) Deviation of this model from pure equilibriumis minimised by considering pyrolysis heat loss in pyrolysischar and tar and particles leaving the gasifier and setting theamount of CH

4produced Review of the literatures reveals

that stoichiometric models formulated for biomass air-steamgasification considering both char and tar are limited Presentwork deals with the stoichiometric modeling of air-steamgasification considering tar and char Coconut shell a locallyavailable nonedible biomass waste is the feedstock selectedfor the study

2 Model Development

In general thermodynamic equilibrium calculations are inde-pendent of gasifier design and are suitable for analysingthe effect of fuel and process parameters [6] These modelsare more appropriate for simulating entrained flow gasifiers

in chemical process simulators or for downdraft fixed-bedgasifiers as long as high temperature and gas residence timeare achieved The objective of present work is to develop athermodynamic equilibriummodel to simulate fluidised bedbiomass gasifier

Biomass gasification being a complex process its theoret-ical modeling requires certain assumptionsThe assumptionsused to formulate the biomass gasification process are asfollows

(i) Gasifier is considered as a steady state system withuniform temperature and pressure throughout

(ii) The residence time of the gases in the gasifier is highenough to establish thermodynamic and chemicalequilibria

(iii) All the gases behave ideally(iv) Gases except H

2 CO CO

2 CH4 and N

2are consid-

ered dilute(v) N2is considered as inert in the entire process

(vi) Biomass is considered to be made up of carbonhydrogen and oxygen

(vii) Steam is supplied under superheated condition of 1bar and 300∘C

By considering chemical formula of feedstock as CH119883O119885

global gasification reaction can be written as

119899119887CH119883O119885+ 119908H

2O(119897)+ 119904H2O(119892)+ 119898O

2+ 376119898N

2

997888rarr 119899H2

H2+ 119899COCO + 119899CO

2

CO2+ 119899CH

4

CH4

+ 119899H2OH2O(119892) + (1 minus 120572)C + 119899tartar + 376119898N2

(1)

where 119883 and 119885 are the number of atoms of hydrogen andoxygen for each atom of carbon per mole of biomass119908 is theamount of moisture present in dry ash free biomass 119904 and119898are the amount of steam and oxygen supplied respectivelyOn the right hand side 119899H

2

119899CO 119899CO2

119899CH4

and 119899tar are thenumbers of moles of H

2 CO CO

2 CH4 and tar and 120572 is the

char conversion factor which can be expressed as a functionof equivalence ratio [23] given by

120572 = 032 + 082 (1 minus 119890minusER0229) (2)

Tar is incorporated in the model as a mixture of benzenetoluene and naphthalene in 1 25 65 proportions by weight[24 25] and its yield can be obtained as a weight percentageof the total gasification products using the following [20]

Tarwt = 3598 exp (minus00029119879) (3)

where 119879 is the temperatureTotal weight of the gasification product is obtained by

applying mass balance to the global reaction between thereactants and the products Somass of tar yield (119898tar) is givenby

119898tar =Tarwt100

(biomass feed + SBR lowast biomass feed

+ moisture in biomass + air supplied) (4)



carbon balance

119899CO + 119899CO2

+ 119899CH4

+ 6119899C6H6

+ 7119899C7H8

+10119899C10H8

+ (1 minus 120572) minus 119899119887= 0

(5)

hydrogen balance

2119899H2

+ 4119899CH4

+ 2119899H2O + 6119899C

6H6

+ 8119899C7H8

+8119899C10H8


(6)

oxygen balance

119899CO + 2119899CO2




2+H2 (8)


reaction

1198701=

119899CO2

119899H2

119899CO119899H2O (9)



4 (10)


1198702=

119899total 119899CH4

(119899H2

)

2 (11)


2can be


1198701= exp (5878

119879


(12)

1198702= exp(7082842

119879


2

119879

minus

2167 times 10minus6

6

1198792+


21198792+ 32541)

(13)



LHV = 1079119884H2

+ 1226119884CO + 3581119884CH4

(14)




S 034


120578gas

=


(15)


3 Model Validation



2

119873

(16)








1and 119870

2



0

5

10

15

20

25

30

35

40

Mol

e fra

ctio

n (

)




T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1085SB

R=0ER

=035

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033


0

5

10

15

20

25

30

Mol

e fra

ctio

n (

)


H2 (E)H2 (M1)CO (E)CO (M1)CO2 (E)


T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033

T=1085SB

R=0ER

=035



2

22

24

26

28

3

32

34

36

38

02 04 06 08 1 12 14 16 18 2 22

RMS

erro

r



0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70

RMS

erro

r




5 Model Application







01

015

02

025

03

035

04

045800

10001200

14001600

1800

0

10

20

30

40

Temperature (K)

ER

H2

mol

e fra

ctio

n (

)

5

10

15

20

25

30

35


01

02

03

04

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

ER

Temperature (K)

CO m

ole f

ract

ion

()

5

10

15

20

25





2








01

015

02025

03035

04

045

8001000

12001400

16001800

10

15

20

25

30

35

40

Temperature (K)

ER

CO2

mol

e fra

ctio

n (

)

15

20

25

30

35


= 1)




4concentrations







01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16


= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35


015)





2is due to the com-







800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40


8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)


= 015)


2and






for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











carbon balance

119899CO + 119899CO2

+ 119899CH4

+ 6119899C6H6

+ 7119899C7H8

+10119899C10H8

+ (1 minus 120572) minus 119899119887= 0

(5)

hydrogen balance

2119899H2

+ 4119899CH4

+ 2119899H2O + 6119899C

6H6

+ 8119899C7H8

+8119899C10H8


(6)

oxygen balance

119899CO + 2119899CO2




2+H2 (8)


reaction

1198701=

119899CO2

119899H2

119899CO119899H2O (9)



4 (10)


1198702=

119899total 119899CH4

(119899H2

)

2 (11)


2can be


1198701= exp (5878

119879


(12)

1198702= exp(7082842

119879


2

119879

minus

2167 times 10minus6

6

1198792+


21198792+ 32541)

(13)



LHV = 1079119884H2

+ 1226119884CO + 3581119884CH4

(14)




S 034


120578gas

=


(15)


3 Model Validation



2

119873

(16)








1and 119870

2



0

5

10

15

20

25

30

35

40

Mol

e fra

ctio

n (

)




T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1085SB

R=0ER

=035

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033


0

5

10

15

20

25

30

Mol

e fra

ctio

n (

)


H2 (E)H2 (M1)CO (E)CO (M1)CO2 (E)


T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033

T=1085SB

R=0ER

=035



2

22

24

26

28

3

32

34

36

38

02 04 06 08 1 12 14 16 18 2 22

RMS

erro

r



0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70

RMS

erro

r




5 Model Application







01

015

02

025

03

035

04

045800

10001200

14001600

1800

0

10

20

30

40

Temperature (K)

ER

H2

mol

e fra

ctio

n (

)

5

10

15

20

25

30

35


01

02

03

04

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

ER

Temperature (K)

CO m

ole f

ract

ion

()

5

10

15

20

25





2








01

015

02025

03035

04

045

8001000

12001400

16001800

10

15

20

25

30

35

40

Temperature (K)

ER

CO2

mol

e fra

ctio

n (

)

15

20

25

30

35


= 1)




4concentrations







01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16


= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35


015)





2is due to the com-







800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40


8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)


= 015)


2and






for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0

5

10

15

20

25

30

35

40

Mol

e fra

ctio

n (

)




T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1085SB

R=0ER

=035

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033


0

5

10

15

20

25

30

Mol

e fra

ctio

n (

)


H2 (E)H2 (M1)CO (E)CO (M1)CO2 (E)


T=1053SB

R=0ER

=019

T=1025SB

R=018ER

=023

T=1000SB

R=028ER

=019

T=1078SB

R=0ER

=027

T=1059SB

R=027ER

=027

T=1028SB

R=043ER

=027

T=1077SB

R=022ER

=033

T=1062SB

R=045ER

=033

T=1085SB

R=0ER

=035



2

22

24

26

28

3

32

34

36

38

02 04 06 08 1 12 14 16 18 2 22

RMS

erro

r



0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70

RMS

erro

r




5 Model Application







01

015

02

025

03

035

04

045800

10001200

14001600

1800

0

10

20

30

40

Temperature (K)

ER

H2

mol

e fra

ctio

n (

)

5

10

15

20

25

30

35


01

02

03

04

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

ER

Temperature (K)

CO m

ole f

ract

ion

()

5

10

15

20

25





2








01

015

02025

03035

04

045

8001000

12001400

16001800

10

15

20

25

30

35

40

Temperature (K)

ER

CO2

mol

e fra

ctio

n (

)

15

20

25

30

35


= 1)




4concentrations







01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16


= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35


015)





2is due to the com-







800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40


8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)


= 015)


2and






for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










01

015

02

025

03

035

04

045800

10001200

14001600

1800

0

10

20

30

40

Temperature (K)

ER

H2

mol

e fra

ctio

n (

)

5

10

15

20

25

30

35


01

02

03

04

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

ER

Temperature (K)

CO m

ole f

ract

ion

()

5

10

15

20

25





2








01

015

02025

03035

04

045

8001000

12001400

16001800

10

15

20

25

30

35

40

Temperature (K)

ER

CO2

mol

e fra

ctio

n (

)

15

20

25

30

35


= 1)




4concentrations







01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16


= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35


015)





2is due to the com-







800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40


8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)


= 015)


2and






for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










01

015

02

025

03

035

04

045

8001000

12001400

1600

1800

0

5

10

15

20

Temperature (K)

ER

CH4

mol

e fra

ctio

n (

)

2

4

6

8

10

12

14

16


= 1)

8001000

12001400

16001800

0

04

08

12

16

25

10

15

20

25

30

35

40

Temperature (K)SBR

H2

mol

e fra

ctio

n (

)

10

15

20

25

30

35


015)





2is due to the com-







800

1000

1200

1400

1600

1800

0

04

08

12

16

20

10

20

30

40

50

Temperature (K)SBR

CO m

ole f

ract

ion

()

5

10

15

20

25

30

35

40


8001000 1200

14001600

1800

0

05

1

15

20

5

10

15

20

25

30

35

40

Temperature (K)

SBR 5

10

15

20

25

30

35

CO2

mol

e fra

ctio

n (

)


= 015)


2and






for 1500K


800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










800 1000 1200 1400 1600 1800

0

0408

1216

2

0

2

4

6

8

10

12

14

16

18

20

SBR

Temperature (K)

CH4

mol

e fra

ctio

n (

)2

4

6

8

10

12

14

16

18



119879



800

02 04 593 115 3325 148 4447 608 14716 716 041 3348 1209 4526 515 141

03 04 458 082 2959 892 5569 379 17616 508 029 2958 659 5686 294 169

04 04 335 055 2698 478 6394 214 20516 32 018 2683 277 6542 135 196

1300

02 04 2744 2532 1328 218 3138 693 20716 3254 1500 2048 208 283 614 221

03 04 2122 2044 1448 129 4217 533 23216 2552 1217 2037 130 3904 475 243

04 04 1597 1613 1574 072 5104 401 25616 1948 961 2044 077 481 359 265

1500

02 04 2887 2953 1000 078 3082 712 21416 3391 1957 1695 0746 2882 715 228

03 04 2186 2436 1157 044 4177 559 23616 2645 1616 1741 046 3952 570 250

04 04 1615 1967 1318 024 5076 431 25916 2015 1304 1796 027 3082 391 271

1800

02 04 2885 3262 770 023 3018 731 21516 3361 2351 1405 021 2702 666 230

03 04 2146 2744 934 012 4124 582 23716 2599 1973 1482 013 3773 534 251

04 04 1553 2259 1107 006 5035 455 26016 1966 1623 1568 007 4676 419 272

7 Conclusion




015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










015020250303504

0

04

08

12

16

2

40

50

60

70

80

90

SBR

ER

Firs

t law

effici

ency

()

45

50

55

60

65

70

75

80

85


01

0203

04

800

1000

12001400

1600

180010

20

30

40

50

60

70

80

ER

Temperature (K)

Firs

t law

effici

ency

()

20

30

40

50

60

70


800

10001200

14001600

1800

0

04

08

12

162

20

30

40

50

60

70

80

Temperature (K)SBR

Firs

t law

effici

ency

()

30

40

50

60

70





Acknowledgment


References










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article analysis of hydrogen generation through...

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