Modelling and optimisation of biomass fluidised bed gasifier

Accepted Manuscript Modelling and optimisation of biomass fluidised bed gasifier Rex T.L Ng, Douglas H.S Tay, Wan Azlina Wan Ab Karim Ghani, Denny K.S Ng PII: S1359-4311(13)00226-3 DOI: 10.1016/j.applthermaleng.2013.03.048 Reference: ATE 4717 To appear in: Applied Thermal Engineering Received Date: 14 November 2012 Accepted Date: 26 March 2013 Please cite this article as: R.T.L Ng, D.H.S Tay, W.A Wan Ab Karim Ghani, D.K.S Ng, Modelling and optimisation of biomass fluidised bed gasifier, Applied Thermal Engineering (2013), doi: 10.1016/ j.applthermaleng.2013.03.048 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain Abstract.docx MODELLING AND OPTIMISATION OF BIOMASS FLUIDISED BED GASIFIER Rex T L Nga,b, Douglas H S Taya, Wan Azlina Wan Ab Karim Ghanic, Denny K S Nga* a Department of Chemical and Environmental Engineering/ Centre of Excellence for Green Technologies, University of Nottingham, Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia b GGS Eco Solutions Sdn Bhd., Wisma Zelan, Suite G.12A & 1.12B, Ground Floor, No 1, Jalan Tasik Permaisuri 2,Bandar Tun Razak, Cheras, 56000 Kuala Lumpur, Malaysia c Department of Chemical & Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Email: ngtonglip@gmail.com; taydouglas@yahoo.com; wanaz@eng.upm.edu.my; *Denny.Ng@nottingham.edu.my; *Tel: +6(03) 8924 8606; Fax: +6(03) 8924 8017 ABSTRACT Recently, biomass for bioenergy and biofuel via gasification has become of great interest to energy and fuels producers and recognised as a promising first processing step in an integrated biorefinery due to green and renewable technology In this work, a stoichiometric equilibrium model of biomass fluidised bed gasifier is developed and followed by model improvement include a correction factor to the equilibrium constants with a function of temperature To illustrate the proposed model, bagasse is taken as the feedstock and gasification modelling based on the experiment result of a fluidised bed gasifier are presented To ensure the accuracy of the model, predicted syngas compositions are validated with the experimental results Besides, the proposed model is also reformulated for different types of biomass feedstock (e.g., rice husk, coconut shell, etc.) Based on the developed models, the operating condition of the gasifier can be optimised and the composition of the syngas can also be determined Keywords: Biomass; Gasification; Modelling; Optimisation; Biorefinery ACCEPTED MANUSCRIPT Introduction Biomass has been distinguished as one of the promising renewable energy sources for power generation and production of chemicals It could be converted into various types of RI PT value-added products via various conversion technologies (e.g., biological, thermochemical, physical, etc.) Biorefinery, a facility which has a similar concept to petroleum refinery, has been proposed to integrate the biomass conversion processes and equipment to produce SC various biochemicals, biofuels and bioenergy with minimum waste generation [1] The concept of integrated biorefinery, which integrates multiple platforms as a whole, has been M AN U proposed to have more flexibility in product generation [2] In integrated biorefinery, the overall energy consumption is lower compared to technology that operates independently [3] Various works have been presented in systematic design of integrated biorefineries based on mathematical optimisation models, such as modular optimisation [4], fuzzy optimisation [5], etc Recently, Martín and Grossmann [6] reviewed the recent works on integration of TE D production processes, including first, second and third generation of biofuels Note that gasification process is recognised as one of the most promising technologies EP to convert biomass into energy and value-added products in an integrated biorefinery [7] Therefore, it is essential to further analyse and optimise the gasification process in order to AC C increase the overall performance of the integrated biorefinery Gasification typically operates in a temperature range of 600˚C – 1400˚C [8], with a controlled supply of oxygen and/or steam to convert biomass into a gaseous mixture which is commonly known as synthesis gas or syngas These gas mixtures consist of carbon dioxide (CO2), steam (H2O), methane (CH4), carbon monoxide (CO) and hydrogen (H2) Other by-products include gaseous hydrocarbons (CHs), tars, char, inorganic constituents, and ashes were also produced from the gasification process other than production of syngas [9] Syngas produced from biomass gasification ACCEPTED MANUSCRIPT through the robust thermal conversion can be used as feedstock for the production of liquid fuels and chemicals as well as the generation of heat and power [10] Waste heat generated in the biomass gasification system can be utilised in utility systems through heat integration RI PT [11] Generally, gasification modelling is predominantly divided into two categories: SC kinetic modelling and thermodynamic equilibrium modelling [12] As shown in literature [12], there are two methods for thermodynamic equilibrium modelling which are M AN U stoichiometric and non-stoichiometric equilibrium The stoichiometric method is based on stoichiometric reactions while non-stoichiometric method is based on minimising the total Gibbs free energy in the system It is noted that equilibrium models with or without char formation were widely studied and presented for predicting syngas composition, coal, and wastes However, in the actual gasification system, equilibrium chemical reactions are hardly TE D entertained due to kinetic limitations [13] Jarungthammachote and Dutta [14] presented a thermodynamic model based on equilibrium constant for predicting the syngas composition of a downdraft waste gasifier without considering the char The model was also enhanced by EP multiplying equilibrium constants with equilibrium constant Later, Huang and Ramaswamy [15] further enhanced the prediction of gas composition by considering and without AC C considering char formation via the same approach However, the prediction of syngas composition via the equilibrium model with char formation does not match well with experimental results Recently, Barman et al [16] presented an equilibrium model for fixed bed downdraft biomass gasifier by considering tar production Modification to the model was made to upgrade their equilibrium model to match experimental data Conceptual designs of gasification-based biorefineries using thermodynamic equilibrium optimisation mathematical model [10] and graphical targeting approach via C-H2 ACCEPTED MANUSCRIPT O ternary diagram [17] was presented Oliveira and Silva [18] developed kinetic mathematical model in predicting the temperature and mole fraction of syngas A nonstoichiometric equilibrium model for a downdraft gasifier was developed in order to predict RI PT syngas composition from miscanthus, olive wood and cardoon gasification [19] On the other hand, process modelling of biomass gasification combined heat and power plant by considering tars in syngas was simulated via ASPEN Plus [20] More recently, SC Computational Fluid Dynamics (CFD) modelling was extended to predict characteristics of fuels in combustion and gasification processes of fluidized bed gasifier [21] A steady state M AN U mathematical model of circulating fluidized bed biomass gasifier had also been presented by integrating the hydrodynamics, gasification reactions, as well as heat and mass balances [22] As shown in the previous works done by Jarungthammachote and Dutta [14], Huang and Ramaswamy [15] and Barman et al [16], the gasification models were improved by TE D introducing correction factors to modify the equilibrium constants in each reaction However, the proposed models are limited to single temperature None of the abovementioned modelling works developed for gasification process at different temperatures based on EP experimental data Most recently, Ng et al [23] proposed a modelling of biomass gasifier for palm kernel shell (PKS) with integrating a function of temperature However, the presented AC C gasification modelling was limited to one type of biomass feedstock This paper describes a systematic approach of gasifier modelling which can be used to predict syngas composition for different types of biomass To illustrate the proposed approach, a stoichiometric equilibrium model of biomass fluidised bed gasifier is first developed Experimental results of bagasse are used to validate the developed model Note that the proposed approach can be reformulated easily for different biomass feedstock (rice ACCEPTED MANUSCRIPT husk and coconut shell) by changing its empirical formula into proposed model The developed model is then further improved by including correction factors to the equilibrium constants with a function of temperature In order to ensure the accuracy of the model, RI PT predicted syngas composition is then validated with the experimental results (e.g., bagasse [24], rice husk [25] and coconut shell [24]) Based on the developed models, the gasifier for different biomass feedstocks can be optimised to achieve various objectives, such as SC maximum hydrogen production A case study with different biomass feedstock was solved to M AN U illustrate the approach Biomass Gasification Modelling As shown in the literature [10], biomass can be generally defined as CaHbOcNd which can be determined from the ultimate analysis of biomass Ultimate analysis gives the compositions of the biomass in weight percentage of carbon (C), hydrogen (H), oxygen (O) TE D and nitrogen (N) The overall gasification reaction with air (79% N2 and 21% O2), steam (H2O), and CO2 can be written as [10]: CaHbOcNd + (w + v) H2O + hO2 + (79/21)hN2 + jCO2 → EP n1H2 + n2CO + n3CO2 + n4H2O + n5CH4 + n6N2 + n7C where a, b, c, and d represent the number of atoms of carbon (C), hydrogen (H), oxygen (O) AC C and nitrogen (N) of biomass; w, is stoichiometric coefficient (per mole of biomass feedstock) of biomass moisture, h is stoichiometric coefficient (per mole of biomass feedstock) of oxygen and nitrogen which supplied as gasification agent from air, v and j are stoichiometric coefficients (per mole of biomass feedstock) of steam and carbon dioxide as gasification agent; n1 – n7 are the stoichiometric coefficients of H2, CO, CO2, H2O, CH4, N2 and solid carbon (C) ACCEPTED MANUSCRIPT Based on the experiment results [24], other than syngas and solid carbon, by-products such hydrocarbons (CHs), tars, inorganic constituents, and ash are obtained throughout the experiment More details on the experiments can be found in [24] Since the results show RI PT additional products are generated, the gasification reaction [10] presented previously is modified by introducing an additional term, CxHyOz to represent the formation of hydrocarbons The modified overall gasification reaction is written as below: SC CaHbOcNd + (w + v) H2O + hO2 + (79/21)hN2 + jCO2 → n1H2 + n2CO + n3CO2 + n4H2O + n5CH4 + n6N2 + n7C + n8CxHyOz M AN U where x, y, and z represent the number of atoms of C, H, and O of hydrocarbons; n8 are the stoichiometric coefficient of hydrocarbons (CxHyOz) Note that the overall gasification reaction can be governed by the overall mass balance, enthalpy balance and thermodynamic equilibrium equations As the main objective TE D of this work is to predict and optimise the syngas composition without considering additional heat transferred into the gasifier, therefore, the enthalpy balance can be neglected In this study, only mass balance and thermodynamic equilibrium equations are taken into account in EP modelling work To further simplify the modelling efforts, the gasifier is assumed to operate under steady state conditions and atmospheric pressure [26] Other than that, syngas behaves AC C as an ideal gas, whereas ash and N2 are assumed to be inert at high temperature [10] With such assumptions, the complexity of gasification model can be significantly reduced Furthermore, the computation time for solving the model can also be reduced However, the proposed model is able to generate reasonable prediction of the syngas composition as compared with the experimental results ACCEPTED MANUSCRIPT Based on modified gasification reaction, the atomic balances of C, H, O and N in biomass gasification are expressed as followed: a fi + j = n2 + n3 + n5+ n7 + xn8 (1) H: bfi + 2(w + v) = 2n1 + 2n4 + 4n5 + yn8 (2) O: cfi + (w + v) + 2h + 2j = n2 + 2n3 + n4 + zn8 (3) N: dfi + (79/21)h = 2n6 RI PT C: (4) SC where fi is the molar flowrate of the biomass i M AN U In a thermodynamic equilibrium model of gasification, five reactions that involve all chemical species are considered Boudouard equilibrium, methane decomposition and heterogeneous water-gas shift reactions are endothermic reactions (positive value of heat of reaction); while hydrogenating gasification and water-gas shift reactions are exothermic reactions (negative value of heat of reaction) [27] TE D Boudouard Equilibrium: C(s) + CO2 ↔ 2CO ∆H0rxn = +172.67MJ/kmol Hydrogenating Gasification: EP C(s) + 2H2 ↔ CH4 ∆H0rxn = -74.94 MJ/kmol Methane Decomposition: AC C CH4 + H2O ↔ CO + 3H2 ∆H0rxn = +206.2 MJ/kmol Water-Gas Shift Reaction: CO + H2O ↔ CO2 + H2 ∆H0rxn = -41.21 MJ/kmol Heterogeneous Water-Gas Shift Reaction C(s) + H2O ↔ CO + H2 ∆H0rxn = +131.46 MJ/kmol ACCEPTED MANUSCRIPT As shown in previous work [28], two independent reactions of five reactions need to be considered in the case where no solid carbon remains in the equilibrium state (n7= 0) Nevertheless, in the case where solid carbon remains as a gasification product because of an RI PT oxidant deficit, three independent reactions are needed for the equilibrium calculations In this study, since solid carbon (i.e., ash) remains as a significant gasification product obtained in the experiment; thus, the formation of solid carbon cannot be neglected Three independent SC reactions are needed for the equilibrium calculations Methane decomposition, water-gas shift reaction and heterogeneous water gas-shift reactions are selected to represent the interaction M AN U of all components In order to determine the syngas composition, equilibrium constant of three selected reactions are required to be included in the model Although all gaseous components are assumed to be ideal gases, the reactions might not interact ideally The models can be further TE D altered by multiplying the equilibrium constants with correction factors (α1 – α3) to yield a corrected activity coefficient of reactants and products [14-16] The corrected equilibrium constants corresponding to stoichiometric coefficients of components are shown as below: m13 m 2 P m m5 EP α K MD = (5) m1 m m2 m4 (6) α3 K HWGS = m1m2 P m4 (7) AC C α K WGS = where KMD, KWGS and KHWGS are equilibrium constants for methane decomposition, watergas shift reaction and heterogeneous water gas-shift reaction; m1 – m5 is the molar fraction of H2, CO, CO2, H2O, and CH4; P is the operating pressure of the gasifier ACCEPTED MANUSCRIPT References [1] National Renewable Energy Laboratory (NREL), Biomass Research, (9.3.2012) S Fernando, S Adhikari, C Chandrapal, N Murali, Biorefineries: Current status, RI PT [2] challenges, and future direction, Energy Fuels 20 (1) (2006) 1727–1737 [3] D.K.S Ng, Automated targeting for synthesis of an integrated biorefinery, Chem Eng [4] SC J 162 (1) (2010) 67−74 R.T.L Ng, D.H.S Tay, D.K.S Ng, Simultaneous process 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including tar, Bioresour Technol 107 (2012) 505–511 D.H.S Tay, H Kheireddine, D.K.S Ng, M.M El-Halwagi, Synthesis of an integrated EP [17] biorefinery via the C-H-O ternary diagram, Clean Techn Environ Policy 13 (4) (2011) AC C 567–579 [18] C Oliveira, J D da Silva, Dynamic modelling of the gasification region of a bubbling fluidized bed gasifier, Chem Eng Trans 29 (2012) 841-846 [19] I.-S Antonopoulos, A Karagiannidis, A Gkouletsos, G Perkoulidis, Modelling of a downdraft gasifier fed by agricultural residues, Waste Management, 32 (2012) 710718 21 ACCEPTED MANUSCRIPT [20] J François, L Abdelouahed, G Mauviel, M Feidt, C Rogaume, O Mirgaux, F Patisson, A Dufour, Estimation of the energy efficiency of a wood gasification CHP plant using Aspen Plus, Chem Eng Trans 29 (2012) 769-774 R I Singh, A Brink, M Hupa, CFD modeling to study fluidized bed combustion and gasification, Appl Therm Eng 52 (2013) 585-614 [22] RI PT [21] Q Miao, J Zhu, S Barghi, C Wu, X Yin, Z Zhou, Modeling biomass gasification in [23] SC circulating fluidized beds, Renewable Energy 50 (2013) 655-661 R.T.L Ng, D.K.S Ng, D.H.S Ng, W.A Wan Ab Karim Ghani Modelling and M AN U optimisation of gasification for palm kernel shell (PKS) in a fluidized bed gasifier, Chem Eng Trans 29 (2012) 1123-1128 [24] W.A Wan Ab Karim Ghani, R.M Esfahani, M.A Mohd Salleh Air gasification of Malaysia agricultural waste in a fluidised bed gasifier: Hydrogen production performance, In: M Nayeripur, M Khesti (Eds.), Sustainable Growth and [25] TE D Applications in Renewable Energy Sources, In Tech, 2011, pp.227–242 R.A Moghadam, W.A Wan Ab Karim Ghani, M.A Mohd Salleh, A Tavassoli, Hydrogen-rich gas production from rice husk by applying air gasification in fluidized EP bed reactor, In: International Seminar on advanced renewable energy technology (ISRAET), Malaysia (2009), Lecture 12 AC C (13.6.2012) [26] Y Zhang, J Xiao, L Shen, Simulation of methanol production from biomass gasification in interconnected fluidized beds, Ind Eng Chem Res 48 (2009) 5351– 5359 [27] D.A Bell, B.F Towler, M Fang, Coal Gasification and Its Applications, Elsevier, Oxford, 2011, pp 38–39 22 ACCEPTED MANUSCRIPT [28] A Mountouris, E Voutsas, D Tassios, Solid waste plasma gasification: Equilibrium model development and exergy analysis, Energy Convers Manage 47 (2006) 1723– 1737 R.E Baron, J.H Porter, O.H Hammond, Chemical equilibria in Carbon-HydrogenOxygen systems, The MIT Press, Cambridge, USA, 1976 [30] RI PT [29] C Gau, L E Schrage, Implementation and testing of a branch-and-bound based SC method for deterministic global optimization: Operations Research Applications In: Frontiers in Global Optimization C A Floudas, P M Pardalos, Kluwer Academic [31] M AN U Publishers, Boston, USA, 2003; pp 145-164 F Weinhold, Classical and geometrical theory of Chemical and Physical Thermodynamic, John Wiley & Sons Inc, New Jersey, USA, 2009, pp 288 [32] F.A Bettelheim, W.H Brown, M.K Capmbell, S.O Farrell, Introduction to General, Organic, and Biochemistry, 8th ed, Brooks/Cole, Belmond, USA, 2009, pp 232 N Sammes, Fuel Cell Technology Reaching Towards Commercialization, Springer, TE D [33] AC C EP Colorado, USA, 2006, pp 182 23 ACCEPTED MANUSCRIPT Highlights of research RI PT Modelling of a biomass fluidised bed gasifier Systematic optimisation tool to predict the syngas composition Validation of predicted syngas composition with experimental results Targeting the maximum hydrogen production of agriculture waste Optimise the operating conditions of a biomass fluidised bed gasifier AC C EP TE D M AN U SC • • • • • ACCEPTED MANUSCRIPT List of Table Table Performance data on fluidized bed gasifier at different operating temperature [24,25] Performance data and RMS errors on modified model at different operating temperature Optimised result prediction AC C EP TE D M AN U SC Table RI PT Table ACCEPTED MANUSCRIPT Table 1: Performance data on fluidized bed gasifier at different operating temperature [24,25] 1,023 1,073 1,123 1,173 1,223 1,273 1,373 Feeding rate (kg/h, Wet basis) 0.75 0.75 0.75 0.75 0.75 0.75 0.75 Air Flowrate (Nm3/h) 1.02 1.02 1.1 1.1 Equivalence Ratio 0.26 0.26 0.26 0.26 H2, n1 5.400 6.089 6.268 8.119 8.834 9.966 10.737 CO, n2 2.384 2.540 2.644 2.868 1.937 1.825 1.713 CO2, n3 2.533 1.937 1.490 1.490 0.931 0.793 0.745 CH4, n5 4.469 3.911 3.661 3.210 2.737 2.391 2.086 Ash, n7 8.750 7.000 5.250 4.125 3.688 2.938 2.500 6.443 7.821 8.268 8.976 9.460 9.944 10.130 2.235 2.272 2.607 3.017 3.240 2.458 2.309 2.458 1.490 1.341 1.378 1.471 1.155 1.564 CH4, n5 2.346 3.017 3.799 3.575 3.501 3.177 2.309 Ash, n7 18.125 16.875 16.250 13.750 12.188 11.688 11.500 1 0.26 0.26 0.26 TE D M AN U Bagasse, CH1.452O0.807N0.023 SC Gas composition (mole) RI PT Temperature, T (K) H2, n1 CO, n2 AC C CO2, n3 EP Rice husk, CH1.644O0.63N0.008 Coconut Shell, CH1.291O0.648N0.025 H2, n1 8.816 9.210 9.609 10.413 11.024 11.620 11.844 CO, n2 2.942 3.203 4.358 4.916 3.128 2.384 2.160 CO2, n3 1.840 1.155 1.613 1.825 1.013 0.857 0.745 ACCEPTED MANUSCRIPT CH4, n5 3.203 3.799 4.022 2.354 2.190 2.086 1.974 Ash, n7 9.063 8.250 7.125 6.250 5.750 4.750 4.375 H2, n1 6.555 7.821 10.652 10.912 12.425 13.296 14.451 CO, n2 2.235 3.017 3.426 3.650 3.277 3.389 2.682 CO2, n3 2.458 1.490 1.557 1.341 1.117 1.117 0.894 CH4, n5 3.091 3.985 3.091 2.495 3.017 2.719 1.564 Ash, n7 10.875 9.875 8.875 8.250 7.500 7.125 6.250 AC C EP TE D M AN U SC RI PT PKS, CH1.283O0.594N0.031 ACCEPTED MANUSCRIPT Table 2: Performance data and RMS errors on modified model at different operating Bagasse, CH1.452O0.807N0.023 1,073 1,123 mole RMS mole 1,173 RMS mole 6.151 7.134 8.054 n2 2.633 2.662 2.512 n3 1.977 n5 3.847 3.436 n7 6.715 5.573 0.141 1.67 RMS mole 1,273 RMS mole 8.903 9.739 2.183 1.903 M AN U n1 1,223 SC T (K) RI PT temperature 0.433 1.359 0.251 1.034 0.148 0.814 3.097 2.859 2.586 4.521 3.552 2.456 1,223 1,273 RMS 0.262 TE D Average RMS Errors = 0.247 Rice husk, CH1.644O0.63N0.008 1,073 mole 6.949 RMS AC C n1 1,123 EP T (K) mole 1,173 RMS mole RMS mole RMS mole 7.963 8.778 9.373 9.938 n2 2.588 2.861 2.898 2.682 2.526 n3 1.417 0.616 1.426 0.407 1.357 0.126 1.207 0.371 1.143 n5 3.769 3.615 3.484 3.414 3.296 n7 17.559 15.458 13.879 12.728 11.586 Average RMS Errors = 0.319 Coconut Shell, CH1.291O0.648N0.025 RMS 0.077 ACCEPTED MANUSCRIPT 1,073 1,123 mole RMS mole 1,173 RMS mole 1,223 RMS mole 9.427 10.2 10.774 11.16 n2 3.457 3.502 3.317 2.898 n3 1.585 0.448 1.407 0.733 1.213 0.786 0.985 n5 3.145 2.791 2.524 2.355 n7 7.722 6.917 6.235 1,123 n2 3.331 n3 RMS mole 0.144 0.847 1,223 RMS mole 4.948 1,273 RMS mole 11.219 12.231 13.159 3.602 3.601 3.281 2.962 1.743 0.509 1.610 0.338 1.409 0.335 1.134 0.115 0.924 n5 3.475 3.319 3.173 3.069 2.903 n7 10.345 9.117 8.215 7.658 7.093 Average RMS Errors = 0.306 0.142 2.167 9.999 TE D 8.627 mole AC C n1 RMS 1,173 RMS 2.568 EP mole M AN U 1,073 mole 11.479 5.688 Average RMS Errors = 0.452 T (K) RMS SC n1 PKS, CH1.283O0.594N0.031 [23] 1,273 RI PT T (K) RMS 0.234 ACCEPTED MANUSCRIPT Table 3: Optimised result prediction Rice Husk Coconut Shell PKS [23] 1,273 1,273 1,273 1,273 - 0.27 0.27 0.27 0.27 H2, n1 mol 9.76 10.07 11.54 13.26 CO, n2 mol 2.04 2.73 2.77 3.18 CO2, n3 mol 0.90 1.26 0.94 1.02 CH4, n5 mol 2.49 3.21 2.09 2.82 Ash, n7 mol 2.34 11.35 4.73 6.86 Temperature K ER M AN U Mole Basis RI PT Bagasse SC Unit TE D Mass Basis g/kg biomass 20.96 21.62 24.78 28.48 Ash, n7 kg/kg biomass 0.04 0.18 0.08 0.11 AC C Volume Basis EP H2 Yield H2, n1 vol% 26.19 27.03 30.98 35.59 CO, n2 vol% 5.48 7.32 7.44 8.55 CO2, n3 vol% 2.41 3.39 2.53 2.74 CH4, n5 vol% 6.67 8.62 5.61 7.57 ACCEPTED MANUSCRIPT List of figures Figure Relationship between correction factors (a) α1 (b) α2 and (c) α3 with operating temperature of bagasse Relationship between R/B and operating temperature of bagasse Figure Actual equilibrium performance of bagasse AC C EP TE D M AN U SC RI PT Figure AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT Figure 1: Relationship between correction factors (a) α1 (b) α2 and (c) α3 with operating temperature of bagasse M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D Figure 2: Relationship between R/B and operating temperature of bagasse M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D Figure 3: Actual equilibrium performance of bagasse