Kinetic-compartmental modelling of potassium-containing cellulose feedstock gasification

Attila Egedy, Lívia Gyurik, Tamás Varga, Jun Zou, Norbert Miskolczi, Haiping Yang

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PDF(440 KB)
Front. Chem. Sci. Eng. ›› 2018, Vol. 12 ›› Issue (4) : 708-717. DOI: 10.1007/s11705-018-1767-y
RESEARCH ARTICLE
RESEARCH ARTICLE

Kinetic-compartmental modelling of potassium-containing cellulose feedstock gasification

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Abstract

Biomass is of growing interest as a secondary energy source and can be converted to fuels with higher energy density especially by pyrolysis or gasification. Understanding the mechanism and the kinetics of biomass pyrolysis (thermal decomposition) and gasification (conversion of organic material to gases) could be the key to the design of industrial devices capable of processing vast amounts of biomass feedstock. In our work real product components obtained in pyrolysis were took into consideration as well as char and oil as lumped components, and the kinetic constants for a biomass model compound (cellulose) pyrolysis and gasification were identified based on a proposed simplified reaction mechanism within a compartment model structure. A laboratory scale reactor was used for the physical experiments containing consecutive fast pyrolysis and gasification stages using alkali metal (K) containing feedstock, which has a significant effect on the cellulose pyrolysis and gasification. The detailed model was implemented in MATLAB/Simulink environment, and the unknown kinetic parameters were identified based on experimental data. The model was validated based on measurement data, and a good agreement was found. Based on the validated first principle model the optimal parameters were determined as 0.15 mL/min steam flow rate, and 4% K content.

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Keywords

biomass pyrolysis / kinetic parameter identification / compartment modelling / optimisation

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Attila Egedy, Lívia Gyurik, Tamás Varga, Jun Zou, Norbert Miskolczi, Haiping Yang. Kinetic-compartmental modelling of potassium-containing cellulose feedstock gasification. Front. Chem. Sci. Eng., 2018, 12(4): 708‒717 https://doi.org/10.1007/s11705-018-1767-y

References

[1]
Bridgwater A V. Review of fast pyrolysis of biomass and product upgrading. Biomass and Bioenergy, 2012, 38: 68–94
CrossRef Google scholar
[2]
Carrol A, Somerville C. Cellulosic biofuels. Annual Review of Plant Biology, 2009, 60(1): 165–182
CrossRef Google scholar
[3]
Suzdalenko V, Barmina I, Lickrastina A, Zake M. The effect of co-gasification of the biomass pellets with gas on the thermal degradation of biomass. Chemical Engineering Transactions, 2011, 24: 7–12
[4]
Sun L, Xu B, Smith R. Power and chemical production analysis based on biomass gasification processes. Chemical Engineering Transactions, 2014, 38: 61–66
[5]
Yang H, Yan R, Chen H, Lee D H, Zheng C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel, 2017, 86(12-13): 1781–1788
CrossRef Google scholar
[6]
Panepinto D, Genon G. Solid waste and biomass gasification: Fundamental processes and numerical simulation. Mathematical modelling. Chemical Engineering Transactions, 2011, 24: 25–30
[7]
Wang S, Lin H, Ru B, Dai G, Wang X, Xiao G, Luo Z. Kinetic modeling of biomass components pyrolysis using a sequential and coupling method. Fuel, 2016, 185: 763–771
CrossRef Google scholar
[8]
Lin Y, Cho J, Tompsett G, Westmoreland P R, Huber G W. Kinetics and mechanism of cellulose pyrolysis. Journal of Physical Chemistry C, 2009, 113(46): 20097–20107
CrossRef Google scholar
[9]
Shen D, Xiao R, Gu S, Zhang H. The overview of thermal decomposition of cellulose in lignocellulosic biomass. Cellulose-Biomass Conversion, 2013, 193–226
[10]
Olsson J G, Pettersson J B, Padban N, Bjerle I. Alkali metal emission from filter ash and fluidized bed material from PFB gasification of biomass. Energy & Fuels, 1998, 12(3): 626–630
CrossRef Google scholar
[11]
Cao W, Li J, Lue L, Zhang X. Release of alkali metals during biomass thermal conversion. Archives of Industrial Biotechnology, 2017, 1: 1–3
[12]
Wang S, Liu Q, Liao Y, Luo Z, Cen K. A study on the mechanism research on cellulose pyrolysis under catalysis of metallic salts. Korean Journal of Chemical Engineering, 2007, 24(2): 336–340
CrossRef Google scholar
[13]
Koven A B, Tong S S, Farnood R R, Jia C Q. Alkali-thermal gasification and hydrogen generation potential of biomass. Frontiers of Chemical Science and Engineering, 2017, 11(3): 369–378
CrossRef Google scholar
[14]
Guan Y, Pei A, Guo L. Hydrogen production by catalytic gasification of cellulose in supercritical water. Frontiers of Chemical Engineering in China, 2008, 2(2): 176–180
CrossRef Google scholar
[15]
Patwardhan P R, Satrio J A, Brown R C, Shanks B H. Influence of inorganic salts on the primary pyrolysis products of cellulose. Bioresource Technology, 2010, 101(12): 4646–4655
CrossRef Google scholar
[16]
Lobo L S, Carabineiro S A C. Kinetics and mechanism of catalytic carbon gasification. Fuel, 2016, 183: 457–469
CrossRef Google scholar
[17]
Kaushal P, Tyagi R. Advanced simulation of biomass gasification in a fluidized bed reactor using Aspen plus. Renewable Energy, 2017, 101: 629–636
CrossRef Google scholar
[18]
Lédé J. Cellulose pyrolysis kinetics: An historical review on the existence and role of intermediate active cellulose. Journal of Analytical and Applied Pyrolysis, 2012, 94: 17–32
CrossRef Google scholar
[19]
Richter F, Rein G. Pyrolysis kinetics and multi-objective inverse modelling of cellulose at the microscale. Fire Safety Journal, 2017, 91: 191–199
CrossRef Google scholar
[20]
Antal M J, Várhegyi G, Jakab E. Cellulose pyrolysis kinetics: Revisited. Industrial & Engineering Chemistry Research, 1998, 37(4): 1267–1275
CrossRef Google scholar
[21]
Goyal H, Pepiot P. A compact kinetic model for biomass pyrolysis at gasification conditions. Energy & Fuels, 2017, 31(11): 12120–12132
CrossRef Google scholar
[22]
Halama S, Spliethoff H. Numerical simulation of entrained flow gasification: Reaction kinetics and char structure evolution. Fuel Processing Technology, 2015, 138: 314–324
CrossRef Google scholar
[23]
Xiong Q, Aramideh S, Passalacqua A, Kong S C. BIOTC: An open-source CFD code for simulating biomass fast pyrolysis. Computer Physics Communications, 2014, 185(6): 1739–1746
CrossRef Google scholar
[24]
Fogarasi S, Egedy A, Imre-Lucaci F, Varga T, Chován T. Hybrid CFD-compartment approach for modelling and optimisation of a leaching reactor. Computer-Aided Chemical Engineering, 2014, 33: 1255–1260
CrossRef Google scholar
[25]
Egedy A, Varga T, Chován T. Compartment model structure identification with qualitative methods for a stirred vessel. Mathematical and Computer Modelling of Dynamical Systems, 2013, 19(2): 115–132
CrossRef Google scholar
[26]
Zou J, Yang H, Zeng Z, Wu C, Williams P T, Chen H. Hydrogen production from pyrolysis catalytic reforming of cellulose in the presence of K alkali metal. International Journal of Hydrogen Energy, 2016, 41(25): 10598–10607
CrossRef Google scholar
[27]
Yan L, Lim C J, Yue G, He B, Grace J R. Simulation of biomass-steam gasification in fluidized bed reactors: Model setup, comparisons and preliminary predictions. Bioresource Technology, 2016, 221: 625–635
CrossRef Google scholar
[28]
Digabel S L. Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 2011, 37(4): 44
CrossRef Google scholar
[29]
Audet C, Dennis J E Jr. Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 2006, 17(1): 188–217
CrossRef Google scholar
[30]
Smith B R J, Loganathan M, Shanta M S. A review of the water gas shift reaction kinetics. International Journal of Chemical Reactor Engineering, 2010, 8: 1–32
[31]
Eri Q, Zhao X, Raganathan P, Gu S. Numerical simulations on the effect of potassium on the biomass fast pyrolysis in fluidized bed reactor. Fuel, 2017, 197: 290–297
CrossRef Google scholar
[32]
Trendewicz A, Evans R, Dutta A, Sykes R, Carpenter D, Braun R. Evaluating the effect of potassium on cellulose pyrolysis reaction kinetics. Biomass and Bioenergy, 2015, 74: 15–25
CrossRef Google scholar

Acknowledgments

The authors acknowledge the Horizon 2020, Marie Curie Research and Innovation Staff Exchange (RISE) (MSCA-RISE-2014 (Flexi-pyrocat, No. 643322)). Attila Egedy’s research was supported by EFOP-3.6.1-16-2016-00015 Smart Specialization Strategy (S3) –Comprehensive Institutional Development Program at the University of Pannonia to Promote Sensible Individual Education and Career Choices project.

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2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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