A Predictive PBM-DEAM Model for Lignocellulosic Biomass Pyrolysis

Abstract

Pyrolysis is a promising and attractive way to convert lignocellulosic biomass into low carbon-emission energy products. To effectively use biomass feedstock with size distribution to produce biofuels, a comprehensive kinetic model of the process, occurring at particle level, is important. In this study, the population balance model (PBM)-distributed activation energy model (DAEM) coupled model is first time developed to predict biomass pyrolysis. The Population balance model is used to present the variable size distribution of solid, decomposed from virgin biomass to porous char. Two different kinetic models are embedded into the conservation equations of mass and energy. They are compared to demonstrate the prediction performance of heating-up time during the pyrolysis process of biomass with a normal size distribution. It is found that non-isothermal kinetics without and with DEAM capture the intra-particle temperature distribution. There is a noticeable difference of heating-up time between single and distributed particle size.

Publication DOI: https://doi.org/10.1016/j.jaap.2021.105231
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Chemical Engineering & Applied Chemistry
College of Engineering & Physical Sciences > Energy and Bioproducts Research Institute (EBRI)
College of Engineering & Physical Sciences
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: Junmeng Cai appreciated the financial support of this work from CAS Key Laboratory of Renewable Energy (No. Y807k91001). Hongyu Zhu gratefully acknowledges Doctoral Training Programme fund from College of Engineering and Physical Sciences, Aston University.
Uncontrolled Keywords: Distributed activation energy model (DAEM),Population balance model (PBM),Kinetics,Biomass pyrolysis,Temperature distribution
Full Text Link:
Related URLs: https://www.sci ... 165237021002175 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-08-01
Published Online Date: 2021-06-07
Accepted Date: 2021-06-04
Authors: Zhu, Hongyu
Dong, Zhujun
Yu, Xi (ORCID Profile 0000-0003-3574-6032)
Cunningham, Grace
Umashanker, Janaki
Zhang, Xingguang
Bridgwater, Anthony V. (ORCID Profile 0000-0001-7362-6205)
Cai, Junmeng

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 7 June 2022.

License: Creative Commons Attribution Non-commercial No Derivatives


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