Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

Fang, Y., Ma, L., Yao, Z., Li, W. and You, S. (2022) Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm. Energy Conversion and Management, 264, 115734. (doi: 10.1016/j.enconman.2022.115734)

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Gasification technologies have been extensively studied for their potential to convert biomass feedstocks into syngas (a mixture of CH4, H2, and CO mainly) that can be further turned into heat or electricity upon combustion. It is crucial to understand optimal gasification process parameters for practical design and operation for maximizing the potential. This study combined the Monte Carlo simulation approach, gasification kinetic modeling, and the random forest algorithm to predict the optimal gasification process parameters (i.e. water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature) towards a maximum syngas yield. The Monte Carlo approach randomly generated a data pool of the process parameters following either a normal or uniform distribution, which was then fed into a validated kinetic model to create 2,000 datasets (process parameters and syngas yields). For the random forest model, the mean decrease accuracy and mean decrease Gini were used to assess the importance of the process parameters on syngas yields. The accuracy of the optimization method was evaluated using the coefficient of determination (R2), the root means square error (RMSE), and the mean absolute error (MAE). Generally, the predictions for the normal distribution case were closer to the experimental data obtained from existing literature than that for the uniform distribution case. The model was used to predict the optimal syngas yield and process parameters of wood gasification and it was shown that the predictions were generally in good agreement (<12% difference for the case of normal distribution) with existing experimental results. The method serves as a useful tool for determining optimal gasification process parameters for process and operation design.

Item Type:Articles
Additional Information:Siming You would like to acknowledge the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) Programme Grant (EP/V030515/1), Supergen Bioenergy Hub Rapid Response Funding (RR 2022_10), and Royal Society Research Grant (RGS\R1\211358). Wangliang Li would like to thank the financial support from the National Natural Science Foundation of China (No. 21878313).
Glasgow Author(s) Enlighten ID:You, Dr Siming and Fang, Mr Yi
Creator Roles:
Fang, Y.Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft
You, S.Supervision, Writing – review and editing, Conceptualization, Funding acquisition, Investigation, Project administration, Validation, Writing – review and editing
Authors: Fang, Y., Ma, L., Yao, Z., Li, W., and You, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Energy Conversion and Management
ISSN (Online):1879-2227
Published Online:19 May 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Energy Conversion and Management 264:115734
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
309846Decentralised water technologiesWilliam SloanEngineering and Physical Sciences Research Council (EPSRC)EP/V030515/1ENG - Infrastructure & Environment
313502Microwave-assisted Integration of Pyrolysis and Anaerobic Digestion for Efficient Biofuel Generation from Organic Waste (MaPAD)Siming YouThe Royal Society (ROYSOC)RGS-R1-211358ENG - Systems Power & Energy
313625Eliminating bottle necks in demand-led whole algal partitioning for nutritional and biofuel end useIan WatsonEngineering and Physical Sciences Research Council (EPSRC)SGBHFF Job code 56544Med - Human Nutrition