Interpretable machine learning to model biomass and waste gasification

Ascher, S., Wang, X., Watson, I. , Sloan, W. and You, S. (2022) Interpretable machine learning to model biomass and waste gasification. Bioresource Technology, 364, 128062. (doi: 10.1016/j.biortech.2022.128062) (PMID:36202285)

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Abstract

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R2) of 0.90 when averaged across ten key gasification outputs. Global and local model interpretability methods have been used to illuminate the developed black box models. The studied models were most strongly influenced by the feedstock’s particle size and the type of gasifying agent employed. By combining global and local interpretability methods, the understanding of black box models has been improved. This allows policy makers and investors to make more educated decisions about gasification process design.

Item Type:Articles
Additional Information:The authors would like to acknowledge the financial support from the Engineering and Physical Sciences Research Council (EPSRC) Studentship and Programme Grant (EP/V030515/1). Siming You would like to thank the financial support from the Royal Society International Exchange Scheme (EC\NSFC\211175).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:You, Dr Siming and Watson, Dr Ian and Ascher, Mr Simon and Sloan, Professor William
Creator Roles:
Ascher, S.Conceptualization, Methodology, Software, Writing – original draft
Watson, I.Supervision
Sloan, W.Funding acquisition
You, S.Conceptualization, Supervision, Funding acquisition, Writing – review and editing
Authors: Ascher, S., Wang, X., Watson, I., Sloan, W., and You, S.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Infrastructure and Environment
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Bioresource Technology
Publisher:Elsevier
ISSN:0960-8524
ISSN (Online):1873-2976
Published Online:03 October 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Bioresource Technology 364:128062
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