Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Ascher, S., Watson, I. and You, S. (2022) Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable and Sustainable Energy Reviews, 155, 111902. (doi: 10.1016/j.rser.2021.111902)

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Abstract

Over the past two decades, the use of machine learning (ML) methods to model biomass and waste gasification/pyrolysis has increased rapidly. Only 70 papers were published in the 2000s compared to a total of 549 publications in the 2010s. However, the approaches and findings have yet to be systematically reviewed. In this work, the machine learning methods most commonly employed for modelling gasification and pyrolysis processes are discussed with reference to their applications, merits, and limitations. Whilst coefficients of determination (R2) can be difficult to compare directly, due to some studies having greatly different approaches and aims, most studies consistently achieved a high prediction accuracy with R2 > 0.90. Artificial neural networks have been most widely used due to their potential to learn highly non-linear input-output relationships. However, a variety of methods (e.g. regression methods, tree-based methods, and support vector machines) are appropriate depending on the application, data availability, model speed, etc. It is concluded that ML has great potential for the development of models with greater accuracy. Some advantages of machine learning models over existing models are their ability to incorporate relevant non-numerical parameters and the power to generate a multitude of solutions for a wide range of input parameters. More emphasis should be placed on model interpretability in order to better understand the processes being studied.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:You, Dr Siming and Watson, Dr Ian and Ascher, Mr Simon
Authors: Ascher, S., Watson, I., and You, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Renewable and Sustainable Energy Reviews
Publisher:Elsevier
ISSN:1364-0321
ISSN (Online):1879-0690
Published Online:20 November 2021
Copyright Holders:Copyright © 2021 Elsevier Ltd.
First Published:First published in Renewable and Sustainable Energy Reviews 155: 111902
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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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