A comprehensive artificial neural network model for gasification process prediction

Ascher, S., Sloan, W. , Watson, I. and You, S. (2022) A comprehensive artificial neural network model for gasification process prediction. Applied Energy, 320, 119289. (doi: 10.1016/j.apenergy.2022.119289)

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The viability and the relative merits of competing biomass and waste gasification schemes depends on a complex mix of interacting factors. Conventional analytical methods that are used to aid decision making rely on a plethora of poorly defined parameters. Here we develop a method that eschews the uncertainty in process representation by using a machine learning, data driven, approach to predicting a set of 10 key measures of gasification technology’s performance. We develop an artificial neural network that is novel in its use of both categorical and continuous data inputs, which makes it flexible and broadly applicable in assessing gasification process designs. It is the first model applicable to a wide range of feedstock types, gasifying agents, and reactor options. A strong predictive performance, quantified by a coefficient of determination (R2) of 0.9310, was confirmed. The approach has the potential to generate accurate input data for e.g., cost-benefit analysis (CBA) and life cycle sustainability assessment (LCSA) and thus allow for more transparency in the decisions made by policy makers and investors.

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 Supergen Bioenergy Hub Rapid Response Funding (RR 2022_10) and Royal Society Research Grant (RGS\R1\211358).
Glasgow Author(s) Enlighten ID:You, Dr Siming and Watson, Dr Ian and Ascher, Mr Simon and Sloan, Professor William
Authors: Ascher, S., Sloan, W., Watson, I., 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:Applied Energy
ISSN (Online):1872-9118
Published Online:25 May 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Applied Energy 320:119289
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