Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass

Li, Y., Gupta, R. and You, S. (2022) Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. Bioresource Technology, 359, 127511. (doi: 10.1016/j.biortech.2022.127511) (PMID:35752259)

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Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.

Item Type:Articles
Additional Information:The authors would like to acknowledge the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) Programme Grant (EP/V030515/1). Siming You would like to thank the financial support from the Royal Society Research Grant (RGS\R1 \211358) and International Exchange Scheme (EC\NSFC\211175).
Glasgow Author(s) Enlighten ID:You, Dr Siming and Gupta, Dr Rohit and Li, Mr Yize
Authors: Li, Y., Gupta, R., and You, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Bioresource Technology
ISSN (Online):1873-2976
Published Online:22 June 2022
Copyright Holders:Copyright © 2022 The Author(s)
First Published:First published in Bioresource Technology 359: 127511
Publisher Policy:Reproduced under a Creative Commons licence

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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