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)

Abstract

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).
• Status: Published
• Refereed: Yes
• 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
• Publisher: Elsevier
• ISSN: 0960-8524
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
309846		Decentralised water technologies	William Sloan	Engineering and Physical Sciences Research Council (EPSRC)	EP/V030515/1	ENG - Infrastructure & Environment