Machine learning for sustainable organic waste treatment: a critical review

Gupta, R., Hajabdollahi Ouderji, Z., Uzma, , Yu, Z. , Sloan, W. T. and You, S. (2024) Machine learning for sustainable organic waste treatment: a critical review. npj Materials Sustainability, 2, 5. (doi: 10.1038/s44296-024-00009-9)

Abstract

Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven modeling techniques for organic treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors. The application of these techniques is explored in terms of their capacity for optimizing complex processes. Additionally, the study delves into physics-informed neural networks, highlighting the significance of integrating domain knowledge for improved model consistency. Comparative analyses are carried out to provide insights into the strengths and weaknesses of each technique, aiding practitioners in selecting appropriate models for diverse applications. Transfer learning and specialized neural network variants are also discussed, offering avenues for enhancing predictive capabilities. This work contributes valuable insights to the field of data-driven modeling, emphasizing the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios.

• Item Type: Articles
• Additional Information: The authors acknowledge the Engineering and Physical Sciences Research Council (EPSRC) Programme Grant (EP/V030515/1). RG acknowledges the Royal Society Newton International Fellowship (NIF\R1\211013).
• Keywords: Waste management, data science, anaerobic digestion, pyrolysis, process optimisation.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: You, Dr Siming and Hajabdollahi Ouderji, Dr Zahra and Uzma, Dr Uzma and Sloan, Professor William and Yu, Professor Zhibin
• Authors: Gupta, R., Hajabdollahi Ouderji, Z., Uzma, , Yu, Z., Sloan, W. T., 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: npj Materials Sustainability
• Publisher: Springer Nature
• ISSN: 2948-1775
• ISSN (Online): 2948-1775
• Copyright Holders: Copyright © The Author(s) 2024
• First Published: First published in npj Materials Sustainability 2:3
• Publisher Policy: Reproduced under a Creative Commons license

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