Application of machine learning in anaerobic digestion: Perspectives and challenges

Ianny Andrade Cruz; Wachiranon Chuenchart; Fei Long; K C Surendra; Larissa Renata Santos Andrade; Muhammad Bilal; Hong Liu; Renan Tavares Figueiredo; Samir Kumar Khanal; Luiz Fernando Romanholo Ferreira

doi:10.1016/j.biortech.2021.126433

Application of machine learning in anaerobic digestion: Perspectives and challenges

Bioresour Technol. 2022 Feb:345:126433. doi: 10.1016/j.biortech.2021.126433. Epub 2021 Nov 27.

Affiliations

¹ Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil.
² Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA.
³ Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA.
⁴ Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA; Global Institute for Interdisciplinary Studies, 44600 Kathmandu, Nepal.
⁵ School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China.
⁶ Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil; Institute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil.
⁷ Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA. Electronic address: khanal@hawaii.edu.

PMID: 34848330
DOI: 10.1016/j.biortech.2021.126433

Abstract

Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.

Keywords: Anaerobic digestion; Machine learning; Modeling; Process instability; Process optimization.

Publication types

Review

MeSH terms

Anaerobiosis
Biofuels
Bioreactors*
Machine Learning
Methane*

Substances

Biofuels
Methane