Application of machine learning in anaerobic digestion: Perspectives and challenges

[Display omitted] •Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and optimization.•Anaerobic digestion instability can be reduced using machine learning algorithms.•Further research on practical application and alg...

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Bibliographic Details
Published in:Bioresource technology 2022-02, Vol.345, p.126433-126433, Article 126433
Main Authors: Andrade Cruz, Ianny, Chuenchart, Wachiranon, Long, Fei, Surendra, K.C., Renata Santos Andrade, Larissa, Bilal, Muhammad, Liu, Hong, Tavares Figueiredo, Renan, Khanal, Samir Kumar, Fernando Romanholo Ferreira, Luiz
Format: Article
Language:English
Subjects:
Anaerobic digestion
Anaerobiosis
Biofuels
Bioreactors
gas production (biological)
Machine Learning
Methane
Modeling
prediction
Process instability
Process optimization
renewable energy sources
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Summary:[Display omitted] •Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and optimization.•Anaerobic digestion instability can be reduced using machine learning algorithms.•Further research on practical application and algorithm combination is needed. 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.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2021.126433