Anaerobic digestion

Methane

Biogas production

Process (computing)

Engineering

Ecology

Biology

Biogas

Stability (learning theory)

Operating system

Computer science

Chemical engineering

Risk analysis (engineering)

Business

Biochemical engineering

Waste management

Renewable energy

Inference

Bioprocess

Artificial intelligence

Digestate

Machine learning

Electrical engineering

Bioresource Technology

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.

Application of machine learning in anaerobic digestion: Perspectives and challenges