An analysis of the application of machine learning techniques in anaerobic digestion

Abstract

This study aims to investigate the use of machine learning in the field of anaerobic digestion. This process involves breaking down organic matter without oxygen to produce biogas. In recent years, machine learning has gained significant attention as a way to improve the efficiency and stability of anaerobic digestion, as well as to forecast uncertain parameters, detect changes or disruptions in the process, and perform real-time monitoring. Artificial neural networks and support vector machines are some of the specific machine-learning techniques applied in this context. This review looks at the various machine learning models used in anaerobic digestion, discusses the opportunities, limitations, and challenges of these techniques, and assesses their suitability for anaerobic digestion processes. The review also considers the potential future use of machine learning in anaerobic digestion and identifies areas for further research.