Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges

Abstract

Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity and instability of biological treatment processes adversely affect the production of renewable energy and nutrient-rich products. To ensure stable processes and consistent product quality, researchers have invested heavily in control strategies for biological treatment, with machine learning (ML) recently proving effective in optimizing treatment, predicting parameters, detecting disturbances, and enabling real-time monitoring. This review critically assesses the application of ML in biological treatment, providing an in-depth evaluation of key algorithms. This study reveals that artificial neural networks, tree-based models, support vector machines, and genetic algorithms are the leading algorithms in biological treatment. A thorough investigation of the applications of ML in anaerobic digestion, composting, and insect farming underscores its remarkable capacity to predict products, optimize processes, perform real-time monitoring, and mitigate pollution emissions. Furthermore, this review outlines the challenges and prospects encountered in applying ML to biological treatment, highlighting crucial directions for future research in this area.