Enhancing Methane Production in Anaerobic Digestion using Hybrid Machine Learning and Bio-Inspired Algorithms: A Comprehensive Review

Anaerobic digestion (AD) is a key technique for transforming organic waste into methane-rich biogas. However, consistent enhancement of methane production remains a challenge due to the nonlinear dynamics and fluctuating nature of AD systems. Traditional mechanistic models like ADM1 often fall short when applied to diverse feedstocks and variable conditions. This review explores the integration of machine learning (ML) models and bio-inspired optimization techniques— such as artificial neural networks (ANN), random forests (RF), and particle swarm optimization (PSO)—to address these challenges. We provide a comparative synthesis of existing methods and discuss how hybrid approaches, including GA-ANFIS and PSO-SVM, outperform standalone techniques in predictive accuracy and operational flexibility. This paper emphasizes the role of intelligent optimization in scaling methane production and outlines future directions for real-time, data-driven AD system control.