A review of machine learning models in predicting biogas production

• Main Authors: Amran N.A.M., Mohamed H., Rafaai Z.F.M., Yacob N.S., Junoh H., Shamsuddin A.H.
• Other Authors: 57194584787
• Format: Conference paper
• Published: American Institute of Physics 2025

One of the main forces advancing Industry 4.0, also known as the fourth industrial revolution, is machine learning (ML). This paper examines the machine learning (ML) models application for biogas production prediction in anaerobic digestion (AD). This study's primary objective is to determine which ML techniques and models are utilised in the AD process. In addition, this study identifies the type of ML techniques, input and output parameters, and software used. Researchers have widely employed a couple of ML models in biogas production. After reviewing the 15 most recent papers, it was discovered that the Artificial Neural Network (ANN) and Adaptive Network-Based Fuzzy Inference System (ANFIS) are the most commonly used types of ML. The most commonly used operating parameters in predicting biogas production were reaction time, temperature, pH, total solids (TS), volatile solids, volatile fatty acids (VFAs), and fixed solids. In conclusion, the review discusses the challenges and prospects of using machine learning in the AD process and provides recommendations for future implementation. ? 2024 Author(s).