Advancing Biogas Production Forecasting Using Artificial Intelligence: A Comprehensive Review of Models and Applications

Artificial intelligence (AI) plays a transformative role in improving the efficiency of biogas production by providing advanced tools for predicting and optimizing anaerobic digestion processes as a sustainable source of organic waste management and renewable energy supply. This study provides a systematic review of the applications of AI in biogas production prediction and, by reviewing recent studies, evaluates statistical, machine learning, and hybrid models and compares the performance of algorithms such as Random Forest and Artificial Neural Networks (ANN). These algorithms have shown outstanding performance in recent studies due to their ability to model nonlinear and dynamic behaviors. However, challenges such as inconsistent data quality, biochemical complexities, and generalizability limitations have limited the full exploitation of these technologies. Through a comprehensive literature review, this study identifies the strengths and weaknesses of existing models and proposes innovative solutions, including the integration of real-time data based on the Internet of Things (IoT), the development of hybrid models, and the utilization of transfer learning. The findings highlight the potential of artificial intelligence in improving the efficiency of biogas systems, reducing operating costs, and supporting sustainable energy planning, and provide directions for the development of intelligent and scalable forecasting tools.

کلیدواژه ها:

Biogas Production ، Artificial intelligence ، Machine Learning ، Forecasting Models ، Anaerobic digestion ، Random Forest