New Methodologies for the Optimization of Operational Parameters of Bio Gas Power Plants: A Review

This review paper analyzes the methodologies adopted to optimize the operational parameters of biogas power plants to enhance biogas production and maintain efficiency. It discusses the influence of various process parameters, such as pH, Organic Loading Rate (OLR), temperature, mixing ratio, Hydraulic Retention Time (HRT), pre-treatment, C/N ratio, inoculation, and seeding, on biogas production. The impact of these parameters varies depending on the operating conditions, leading to instability and process interference issues in the anaerobic digestion process. Numerous studies have been conducted on optimizing biogas power plant processes using techniques such as the Taguchi method and machine learning approaches. This review provides an in-depth analysis of the application of Taguchi Design of Experiments (DoE) and grey relational analysis (GRA) in the multi-objective optimization of process parameters in biogas power plants. Additionally, it examines various machine learning methods employed by researchers for process optimization, helping to identify suitable algorithms based on substrate quantity, operating conditions, and the thermophysical parameters of the anaerobic digestion process.

کلیدواژه ها:

Artificial Neural Networks ، Biogas production ، Process Parameters ، Taguchi design of experiments ، Machine Learning