Biogas Production: Feedstocks, Production Process, and Optimization Strategies

Biogas is an alternative source of energy, which reduces greenhouse gas emission and decreases fossil fuel dependence. Anaerobic digestion (AD) is the major process for biogas production, utilizing different organic materials such as animal manure, food waste, municipal waste, agricultural waste, etc. AD is a sequential four-step process involving hydrolysis, acidogenesis, acetogenesis, and methanogenesis. All these processes are mediated by different microorganisms. The biogas production is majorly influenced by several process parameters such as feedstock type, solid content, C/N ratio, inoculum contents, initial pH, temperature, and hydrolytic retention time (HRT); therefore, optimization of these parameters is essential for achieving high yield and productivity of biogas in AD. Conventionally, the “one-factor-at-a-time (OFAT)” method has been used in various studies; however, this possess limitation of high time consumption and ignorance of the interactive effect of parameters. To overcome these limitations, different statistical methods such as response surface methodology (RSM) are being utilized for optimization studies, wherein individual and interactive effects of parameters are considered and yield high efficiency of biogas. In recent years, with the advancement of computational studies, integration of artificial intelligence (AI) and machine learning has shown significant improvement and précised outcomes. Different AI models, such as artificial neural networks (ANNs) and genetic algorithms (GAs), are being used effectively to optimize biogas yield. These optimizations enable the development of a sustainable and commercially viable AD process for biogas production.