Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

Abstract

Growing concern about global climate change has led to considerable interest in investigating renewable energy sources such as the biological conversion of biomass to methane in an anaerobic environment. Through a series of complicated biochemical interactions, it uses various bacterial species to degrade biodegradable material in the feedstock. Due to the complex and interacting biochemical processes, anaerobic digestion has nonlinear dynamics. Anaerobic digestion is highly at risk of instabilities and uncertainties because of its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions. Therefore, effectively operating a biogas production unit necessitates a thorough understanding of the system’s uncertainties. The present study aims to identify and assess the sources and methods of coping with the uncertainties in anaerobic digestion processes through a narrative review. Moreover, the knowledge gap is also investigated to reveal the challenges and opportunities to have a robust model. The results indicate that the unpredictability of model parameters and input variables were the most significant source of uncertainty, and the Monte Carlo technique, confident interval, and interval observers, as well as sensitivity analysis were the most frequently used tools to cope with these uncertainties.