Parametric Sensitivity Analysis of the Modified AMOCO Model for an Anaerobic Digester

Abstract

Sensitivity analysis plays a crucial role in understanding the dynamics of anaerobic digesters, converting biodegradable matter into biogas, and it can be used to reduce the need for extensive parameter estimation by determining what the most influential model parameters are. The anaerobic digestion process has complex nonlinear dynamics, that can be described by the modified AMOCO model having seven states and twenty parameters but suffering from a lack of accurate and robust online measurement. Therefore, the development of accurate state and parameter estimation is challenging, one way to improve the robustness and accuracy of the estimator, is to reduce the number of parameters through sensitivity analysis. This study identifies the most influential parameters based on dimensionless sensitivity coefficients. Through a systematic assessment of parameter impacts on model variables, we highlight the strong influence of the model's cascade structure on parameter sensitivity. Furthermore, employing period averaging with a threshold of 0.2, we identify eight significant parameters out of the eighteen model parameters. This research contributes to the understanding of anaerobic digesters, specifically employing the modified AMOCO model, and suggests a framework for parameter selection to enable optimized operation.