Abstract

This paper focuses on the state estimation of an Anaerobic Digestion (AD) model using two filtering algorithms: the Extended Kalman Filter (EKF) and the Particle Filter (PF). AD is a biological process converting organic matter into biogas, offering both a valuable renewable energy source and a sustainable solution for waste treatment. We investigate the applicability of these methods to the state space model called AM2, a simplified AD model involving only the key acetogenesis and methanogenesis reactions and excluding other specific biochemical steps. Subsequently, we evaluate their performance through simulation experiments.