Robust state and input estimation with enhanced convergence rate for monitoring anaerobic digestion. (September 2022) - Rueda-Escobedo, Juan G.; Sbarciog, Mihaela; Moreno, Jaime A.; Van Impe, Jan; Wouwer, Alain Vande
Abstract: In this study, an observer for estimating the state and unknown inputs is proposed for monitoring anaerobic digestion processes. This estimator is based on a dynamic model considering acidogenesis and methanogenesis, and consists of three sub-observers: (a) a gramian-based fixed-time convergent observer for the inlet chemical oxygen demand (COD) and the acidogenic bacteria population, (b) an asymptotic observer for the methanogenic bacteria population, and (c) a super-twisting observer for systems with time-varying parameters to estimate the inlet volatile fatty acid (VFA) concentration. These sub-observers can be designed independently, which greatly simplifies the tuning process. Proofs of convergence are developed and simulation tests show the performance of the estimation scheme as compared to classical extended Kalman filtering. Highlights: Three observers are proposed for monitoring anaerobic digestion processes. Unknown inputs and states are estimated. Enhanced convergence speed such as finite- and fixed-time convergence is achieved. Observability analysis and convergence analysis is provided.