Robust state and input estimation with enhanced convergence rate for monitoring anaerobic digestion

In this study, an observer for estimating the state and unknown inputs is proposed monitoring anaerobic digestion processes. This estimator based on a dynamic model considering acidogenesis methanogenesis, consists of three sub-observers: (a) gramian-based fixed-time convergent inlet chemical oxygen demand (COD) acidogenic bacteria population, (b) asymptotic methanogenic (c) super-twisting systems with time-varying parameters to estimate volatile fatty acid (VFA) concentration. These sub-observers can be designed independently, which greatly simplifies tuning process. Proofs convergence are developed simulation tests show performance estimation scheme as compared classical extended Kalman filtering.