Maximum extrem biogas yield prediction based tracking control for two-stage anaerobic digestion using CKF robust observer feedback

Two-stage anaerobic digestion process, recognized as a promising microbiological technology, can effectively converts organic pollutants into renewable energy gases. However, practical implementation faces two fundamental challenges: the critical process states (for example, concentrations of anaerobic microorganisms) are not directly measurable through conventional sensors, and the gas production efficiency remains suboptimal under current operational paradigms. To address these challenges, this study proposed a robust observer-based biogas yield extremum prediction tracking controller (RO-EPTC). The proposed RO-EPTC controller integrates a cubature Kalman filter robust observer and an artificial neural network-based prediction tracking controller. The RO-EPTC enables dynamic extremum prediction of biogas yield while ensuring real-time convergence of actual gas production to the identified optimal trajectory. Additionally, the proposed scheme provides accurate estimation of unmeasurable system states. Finally, through simulation comparison experiments, the effects of proposed RO-EPTC method were verified.