Two-stage Anaerobic Digestion Process Optimal Control Study based on Extremum-seeking Control and Self-optimizing Control

In this paper, a new dynamic nonlinear gradient observer-based extremum-seeking control algorithm (DNGO-based ESC) and a dynamic Jacobian matrices estimator-based self-optimizing control algorithm (DJE-based SOC) are designed for the control of two-stage anaerobic digestion (TSAD). None of two algorithms requires priori knowledge about the system model. The proposed algorithms are compared with the classical extremum-seeking control algorithm and the Kalman Filter based Newton extremum-seeking control algorithm. The simulation results show that in the presence of disturbance both of proposed control algorithms can maintain the system at the optimal operating point and drive the hydrogen and methane yields to the extreme point. Future work is to validate the designed control algorithm in an actual two-stage anaerobic digestion process.