Knowledge-Data-Driven Robust Fault-Tolerant Control for Sludge Bulking in Wastewater Treatment Process

The increasing complexity and scale of the wastewater treatment process (WWTP) demand more and more safety and stability. However, due to the unavoidable existence of external disturbance, sludge bulking is commonly encountered, which can result in risks for the efficient and stable operation of WWTP. To address this problem, a knowledge-data-driven robust fault-tolerant control (KDD-RFTC) is proposed in this article. First, a robustness evaluation strategy (RES) is constructed to extract the response and fluctuation characteristics of KDD-RFTC. Then, the antijamming ability of KDD-RFTC can be obtained in the presence of sludge bulking. Second, an adaptive knowledge transfer strategy (AKTS), based on RFTC, is designed to suppress the sludge bulking with the knowledge from the results of RES and the process data. Then, the proposed KDD-RFTC can readjust the manipulated variable to ensure a safe and stable operation. Third, the stability proof of KDD-RFTC is verified by the Lyapunov theory. Then, the successful application of KDD-RFTC can be guaranteed. Finally, KDD-RFTC is employed in the benchmark simulation model no. 1 (BSM1) to verify its merits. The experimental results illustrate that the proposed KDD-RFTC method can obtain excellent control performance and inhibit sludge bulking.