Knowledge-Guided Adaptive Neuro-Fuzzy Self-Healing Control for Sludge Bulking in Wastewater Treatment Process

Neural network control has been developed into an efficient strategy to guarantee the safe and steady operation of wastewater treatment process (WWTP). However, due to the complex mechanism and serious damage of sludge bulking in WWTP, it is significant for neural network control to achieve the timely self-helaing of operation. Therefore, the goal of this paper is to devise a knowledge-guided adaptive neuro-fuzzy self-healing control (KG-ANFSHC) for sludge bulking. The originality of KG-ANFSHC is threefold. First, a knowledge evaluation strategy is introduced to consider the correlation and differentiation between the normal operation condition and sludge bulking to obtain available information. Then, the proposed strategy can provide a guide for control to take remedial actions. Second, a KG-ANFSHC based on a knowledge transfer mechanism, which makes full use of knowledge and data to dynamically adjust its parameters, is designed to eliminate the sludge bulking. Then, KG-ANFSHC can timely and precisely regulate manipulated variables to realize the self-healing of operation. Third, the Lyapunov stability theorem is employed to ensure the stability of KG-ANFSHC. Then, the proof of stability can assist its effective application. Finally, the proposed control is applied to Benchmark Simulation Model No. 2 (BSM2) to verify its advantages. Several results demonstrate that KG-ANFSHC can own satisfying self-healing performance to guarantee the operation recovered from sludge bulking.