LAUSR

Decision making is essential to utilize the operation information of wastewater treatment process (WWTP) to provide the inhibition strategy for sludge bulking. However, since majority of decision-making models focus solely on knowledge or data resources, avoiding the interrelations and dependencies between the operation information, these models are difficult to obtain comprehensive and precise solutions. Thus, to solve this problem, a knowledge-aided and data-driven fuzzy decision-making (KD-FDM) model is designed for sludge bulking. First, a recursive reconstruction contribution (RRC) method is proposed to analyze the operation data to diagnose the fault of sludge bulking. Then, the fault information can be recorded as valid knowledge to assist in decision making. Second, a knowledge internalization mechanism is developed to make use of the knowledge from the results of RRC and the expert experience of sludge bulking to construct the initial condition of KD-FDM model. Then, the KD-FDM model can obtain the precision parameters and compact structure in the initialization phase. Third, the KD-FDM model using a knowledge-aided fuzzy broad learning system is employed to determine suppression strategies for sludge bulking. Then, the KD-FDM model can obtain fast and accurate strategies to mitigate the detrimental impact on the process performance. Finally, the KD-FDM model is tested in a real WWTP to confirm its effectiveness. The experimental results demonstrate that the proposed model can achieve outstanding performance.