Intelligent decision method of sludge bulking using recursive kernel principal component analysis and Bayesian network

Abstract

Efficient and accurate decision is essential to avoid sludge bulking and ensure the stable operation of wastewater treatment process (WWTP). However, due to the strong nonlinear characteristic, time-varying characteristic and changeable triggering causes of sludge bulking, the decision performance is not optimistic. In this paper, an intelligent decision method combining recursive kernel principal component analysis (RKPCA) and Bayesian network (BN) is proposed to accurately detect the occurrence, identify the root causes and severity level, and provide the restraining measures for sludge bulking. The proposed intelligent decision method consists of three main parts. First, a RKPCA model based on the sliding window mechanism is proposed to extract the nonlinear and time-varying characteristics of WWTP. Then, the proposed RKPCA can assist the decision method to detect the sludge bulking with high accuracy. Second, a mean reconstruction contribution of RKPCA (MRC-RKPCA) method is designed to preliminarily isolate all candidate faulty relevant variables of sludge bulking. Then, the MRC-RKPCA method is able to provide the relationship between the sludge bulking and the corresponding faulty variables for the decision method. Third, a decision model using BN is developed to determine the root causes and the severity of sludge bulking through reverse and forward inference, and provide corresponding restraining measures. Then, the proposed decision method can obtain accurate strategies to suppress the sludge bulking. Finally, the proposed intelligent decision method was applied for a real WWTP. The results demonstrate the proposed intelligent decision method can obtain better decision performance than some existing methods.