Full Life-Cycle Intelligent Detection and Diagnosis Analysis for Sludge Bulking

Activated sludge process is the most commonly used sewage treatment process in China. The occurrence of sludge bulking is an unavoidable and urgent problem for the stable and reliable operation of activated sludge process. To solve this problem, this paper proposed a new full life-cycle fault diagnosis method to monitor sludge bulking and provide reasonable decision support after accurate fault warning. In order to fully mine the hidden information of sludge bulking data, this paper used the canonical correlation analysis (CCA) and absolute average amplitude value (AMAV) to extract the relevant features and apply them to fault detection. The contribution plots were improved by rearranging historical observation samples and applied to fault isolation. According to the results of fault warning, a fault propagation location method based on feature extraction of AMAV and multivariate Granger causality (MVGC) analysis was proposed. The field data collected in a sewage plant were used for experiments. The results show that the proposed method can detect, separate and analyze the occurrence of sludge bulking timely and effectively.