Fault Diagnosis of Wastewater Treatment Processes Based on CPSO-DKPCA

• Published in: International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 25
• Main Authors: Xu, Baochang, Zhuang, Peng, Wang, Yaxin, He, Wei, Wang, Zhongjun, Liu, Zhongyao
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2024; Springer
• Subjects: Artificial Intelligence; Chaotic particle swarm optimization; Computational Intelligence; Control; Dynamic kernel principal component analysis; Engineering; Fault diagnosis; Granger causality analysis; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Wastewater treatment; Fault diagnosis; Granger causality analysis; Chaotic particle swarm optimization; Wastewater treatment; Dynamic kernel principal component analysis
• ISSN: 1875-6883; 1875-6891; 1875-6883
• DOI: 10.1007/s44196-024-00412-z

The wastewater treatment process (WWTP) is one of the most common links in chemical plants. However, the testing for diagnosing faults in wastewater treatment plants is expensive and time-consuming. Due to strong nonlinearity and variable autocorrelation, traditional WWTP diagnostic methods based on principal component analysis (PCA) can lead to low fault detection rates (FDR) or difficulty in determining the root cause of faults. In this paper, an improved dynamic kernel principal component analysis (DKPCA) and Granger causality (GC) analysis model that uses chaotic particle swarm optimization (CPSO) to detect WWTP and locate the root causes of faults is proposed. First, a kernel function is introduced to map a nonlinear matrix to a linear space. Then, the training data are extended through a time lag constant to solve the problem of nonlinear and variable autocorrelation in WWTP. Moreover, a novel fault candidate variables selection method, together with GC, is introduced to locate the root variables of the fault. The CPSO algorithm is employed to optimize DKPCA's kernel function parameters, enhancing the accuracy of fault monitoring and diagnosis models. Compared with traditional methods, the proposed method has a better fault detection rate, achieving 95.83% and 93.33% fault detection rates in simulated and real WWTP, respectively.