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| Online fault detection of complex wastewater treatment process using dynamic kernel PCA#br# |
（1. Jiangsu CoInnovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu 210037, China； 2. Shandong Huatai Paper Industry Co., Ltd., Dongying， Shandong 257335, China）
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Abstract To overcome the strong nonlinearity and dynamic characteristics, the online fault detection was investigated based on principal component analysis (PCA) in a wastewater treatment process. The kernel principal component analysis (KPCA) was constructed by introducing kernel functions in PCA, and the dynamic kernel principal component analysis (DKPCA) was proposed by embedding dynamic models to achieve online fault detection in the wastewater treatment process. According to the data from the treatment process of a paper mail wastewater, the bias faults, the drift faults and the precision degradation faults were established and simulated. The results show that under the condition of bias faults, compared with PCA and KPCA, the squared prediction error of DKPCA is respectively improved by 96.96% and 87.87%, and the detection sensitivity in drift fault is also improved. The validity of the DKPCA method is verified in online fault detection of wastewater treatment.
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Received: 29 October 2019
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