Feature Extraction Method of Sludge Bulking Using Multi-KPCA

Sludge bulking is a common abnormal condition in municipal wastewater treatment process (WWTP). It is difficult for WWTP to effectively achieve high-precision feature extraction since it has complex reactions, many influencing factors, and strong coupling of factors. In this paper, a multi-kernel combined principal component analysis (MKPCA) method for extracting characteristic variables of sludge bulking based on multi-innovation random gradient is proposed. Firstly, based on the nonlinear characteristics of kernel functions and the advantages of adaptability of different kernel functions, a multi-kernel combination mechanism is designed. Then, a principal component analysis method based on multi-kernel combination is proposed. Secondly, a multi-innovation random gradient identification method is designed, which introduces a sliding window mechanism to update the structure and parameters of kernel functions with multiple time data. In addition to ensuring the identification accuracy, the variable feature extraction effect of sludge bulking process can be improved. Finally, the method is tested with actual data from wastewater treatment plant. The results show that the proposed method has a better feature extraction effect.