Monitoring of Wastewater Treatment Processes Using Dynamic Concurrent Kernel Partial Least Squares

Abstract To meet the standards of effluent quality in wastewater treatment processes (WWTPs), a dynamic concurrent kernel partial least squares (DCKPLS) method is proposed for process monitoring. After integrating the augmented matrices and kernel technique, the proposed method can be used to handle the dynamic and nonlinear characteristics of WWTP data simultaneously. Besides, the inherent limitation of PLS decomposition can be overcome by DCKPLS model, which concurrently partitions the feature space data and output variables into five subspaces. Monitoring performance is evaluated by simulated sensor faults of industrial WWTP data. Specifically, the fault detection rates of bias fault and drifting fault using DCKPLS are increased by 22.65% and 8.06%, respectively, in comparison with CKPLS. It is also shown that the DCKPLS model provides better monitoring performance than the other counterparts.