Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment. (November 2021) - Cheng, Hongchao; Wu, Jing; Huang, Daoping; Liu, Yiqi; Wang, Qilin
Abstract: Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP). Highlights: Rab-CCA method is proposed to monitor the quality-relevant fault. Inexact augmented Lagrange algorithm is improved to decompose the corrupted data matrix. Adaptive statistical control limit is proposed to adapt to process variations. A new criterion function is established to achieve multi-objective optimization. Quality-relevant faults of a full-scale WWTP and BSM1 are effectively diagnosed.