Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment

• Published in: ISA transactions 2021-11, Vol.117, p.210-220
• Main Authors: Cheng, Hongchao, Wu, Jing, Huang, Daoping, Liu, Yiqi, Wang, Qilin
• Format: Article
• Language: English
• Subjects: Adaptive threshold; Canonical correlation analysis (CCA); Fault detection; Quality-relevant; Wastewater treatment
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2021.01.039

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). •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.