School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China; Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
ISA Trans. 2021 Nov;117:210-220. doi: 10.1016/j.isatra.2021.01.039. Epub 2021 Jan 25.
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).
质量相关的过程监测因其能够辅助维持高效的工厂运行而受到广泛关注。然而，当过程受到非平稳和过度复杂（存在噪声、乘法故障等）的特征影响时，传统方法通常无法有效地应用。为此，提出了一种新的方法，称为稳健自适应增强正则相关分析（Rab-CCA），用于监测废水处理过程。首先，提出了一种稳健分解方法，通过将受污染的矩阵分解为低矩阵和稀疏矩阵来减轻标准 CCA 的缺陷。其次，为了进一步提高标准过程监测方法的性能，相应地重建了新的准则函数和控制图。此外，提出了一种自适应统计控制限，它可以根据系统的状态调整阈值，同时可以有效地减少漏报和误报。Rab-CCA 的优越性通过基准模拟模型 1（BSM1）和实际的全规模废水处理厂（WWTP）得到了验证。
