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.

中文翻译：

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使用动态并发内核偏最小二乘法监测废水处理过程

摘要 为满足污水处理工艺（WWTP）出水水质标准，提出了一种动态并发核偏最小二乘法（DCKPLS）用于工艺监测。结合增强矩阵和核技术，该方法可以同时处理污水处理厂数据的动态和非线性特征。此外，可通过 DCKPLS 模型克服 PLS 分解的固有局限性，该模型将特征空间数据和输出变量同时划分为五个子空间。通过工业污水处理厂数据的模拟传感器故障评估监测性能。具体而言，与CKPLS相比，使用DCKPLS的偏置故障和漂移故障的故障检测率分别提高了22.65%和8.06%。