Lee Dae Sung, Vanrolleghem Peter A
BIOMATH, Department of Applied Mathematics, Biometrics and Process control, Ghent University, Ghent, Belgium.
Environ Monit Assess. 2004 Mar;92(1-3):119-35. doi: 10.1023/b:emas.0000014498.72455.18.
As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.
随着污水排放标准日益严格，废水处理过程的在线监测变得非常重要。诸如主成分分析（PCA）等多元统计过程控制方法已在利用测量数据进行过程故障检测与诊断中得到广泛应用。在这项工作中，我们提出了一种用于自适应废水处理过程监测的一致性主成分分析算法。该方法通过递归更新协方差结构克服了操作条件变化的问题。与典型的多向主成分分析模型相比，该算法无需任何估计。利用此方法可实时检测过程干扰并直接识别出有问题的测量值。所提出的方法成功应用于一个中试规模的废水处理序批式反应器。
