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A Monitoring Technique Using Multivariate Statistical Process Control Method for Performance Improvement with Application to Wastewater Treatment Plant Operation

机译:一种利用多元统计过程控制方法的监测技术,适用于应用于污水处理厂运行的性能改进

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This paper presents a new monitoring technique to improve the performance of plant operation based on multivariate statistical process control (MSPC). The proposed method combines with MSPC by principal component analysis (PCA-MSPC) and monitoring of a pre-defined performance index for efficient and stable plant operation. Fault detection and isolation (FDI) related to the performance index is selectively performed by monitoring the time series data of the index wherein the sample points violating the control limit of the Q statistic or that of the T~2 statistic in PCA-MSPC are indicated. Hidden patterns of probable cause to deteriorate the performance index are discovered from the FDI by observing the time series data of the isolated variables. Applications of the proposed method to real wastewater treatment process illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.
机译:本文提出了一种新的监控技术,以提高基于多元统计过程控制(MSPC)的植物运行性能。所提出的方法通过主成分分析(PCA-MSPC)与MSPC相结合,并监控预定义的性能指标,以实现高效且稳定的工厂操作。通过监视索引的时间序列数据,选择性地执行与性能索引相关的故障检测和隔离(FDI),其中示出了违反了Q统计控制限制的采样点或PCA-MSPC中的T〜2统计信息的采样点。通过观察分离变量的时间序列数据,从FDI发现可能导致劣化性能指标的可能原因的隐藏模式。所提出的方法对实际废水处理过程的应用说明了所提出的方法的有效性,通过显示节能运行和稳定的植物操作可能的改进。

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