A Knowledge Discovery Assistance Method Using Multivariate Statistics for Efficient Wastewater Treatment Plant Operation

Abstract

This paper presents a new knowledge discovery assistance method to improve wastewater treatment plant (WWTP) 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 performance index wherein the sample points violating the control limit of Q statistic or that of T² statistic in PCA-MSPC are indicated. Hidden patterns of probable cause variables 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 WWTPs illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.