A new approach to multivariate statistical process control and its application to wastewater treatment process monitoring

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Published Sep 4, 2023
Osamu Yamanaka Yukio Hiraoka

Abstract

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate Statistical Process Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. A conventional MSPC based on Principal Component Analysis (PCA) is firstly adjusted to have an easy-to-understand user interface and then a new yet simplified decomposable diagnostic model is introduced. The developed user interface is designed to seamlessly connect MSPC to existing process monitoring system adopting the so-called trend graphs. The proposed diagnostic model is derived in a constructive way by aggregating small size models with one input or two inputs to improve tractability of the diagnostic model. The effectiveness of the modified MSPC is illustrated through some offline and on-line experiments by using a set of real multivariate process data at a municipal wastewater treatment plant.  

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Keywords

Process Monitoring and Diagnosis, Multivariate Statistical Process Control, Wastewater Treatment Process, Principal Component Analysis, Supervisory Control and Data Acquisition System

References
B-DASH project (2016). Guideline for introducing ICTbased advanced process control and remote diagnosis technology for efficient wastewater treatment plant operation,Technocal Note of NILIM, No.939, http://www.nilim.go.jp/lab/bcg/siryou/tnn/tnn0939.htm

Bodenham, D. A., and Adams, N. M. (2016). A comparison of efficient approximations for a weighted sum of chisquared random variables. Statistics and Computing, 26(4), 917-928.

Camacho, J., Perez-Villegas, A., Garcia-Teodoro, P., and Macia-Fern ́ andez, G. (2016). PCA-based multivariate sta ́ tistical network monitoring for anomaly detection. Computers & Security, 59, 118-137.

Choi, S. W., Morris, J., and Lee, I. B. (2008). Nonlinear multiscale modelling for fault detection and identification. Chemical engineering science, 63(8), 2252-2266.

Jackson, J.E. (1991). A user’s guide to principal components, New York:Wiley.

Uchida, Y., Fujiwara, K., Saito, T., and Osaka, T. (2022). Process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman Process. IFAC-PapersOnLine, 55(2), 384-389.

Wise, B.M., Gallagher, N.B. (1996). The process chemometrics. approach to process monitoring and fault detection, J. Proc. Cont, 6(6), 329–348.

Zhao, L. T., Yang, T., Yan, R., and Zhao, H. B. (2022). Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model. Process Safety and Environmental Protection, 166, 617-62.
Section
Special Session Papers