A new approach to multivariate statistical process control and its application to wastewater treatment process monitoring
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Published
Oct 8, 2024
Osamu Yamanaka
Ryo Namba
Takumi Obara
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. Firstly,
a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand user
interface and then a new yet simplified reconfigurable diagnostic model is introduced. The user interface that has been
developed is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trend
graphs. The proposed diagnostic model is constructed by aggregating small models with either one or two inputs, which
enhances the tractability of the diagnostic model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment.
plant.
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Keywords
Fault Diagnosis, Multivariate Statistical Process Control, Wastewater Treatment Plant
References
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Bodenham, D. A., & Adams, N. M. (2016). A comparison of efficient approximations for a weighted sum of chi-squared random variables. Statistics and Computing, 26(4), 917–928.
Camacho, J., Pérez-Villegas, A., García-Teodoro, P., & Macía-Fernández, G. (2016). Pca-based multivariate statistical network monitoring for anomaly detection. Computers & Security, 59, 118–137.
Choi, S. W., Morris, J., & Lee, I.-B. (2008). Nonlinear multiscale modelling for fault detection and identification. Chemical engineering science, 63(8), 2252–2266.
de Oliveira, R. R., Pedroza, R. H., Sousa, A. O., Lima, K. M., & de Juan, A. (2017). Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy. Analytica chimica acta, 985, 41–53.
García-Alvarez, D. (2009). Fault detection using principal component analysis (pca) in a wastewater treatment plant (wwtp). Proceedings of the International Student’s Scientific Conference, 55–60.
Ge, Z., Yang, C., & Song, Z. (2009). Improved kernel pca-based monitoring approach for nonlinear processes. Chemical Engineering Science, 64(9), 2245–2255.
Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349.
Jaffel, I., Taouali, O., Harkat, M. F., & Messaoud, H. (2017). Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. The International Journal of Advanced Manufacturing Technology, 88, 3265–3279.
Joe Qin, S. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics: A Journal of the Chemometrics Society, 17(8-9), 480–502.
Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H., Strauss, R., & Bakshi, B. R. (2002). Comparison of multivariate statistical process monitoring methods with applications to the eastman challenge problem. Computers & chemical engineering, 26(2), 161–174.
Kourti, T., & MacGregor, J. F. (1996). Multivariate spc methods for process and product monitoring. Journal of quality technology, 28(4), 409–428.
Kresta, J. V., Macgregor, J. F., & Marlin, T. E. (1991). Multivariate statistical monitoring of process operating performance. The Canadian journal of chemical engineering, 69(1), 35–47.
Lemaigre, S., Adam, G., Goux, X., Noo, A., De Vos, B., Gerin, P. A., & Delfosse, P. (2016). Transfer of a static pca-mspc model from a steady-state anaerobic reactor to an independent anaerobic reactor exposed to organic overload. Chemometrics and Intelligent Laboratory Systems, 159, 20–30.
Rosén, C. (2001). A chemometric approach to process monitoring and control-with applications to wastewater treatment operation. Lund University.
Sandberg, E., Lennox, B., & Undvall, P. (2007). Scrap management by statistical evaluation of eaf process data. Control engineering practice, 15(9), 1063–1075.
Uchida, Y., Fujiwara, K., Saito, T., & Osaka, T. (2022). Process fault diagnosis method based on mspc and lingam and its application to tennessee eastman process. IFACPapersOnLine, 55(2), 384–389.
Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2000). Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and intelligent laboratory systems, 51(1), 95–114.
Wise, B. M., & Gallagher, N. B. (1996). The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6), 329–348.
Zhao, L.-T., Yang, T., Yan, R., & 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, 617–627.
AlGhazzawi, A., & Lennox, B. (2008). Monitoring a complex refining process using multivariate statistics. Control Engineering Practice, 16(3), 294–307.
BDASH-Project. (2016). Guideline for introducing ict-based advanced process control and remote diagnosis technology for efficient wastewater treatment plant operation (Tech. Rep. No. 939). National Institute for Land and Infrastructure Management. Retrieved from http://www.nilim.go.jp/lab/bcg/siryou/tnn/tnn0939.htm (Accessed on: 2024-02-14)
Bodenham, D. A., & Adams, N. M. (2016). A comparison of efficient approximations for a weighted sum of chi-squared random variables. Statistics and Computing, 26(4), 917–928.
Camacho, J., Pérez-Villegas, A., García-Teodoro, P., & Macía-Fernández, G. (2016). Pca-based multivariate statistical network monitoring for anomaly detection. Computers & Security, 59, 118–137.
Choi, S. W., Morris, J., & Lee, I.-B. (2008). Nonlinear multiscale modelling for fault detection and identification. Chemical engineering science, 63(8), 2252–2266.
de Oliveira, R. R., Pedroza, R. H., Sousa, A. O., Lima, K. M., & de Juan, A. (2017). Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy. Analytica chimica acta, 985, 41–53.
García-Alvarez, D. (2009). Fault detection using principal component analysis (pca) in a wastewater treatment plant (wwtp). Proceedings of the International Student’s Scientific Conference, 55–60.
Ge, Z., Yang, C., & Song, Z. (2009). Improved kernel pca-based monitoring approach for nonlinear processes. Chemical Engineering Science, 64(9), 2245–2255.
Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349.
Jaffel, I., Taouali, O., Harkat, M. F., & Messaoud, H. (2017). Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. The International Journal of Advanced Manufacturing Technology, 88, 3265–3279.
Joe Qin, S. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics: A Journal of the Chemometrics Society, 17(8-9), 480–502.
Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H., Strauss, R., & Bakshi, B. R. (2002). Comparison of multivariate statistical process monitoring methods with applications to the eastman challenge problem. Computers & chemical engineering, 26(2), 161–174.
Kourti, T., & MacGregor, J. F. (1996). Multivariate spc methods for process and product monitoring. Journal of quality technology, 28(4), 409–428.
Kresta, J. V., Macgregor, J. F., & Marlin, T. E. (1991). Multivariate statistical monitoring of process operating performance. The Canadian journal of chemical engineering, 69(1), 35–47.
Lemaigre, S., Adam, G., Goux, X., Noo, A., De Vos, B., Gerin, P. A., & Delfosse, P. (2016). Transfer of a static pca-mspc model from a steady-state anaerobic reactor to an independent anaerobic reactor exposed to organic overload. Chemometrics and Intelligent Laboratory Systems, 159, 20–30.
Rosén, C. (2001). A chemometric approach to process monitoring and control-with applications to wastewater treatment operation. Lund University.
Sandberg, E., Lennox, B., & Undvall, P. (2007). Scrap management by statistical evaluation of eaf process data. Control engineering practice, 15(9), 1063–1075.
Uchida, Y., Fujiwara, K., Saito, T., & Osaka, T. (2022). Process fault diagnosis method based on mspc and lingam and its application to tennessee eastman process. IFACPapersOnLine, 55(2), 384–389.
Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2000). Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and intelligent laboratory systems, 51(1), 95–114.
Wise, B. M., & Gallagher, N. B. (1996). The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6), 329–348.
Zhao, L.-T., Yang, T., Yan, R., & 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, 617–627.
Section
Technical Papers