Browse > Article
http://dx.doi.org/10.4491/KSEE.2015.37.3.137

Development of Real-Time Water Quality Abnormality Warning System for Using Multivariate Statistical Method  

Heo, Tae-Young (Department of Information & Statistics, Chungbuk National University)
Jeon, Hang-Bae (Department of Information & Statistics, Chungbuk National University)
Park, Sang-Min (Monitoring and Analysis Division, Saemangeum Regional Environmental Office)
Lee, Young-Joo (Water Research Center, K-water Institute, K-water)
Publication Information
Journal of Korean Society of Environmental Engineers / v.37, no.3, 2015 , pp. 137-144 More about this Journal
Abstract
The purpose of this study is to develop an warning system to detect real-time water quality abnormality using a multivariate statistical approach. In this study, we applied principal component analysis among multivariate data analyses which was used for the correlation between water quality parameters considering the real-time algorithm to determine abnormality in water quality. We applied our approach to real field data and showed the utilization of algorithm for the real-time monitoring to find water quality abnormality. In addition, our approach with Korea Meterological Adminstration database identified heavy rain data due to climate change is one of the most important factors to explain water quality abnormality.
Keywords
Multivariate statistical method; Warning system; Principal component Analysis; Real-Time monitoring;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Oh, H. J., Hwang, T. M., Jung, J. H. and Bahn, S. H., "Development of Web-Based Auto Monitoring System for the Control of Conventional Water Treatment System," Proceeding of KSEE, Chonbuk University, pp. 1086-1090(2004).
2 Mason, R. L., Tracy, N. D. and Young, J. C., "Monitoring a multivariate step process," J. Qual. Technol., 28, 39-50(1996).
3 Mason, R. L. and Young J. C., "Multivariate Statistical Process Control with Industrial Applications," ASA-SIAM(2002).
4 Nomikos, P. and MacGregor, J. F., "Multivariate SPC Charts for Monitoring Batch Processes," Technometrics, 37(1), 41-58(1995).   DOI   ScienceOn
5 Wise, B. M. and Gallagher, N. B., "The Process Chemometrics Approach to Process Monitoring and Fault Detection," J. Proc. Control, 6(6), 329-348(1996).   DOI   ScienceOn
6 Chen, Q., Wynne, R. J., Goulding, P. and Sandoz, D., "The Application of Principal Component Analysis and Kernel Density Estimation to Enhance Process Monitoring," Control Eng. Prac., 8(5), 531-543(2000).   DOI   ScienceOn
7 Kourti, T., "Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions," J. Chemometrics, 17(1), 93-109(2003).   DOI   ScienceOn
8 Montgomery, D. C., Introduction to Statistical Quality Control, 3rd Edition, John Wiley & Sons Inc., USA(1996).
9 Kourti, T. and MacGregor, J. F., "Process Analysis, Monitoring and Diagnosis Using Multivariate Projection Methods," Chemomet. Intelligent Laboratory Syst., 28(1), 3-21(1995).   DOI   ScienceOn
10 Yang, Y. J., Haught, R. C. and Goodrich, J. A., "Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results," J. Environ. Manage., 90(8), 2494-2506 (2009).   DOI   ScienceOn
11 Lennox, J. and Rosen, C., "Adaptive multiscale principal components analysis for online monitoring of wastewater treatment," Water Sci. Technol., 45(4-5), 227-235(2002).
12 Baggiani, F. and Marsili-Libelli, S., "Real-time fault detection and isolation in biological wastewater treatment plants," Water Sci. Technol., 60(11), 2949-2961(2009).   DOI   ScienceOn
13 Choi, S. W., Martin, E. B., Morris, A. J. and Lee, I., "Adaptive Multivariate Statistical Process Control for Monitoring Time-varying Processes," Ind. Eng. Chem. Res., 45(9), 3108-3118(2006).   DOI   ScienceOn
14 Hotelling, H., "The Generalization of Student's Ratio," Annal. Mathematical Statistics, 2, 360-378(1931).   DOI
15 Hotelling, H., Multivariate Quality Control, McGraw-Hill, New York(1947).
16 Mason, R. L., Tracy, N. D. and Young, J. C., "Decomposition of $T^2$ for multivariate control chart interpretation," J. Qual. Technol., 27(2), 99-108(1995).
17 Jackson, J. E. and Mudholkar, G. S. "Control procedures for residuals associated with principal component analysis," Technometrics, 21(3), 341-349(1979).   DOI   ScienceOn
18 Mason, R. L., Tracy, N. D. and Young, J. C., "A practical approach for interpreting multivariate $T^2$ control chart signals," J. Qual. Technol., 29(4), 396-406(1997).
19 Mason, R. L. and Young, J. C., "Improving the sensitivity of the $T^2$ statistic in multivariate process Control," J. Qual. Technol., 31(2), 155-165(1999).
20 George, J. P., Chen, Z. and Shaw, P., "Fault detection of drinking water treatment process using PCA and Hotelling's $T^2$ chart," World Acad. Sci. Eng. Technol., 3, 733-738(2009).