Defining Influent Scenarios: Application of Cluster Analysis to a Water Reclamation Plant
Publication: Journal of Environmental Engineering
Volume 141, Issue 7
Abstract
Process control at older water reclamation plants (WRPs) can be challenging as operators work to balance higher energy costs and more stringent effluent limitations while managing fluctuating loads. To better understand WRP loading characteristics, we developed a classification system for the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC) Calumet WRP by combining -means cluster analysis with cross-tabulation analysis. Based on weather and influent composition characteristics, we identified 25 clusters; nine of those clusters are significant (99% confidence level) based on chi-square tests. Dry weather and middle temperature conditions after wet-weather days are common, and compared to cold-weather flows, warm-weather flows are more likely to have large precipitation events and more variation in influent quality. To explore how information about influent scenarios could inform process control and improve process efficiency, we also developed a process simulation model. The model indicates that aeration strategies for each of the nine scenarios could be modified to make it possible for the plant to operate more efficiently while still meeting effluent requirements. The approach described here can provide useful information on the frequency and duration of different types of loading, enabling WRP operators to move toward more flexible and efficient operations.
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Acknowledgments
This study is part of project that is funded by the National Science Foundation (NSF) (Award Number: 1035894) and collaborated with Metropolitan Water Reclamation District of Greater Chicago (MWRDGC). The authors wish to thank Dr. Catherine O’Connor and Judith Moran, MWRDGC, for providing the data.
References
Abbas, O. A. (2008). “Comparisons between data clustering algorithms.” Int. Arab J. Inf. Technol., 5(3), 320–325.
Agresti, A. (2007). An introduction to categorical data analysis, 2nd Ed., Wiley-Interscience, Hoboken, NJ.
Akbar, T. A., Hassan, Q. K., and Achari, G. (2011). “A methodology for clustering lakes in Alberta on the basis of water quality parameters.” Clean Soil, Air, Water, 39(10), 916–924.
Albazzaz, H., Wang, X. Z., and Marhoon, F. (2005). “Multidimensional visualisation for process historical data analysis: A comparative study with multivariate statistical process control.” J. Process Contr., 15(3), 285–294.
Angiulli, F., Basta, S., and Pizzuti, C. (2006). “Distance-based detection and prediction of outliers.” IEEE Trans. Knowl. Data Eng., 18(2), 145–160.
Astel, A., Tsakovskib, S., Barbieric, P., and Simeonovb, V. (2007). “Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets.” Water Res., 41(19), 4566–4578.
Brena, B. M., et al. (2005). “ELISA as an affordable methodology for monitoring groundwater contamination by pesticides in low-income countries.” Environ. Sci. Technol., 39(11), 3896–3903.
Chandramouli, V., Brion, G., Neelakantan, T. R., and Lingireddy, S. (2007). “Backfilling missing microbial concentrations in a riverine database using artificial neural networks.” Water Res., 41(1), 217–227.
Corona, F., Mulas, M., Haimi, H., Sundell, L., Heinonen, M., and Vahala, R. (2013). “Monitoring nitrate concentrations in the denitrifying post-filtration unit of a municipal wastewater treatment plant.” J. Process Control, 23(2), 158–170.
CPS. (2010). “CPS project on managing loosely coupled networked control systems with external disturbances: Wastewater processing.”, 〈http://www.nsf.gov/awardsearch/showAward?AWD_ID=1035894〉 (Apr. 28, 2014).
Dahm, K. G., Guerra, K. L., Xu, P., and Drewes, J. E. (2011). “Composite geochemical database for coalbed methane produced water quality in the rocky mountain region.” Environ. Sci. Technol., 45(18), 7655–7663.
De la Vega, P. T. M., de Salazar, W. M., Jaramillo, M. A., and Cros, J. (2012). “New contributions to the ORP & DO time profile characterization to improve biological nutrient removal.” Bioresour. Technol., 114, 160–167.
Flood, C., Ufnar, J., Wang, S., Johnson, J., Carr, M., and Ellender, R. (2011). “Lack of correlation between enterococcal counts and the presence of human specific fecal markers in Mississippi creek and coastal waters.” Water Res., 45(2), 872–878.
Grieu, S., Traore, A., Polit, M., and Colprim, J. (2005). “Prediction of parameters characterizing the state of a pollution removal biologic process.” Eng. Appl. Artif. Intell., 18(5), 559–573.
Haberman, S. J. (1973). “The analysis of residuals in cross-classified tables.” Biometrics, 29(1), 205–220.
Hamerly, G., and Elkan, C. (2003). “Learning the in -means.” Proc., 17th Annual Conf. on Neural Information Processing Systems (NIPS), Vancouver, Whistler, Canada, 281–288.
Hydromantis. (2013). “GPS-X is a modular, multi-purpose computer program for the modeling and simulation of municipal and industrial wastewater treatment plants.” 〈http://www.hydromantis.com/GPS-X.html〉 (Sep. 16, 2014).
IBM (International Business Machines). (2013). “SPSS statistical (statistical package for the social sciences), the latest version 22.0 was released on August, 2013.” 〈http://www-01.ibm.com/software/analytics/spss/〉 (Apr. 28, 2014).
Inselberg, A. (1997). “Multidimensional detective.” Proc., 1997 IEEE Information Visualization Conf., Phoenix, AZ, 100–107.
Isovitsch, S. L., and VanBriesen, J. M. (2008). “Sensor placement and optimization criteria dependencies in a water distribution system.” J. Water Resour. Plann. Manage., 186–196.
Jain, A. K. (2010). “Data clustering: 50 years beyond -means.” Pattern Recognit. Lett., 31(8), 651–666.
Kamble, S. R., and Vijay, R. (2011). “Assessment of water quality using cluster analysis in coastal region of Mumbai, India.” Environ. Monit. Assess., 178(1–4), 321–332.
Mohamad, I. B., and Usman, D. (2013). “Standardization and its effects on k-means clustering algorithm.” Res. J. Appl. Sci. Eng. Technol., 6(17), 3299–3303.
Mooi, E., and Sarstedt, M. (2011). “Chapter 9. Cluster analysis.” A concise guide to market research. The process, data, and methods using IBM SPSS statistics, Springer-Verlag Berlin Heidelberg, New York, 237–284.
Mukhopadhay, A., Akber, A., and Al-Awadi, E. (2011). “Evaluation of urban groundwater contamination from sewage network in Kuwait City.” Water Air Soil Pollut., 216(1–4), 125–139.
MWRDGC (Metropolitan Water Reclamation District of Greater Chicago). (2013). 〈https://www.mwrd.org〉 (Dec. 2013).
Nnane, D. E., Ebdon, J. E., and Taylor, H. D. (2011). “Integrated analysis of water quality parameters for cost-effective faecal pollution management in river catchments.” Water Res., 45(6), 2235–2246.
Oliker, N., and Ostfeld, A. (2014). “A coupled classification—Evolutionary optimization model for contamination event detection in water distribution systems.” Water Res., 51, 234–245.
Pham, D. T., Dimov, S. S., and Nguyen, C. D. (2005). “Selection of k in k-means clustering.” Proc. Inst. Mech. Eng. C-J. Mech. Eng. Sci., 219(1), 103–119.
Platikanov, S., Puig, X., Martín, J., and Tauler, R. (2007). “Chemometric modeling and prediction of trihalomethane formation in Barcelona’s water works plant.” Water Res., 41(15), 3394–3406.
Rousseeuw, P. J. (1987). “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” J. Comput. Appl. Math., 20(1), 53–65.
Smith, L. M., Engle, V. D., and Summers, J. K. (2006). “Assessing water clarity as a component of water quality in Gulf of Mexico estuaries.” Environ. Monit. Assess., 115(1–3), 291–305.
Xie, L., et al. (2013). “Soft sensors for online steam quality measurements of OTSGs.” J. Process Control, 23(7), 990–1000.
Information & Authors
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Published In
Journal of Environmental Engineering
Volume 141 • Issue 7 • July 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jun 2, 2014
Accepted: Dec 5, 2014
Published online: Jan 19, 2015
Discussion open until: Jun 19, 2015
Published in print: Jul 1, 2015
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