Application of Artificial Neural Networks for Filtration Optimization
Publication: Journal of Environmental Engineering
Volume 137, Issue 11
Abstract
Granular media filtration is an important process in drinking water treatment to ensure the adequate removal of particle-bound pathogens (i.e., Giardia and Cryptosporidium). Filtration performance is typically monitored in terms of filtered water turbidity. However, particle counts may provide further insight into treatment efficiency, as they have a greater sensitivity for detecting small changes in filtration operation. Artificial neural networks (ANN) models were applied to optimize filtration at the Elgin Area water treatment plant (WTP) in terms of postfiltration particle counts. Process models were successfully developed to predict postfiltration particle counts. Two inverse-process models were developed to predict the optimal coagulant dosage required to attain target particle counts. Upon testing each model, a high correlation was observed between the actual and predicted data sets. The ANNs were then integrated into an optimization application to allow for the transfer of real-time data between the models and the online supervisory control and data acquisition (SCADA) system.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC)NSERC Chair in Drinking Water Research and the City of London. The writers would also like to acknowledge Insyght Systems Inc. for its assistance during development of the ANN software application.
References
Balakrishnan, S. N., and Weil, R. D. (1996). “Neurocontrol: A literature survey.” Math. Comput. Model., 23(1/2), 101–117.
Basheer, I., and Hajmeer, M. (2000). “Artificial neural networks: Fundamentals, computing, design, and application.” J. Microbiol. Methods, 43, 3–31.
Baxter, C. W., Stanley, S. J., Zhang, Q., and Smith, D. W. (2002). “Developing artificial neural network models of water treatment processes: A guide for utilities.” J. Environ. Eng. Sci., 1, 201–211.
Betancourt, W. Q., and Rose, J. B. (2004). “Drinking water treatment processes for removal of Cryptosporidium and Giardia.” Vet. Parasitol., 126(1–2), 219–234.
Chai, A. (1998). “Application of neural networks to the modelling of water treatment particulate removal processes.” M.S. thesis, Univ. of Toronto.
Gippel, C. J. (1995). “Potential of turbidity monitoring for measuring the transport of suspended solids in streams.” Hydrol. Processes, 9, 83–97.
Griffiths, K. (2010). “The application of artificial neural networks for filtration optimization in drinking water treatment.” M.S. thesis, Univ. of Toronto.
Gyurek, L. L., and Finch, G. R. (1998). “Modelling water treatment chemical disinfection kinetics.” J. Environ. Eng., 124(9), 783–793.
Hargesheimer, E. E., McTigue, N. E., Mielke, J. L., Yee, P., and Elford, T. (1998). “Tracking filter performance with particle counting.” J. Am. Water Works Assoc., 90(12), 32–41.
Hatukai, S., Ben-Tzur, Y., and Rebhun, M. (1997). “Particle counts and size distribution in system design for removal of turbidity by granular deep bed filtration.” Water Sci. Technol., 36(4), 225–230.
Haykin, S. (1994). Neural networks: A comprehensive foundation, Macmillan, New York.
Krose, B., and van der Smagt, P. (1996). “Back-propagation.” An introduction to neural networks, 8th Ed., Univ. of Amsterdam, 35–36.
LeChevallier, M. W., and Norton, W. D. (1992). “Examining relationships between particle counts and Giardia, Cryptosporidium, and turbidity.” J. Am. Water Works Assoc., 84(12), 54–60.
Lewin, N., Zhang, Q, Chu, L., and Shariff, R. (2004) “Predicting total trihalomethane formation in finished water using artificial neural networks.” J. Environ. Eng. Sci., 3, S35–S43.
Lewis, D. J., Tate, K. W., Dahlgren, R. A., and Newell, J. (2002). “Turbidity and total suspended solid concentration dynamics in streamflow from California oak woodland watersheds.” Rep. PSW-GTR-184, U.S. Dept. of Agriculture (USDA) Forest Service Gen. Tech., Washington, DC.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resource variables: A review of modelling issues and applications.” Environ. Model. Software, 15(1), 101–124.
Maier, H. R., Morgan, N., and Chow, C. W. K. (2004). “Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters.” Environ. Model. Software, 19(5), 485–494.
Morrison, J. (2000). for VB programmers, Apress Media, New York.
Nelson, M. M., and Illingworth, W. T. (1991). A practical guide to neural nets, Addison-Wesley, Boston.
Nieminski, E. C., and Ongerth, J. E. (1995). “Removing Giardia and Cryptosporidium by conventional treatment and direct filtration.” J. Am. Water Works Assoc., 87(9), 96–106.
Rodriguez, M. J., and Sérodes, J. B. (1998). “Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems.” Environ. Model. Software, 14(1), 93–102.
Stein, R. (1993). “Selecting data for neural networks.” AI Expert, 8(2), 42–47.
Tupas, R.-R. T. (2000). “Artificial neural network modelling of filtration performance.” M.S. thesis, Univ. of Alberta, Edmonton, Alberta.
Yu, M. J., et al. (2006). “Evaluation of the rapid filtration system with particle size distribution and Cryptosporidium in different operating conditions.” Water Sci. Technol.: Water Supply, 6(1), 129–139.
Zhang, Q., Cudrak, A. A., Shariff, R., and Stanley, S. J. (2004). “Implementing artificial neural network models for real-time water colour forecasting in a water treatment plant.” J. Environ. Eng. Sci., 3, S15–S23.
Zhang, Q., and Stanley, S. J. (1999). “Real-time water treatment process control with artificial neural networks.” J. Environ. Eng., 125(2), 153–160.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 137 • Issue 11 • November 2011
Pages: 1040 - 1047
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Oct 19, 2010
Accepted: Jun 2, 2011
Published online: Jun 4, 2011
Published in print: Nov 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.