Applications of Radial-Basis Function and Generalized Regression Neural Networks for Modeling of Coagulant Dosage in a Drinking Water-Treatment Plant: Comparative Study
Publication: Journal of Environmental Engineering
Volume 137, Issue 12
Abstract
The coagulation process, which involves many complex physical and chemical phenomena, is one of the most important stages in water-treatment plants. The coagulant dosage rate is nonlinearly correlated to raw water characteristics such as turbidity, conductivity, and pH. The coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. The coagulant dosage has typically been determined through the jar test, which requires a long experiment time in a field-water-treatment plant. Modeling can be used to overcome these limitations. In this study, a model for the approximation of coagulant dosage rates in water-treatment plants in Algeria has been developed using artificial neural network (ANN) techniques. Two different ANN techniques, the generalized regression neural network (GRNN) and the radial-basis function neural network (RBFNN), were tested for this purpose. The trained GRNN model outperforms the corresponding RBFNN model.
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References
Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, Oxford, UK, 482.
Broomhead, D. S., and Lowe, D. (1988). “Multivariable functional interpolation and adaptive networks.” Complex Syst., 2, 321–355.
Celikoglu, H. B. (2006). “Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modeling.” Math. Comput. Model., 44(7–8), 640–658.
Celikoglu, H. B., and Cigizoglu, H. K. (2007). “Public transportation trip flow modelling with generalized regression neural networks.” Adv. Eng. Software, 38(2), 71–79.
Chen, C. L., and Hou, P. L. (2006). “Fuzzy model identification and control system design for coagulation chemical dosing of potable water.” Water Sci. Technol.: Water Supply, 6(3), 97–104.
Edzwald, J. K. (1993). “Coagulation in drinking water treatment: Particles, organics and coagulants.” Water Sci. Technol., 27(11), 21–35.
Gagnon, C., Grandjean, B. P. A., and Thibault, J. (1997). “Modelling of coagulant dosage in a water treatment plant.” Artif. Intell. Eng., 11(4), 401–404.
Haykin, S. (1994). Neural networks: A comprehensive foundation, Prentice Hall, Upper Saddle River, NJ.
Hernandez, H., and Le Lann, M. V. (2006). “Development of a neural sensor for on-line prediction of coagulant dosage in a potable water treatment plant in the way of its diagnosis.” Lect. Notes Comput. Sci., 4140, 249–257.
Ibrić, S., Jovanović, M., Djurić, Z., Parojčić, J., and Solomun, L. (2002). “The application of generalized regression neural network in the modelling and optimization of aspirin extended release tablets with Eudragit RS PO as matrix substance.” J. Control. Release, 82(2–3), 213–222.
Joo, D. S., Choi, D. J., and Park, H. (2000). “The effects of data preprocessing in the determination of coagulant dosing rate.” Water Res., 34(13), 3295–3302.
Kulkarni, S. G., Chaudhary, A. K., Nandi, S., Tambe, S. S., and Kulkarni, B. D. (2004). “Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN).” Biochem. Eng. J., 18(3), 193–210.
Larmrini, B., Benhammou, A., Le Lann, M. V., and Karama, A. (2005). “A neural software sensor for online prediction of coagulant dosage in a drinking water treatment plant.” Trans. Inst. Meas. Control (London), 27(3), 195–213.
Lippmann, R. P. (1987). “An introduction to computing with neural nets.” IEEE ASSP Mag., 3(4), 4–22.
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.
MathWorks. (2005). Matlab Programming. 〈http://www.mathworks.com〉 (Mar. 7, 2005).
Park, J., and Sandberg, I. W. (1991). “Universal approximation using radial basis function networks.” Neural Comput., 3(2), 246–257.
Park, S., Bae, H., and Kim, C. (2008). “Decision model for coagulant dosage using genetic programming and multivariate statistical analysis for coagulation/flocculation at water treatment process.” Korean J. Chem. Eng., 25(6), 1372–1376.
Robenson, A., Abd. Shukor, S. R., and Aziz, N. (2009). “Development of process inverse neural network model to determine the required alum dosage at Segama Water Treatment Plant Sabah, Malaysia.” Computer aided chemical engineering, Vol. 27, Elsevier, Amsterdam, Netherlands. 525–530.
Specht, D. F. (1991). “A general regression neural network.” IEEE Trans. Neural Networks, 2(6), 568–576.
Trinh, T. K., and Kang, L. S. (2011). “Response surface methodological approach to optimize the coagulation-flocculation process in drinking water treatment.” Chem. Eng. Res. Des., 1126–1135.
Van Leeuwen, J., Chow, C. W. K., Bursill, D., and Drikas, M. (1999). “Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters.” Aqua, 48(3), 115–127.
Wu, G. D., and Lo, S. L. (2008). “Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network based fuzzy inference system.” Eng. Appl. Artif. Intell., 21(8), 1189–1195.
Wu, G. D., and Lo, S. L. (2010). “Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network.” Expert Syst. Appl., 37(7), 4974–4983.
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© 2011 American Society of Civil Engineers.
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Received: Oct 29, 2010
Accepted: May 25, 2011
Published online: May 27, 2011
Published in print: Dec 1, 2011
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