A Neural Network Model for Prediction of Tri-Halo-Methane Concentration in Drinking Water

Subject Areas : environmental management

Mohammad Javad Zoqi ¹ , Mohammad Ali Jafari ²

1 - Senior Expert of Environmental Engineering, Member of research institute of Environment of Jahad Daneshgahi
2 - Senior Expert of Environmental Engineering, Research institute of Environment of Jahad Daneshgahi

Received: 2009-04-04 Accepted : 2009-06-07 Published : 2014-09-23

Keywords: Neural Network, tri-halo-methane concentration, chlorine, water quality parameters,

Abstract :

In this study a neural network model is proposed for modeling tri-halo-methane concentration indrinking water. After training, the neural network model predicts tri-halo-methane concentration basedon input data. Parameters such as pH, Temperature, free chlorine residue and TOC were used as inputdata. To validate the proposed method, a case study was carried out, based on the data obtained fromGuilan grand treatment plant (Sangar). The Levenberg-Marquardt algorithm was selected as the bestof thirteen back-propagation algorithms. The optimal neuron number for Levenberg-Marquardtalgorithm is 8 neurons. The performance of modeling was determined. The trends of the forecast andmeasured data were in good agreement.

References:

منابع

1. صمدی، محمدتقی و همکاران، بهار1385. "بررسی مقایسه ای حذف تری هالومتان ها از آب شرب با ستون آکنده زدایش با هوا و نانوفیلتراسیون"، مجله آب و فاضلاب، شماره 57،


2. Garcia-Villanova, R.J., Garcia, C., Gomez, A., Paz Garcia, M., & Ardanuy, R. (1997). Formation, evolution, and modeling of trihalomethane in the drinking water of a town: I. At the municipal treatment utilities. Water Research, 31 (6), 1229-1308.                                                   


3. Hsu, Ching-Hung, Woei-Lih Jeng, Ruey-Mai Chang, Ling-Chu Chien, and Bor-Cheng Han., (2001). “Estimation of potential lifetime cancer risks for trihalomethane from consuming chlorinated drinking water in Taiwan” J. Environment Research, 85, 77-82                 


4. W. Elshorbagy., 2000. Kinetics of THM species in finished water, J. Water Resource Planning and Management 126(1) 21-28.


5. غنی زاده، قادر و نقی ئی، محمدرضا. 1382. "پیامدهای بارداری ناشی از مصرف آب شرب حاوی ترکیبات آلی کلره"، مجموعه مقالات ششمین همایش بهداشت محیط


6. World Health Organization, Geneva. 1984. World Health Organization, Guidelines for Drinking Water Quality, Vol. 2: Health Criteria and Other Supporting Information


7. ناصری، سیمین. 1375. "فرآورده های جانبی کلرزنی و روشهای کاهش آنها"، مجله آب و محیط زیست، شماره 4.


8. Bestamin, O., Ahmet, D., 2007. Neural network prediction model for methane fraction inbiogas from field scale landfill ­­bioreactors. Environmental Modelling & Software 22:  815-822.


9. Rodriguez, M.J., Se´rodes, J.B., 1999. Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environmental Modelling & Software 14 (1), 93-102.


10. Onkal-Engin, G., Demir, I., Engin, S.N., 2005. Determination of the relationship between sewage odour and BOD by neural networks. Environmental Modelling & Software 20 (7), 843-850.


11. Kolehmainen, M., Martikainen, H., Ruuskanen, J., 2001.  Neural networks and periodic components used in air quality forecasting. Atmospheric Environment 35:  815-825.


12. Holubar, P., Zani, L., Hager, M., Fro¨schl, W., Radak, Z., Braun, R., 2002. Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research 36, 2582-2588.


13. Maier, H.R., Dandy, G.C., 1998. Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study. Environmental Modelling & Software 13 (2), 179-191.


14. Hagan, M.T., Demuth, H.B., Beale, M.H., 1996. Neural Network Design. PWS Publishing, Boston, MA.


15. Abdi, H., Valentin, D., Edelman, B., O’Toole, A.J., 1996. A WidroweHoff learning rule for a generalization of the linear auto-associator. Journal of Mathematical Psychology 40 (2), 175-182.


16. A.P.H.A., A.W.W.A. & W.P.C.F., 1992. Standard methods for the examination of water and wastewater. 16th edition. American Public Health Association, American water Works Association and Water Pollution Control Federation, Washington, D.C.


17. حاج کاظمی ها، نرگس. 1379. "بررسی وضعیت کدورت، pH، کلر باقیمانده و تری هالومتانها در آب استخرهای شنای دائمی در سطح تهران"، پایان نامه کارشناسی ارشد، دانشکده محیط زیست دانشگاه علوم و تحقیقات تهران.


18. Md. Pauzi Abdullah, C.H. yew, Mohamad Salleh bin Ramli., 2003. “Formation, modeling and validation of trihalomethanes in Malaysian drinking water: a case study in the districts of Tampin, Negeri Sembilanand Sabak Bernam, Selangor”,water research 37 4637-4644.                    


19. Hamed, M.M., Khalafallah, M.G., Hassanien, E.A., 2004. Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software 19 (10), 919-928.


20. Almasri, M.N., Kaluarachchi, J.J., 2005. Modular neural networks to predict the nitrate distribution in ground water using the onground nitrogen loading and recharge data. Environmental Modelling & Software 20 (7), 851- 871.