Articles | Volume 11, issue 1
Research article
29 Jan 2018
Research article |  | 29 Jan 2018

Optimum coagulant forecasting by modeling jar test experiments using ANNs

Sadaf Haghiri, Amin Daghighi, and Sina Moharramzadeh

Related subject area

Treatment: Coagulation, sedimentation, flotation, flocculation
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Cited articles

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Bowden, G. J., Maier, H. R., and Dandy, G. G.: Optimal division of data for neural network models in water resources applications, Water Resour. Res., 38, 2-1–2-11, 2002. 
Bui, H. M., Perng, Y. S., and Duong, H. G. T.: The use of artificial neural network for modeling coagulation of reactive dye wastewater using Cassia fistula Linn. (CF) gum, J. Environ. Sci. Manag., 19, 1–8, 2016. 
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Short summary
Modeling can be used to overcome water jar test limitations. In this research study, MLP-type ANNs with one hidden layer have been used for modeling jar tests to determine the dosage level of coagulant used in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in the Ardabil province in Iran. To evaluate the performance of the model, the mean square error and the correlation coefficient parameters have been used.