Articles | Volume 11, issue 1
Drink. Water Eng. Sci., 11, 1–8, 2018
https://doi.org/10.5194/dwes-11-1-2018
Drink. Water Eng. Sci., 11, 1–8, 2018
https://doi.org/10.5194/dwes-11-1-2018

Research article 29 Jan 2018

Research article | 29 Jan 2018

Optimum coagulant forecasting by modeling jar test experiments using ANNs

Sadaf Haghiri et al.

Related subject area

Treatment: Coagulation, sedimentation, flotation, flocculation
Optimization of coagulation-flocculation parameters using a photometric dispersion analyser
S. R. Ramphal and M. S. Sibiya
Drink. Water Eng. Sci., 7, 73–82, https://doi.org/10.5194/dwes-7-73-2014,https://doi.org/10.5194/dwes-7-73-2014, 2014

Cited articles

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Baxter, C. W., Stanley, S. J., and Zhang, Q.: Development of a full scale artificial neural network model for the removal of natural organic matter by enhanced coagulation, Aqua, 48, 129–136, 1999. 
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.