Articles | Volume 11, issue 1
https://doi.org/10.5194/dwes-11-1-2018
© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2018
Research article |
| 29 Jan 2018
| 29 Jan 2018
Optimum coagulant forecasting by modeling jar test experiments using ANNs
Related subject area
Treatment: Coagulation, sedimentation, flotation, flocculation
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Cited articles
Ardabil's Water and Waste Water Company: The characteristics of the input and
output water, available at:
http://www.abfa-ardabil.co.ir/HomePage.aspx?lang=en-US&site=DouranPortal&tabid=1,
last access: 24 January 2018.
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.
Cybenko, G.: Approximation by Superpositions of a Sigmoidal Function Math,
Control Signals Systems, 2, 303–314, 1989.
