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Drinking Water Engineering and Science An interactive open-access journal

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Drink. Water Eng. Sci., 11, 1-8, 2018
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
Optimum coagulant forecasting by modeling jar test experiments using ANNs
Sadaf Haghiri1, Amin Daghighi2,3, and Sina Moharramzadeh4 1Department of Environmental Engineering, Middle East Technical University, Ankara, Turkey
2Department of Civil Engineering, College of Engineering, University of Texas at Arlington, Arlington, Texas, USA
3Consultant engineer at Daneshkar Ahwaz Company, Tehran, Iran
4Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, Iowa, USA
Abstract. Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc.) that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN) multi-layer perceptron (MLP) with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE) and correlation coefficient (R2) parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform experimental jar tests but also to predict a proper dosage for coagulant amounts and to project the quality of the output water under real conditions.

Citation: Haghiri, S., Daghighi, A., and Moharramzadeh, S.: Optimum coagulant forecasting by modeling jar test experiments using ANNs, Drink. Water Eng. Sci., 11, 1-8, https://doi.org/10.5194/dwes-11-1-2018, 2018.
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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.
Modeling can be used to overcome water jar test limitations. In this research study, MLP-type...
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