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Drinking Water Engineering and Science An interactive open-access journal

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https://doi.org/10.5194/dwes-2017-24
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
05 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Drinking Water Engineering and Science (DWES).
Optimum Coagulant Forecasting with Modeling the Jar Test Experiments Using ANN
Sadaf Haghiri1, Sina Moharramzadeh2, and Amin Daghighi3,4 1Department of Environmental Engineering Faculty of Middle East Technical University, Turkey
2Department of Civil, Construc tion and Environmental Engineering, Iowa State University, Iowa, USA
3Department of Civil and Environmental Engineering, Cleveland State University, Ohio, USA
4Consultant Engineer at Daneshkar Ahwaz Company, Tehran, Iran
Abstract. Nowadays the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are among the common ways through which the use of coagulants leads to instability of particles and 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. While 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, and in this research study, 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 the Ardabil province. To evaluate the performance of the model, the parameters Mean Squared Error (MSE) and the Correlation Coefficient R2 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 variable conditions.

Citation: Haghiri, S., Moharramzadeh, S., and Daghighi, A.: Optimum Coagulant Forecasting with Modeling the Jar Test Experiments Using ANN, Drink. Water Eng. Sci. Discuss., https://doi.org/10.5194/dwes-2017-24, in review, 2017.
Sadaf Haghiri et al.
Sadaf Haghiri et al.
Sadaf Haghiri et al.

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