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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/dwes-11-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
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
Sadaf Haghiri
Department of Environmental Engineering, Middle East Technical University, Ankara, Turkey
Department of Civil Engineering, College of Engineering, University of Texas at Arlington, Arlington, Texas, USA
Consultant engineer at Daneshkar Ahwaz Company, Tehran, Iran
Sina Moharramzadeh
Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, Iowa, USA
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Cited
29 citations as recorded by crossref.
- Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models M. Achite et al. 10.1007/s10668-022-02835-0
- A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment K. Wang et al. 10.1016/j.compchemeng.2021.107383
- Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC) D. Wadkar et al. 10.1007/s40899-021-00532-w
- Removal of brilliant green dye from synthetic wastewater under batch mode using chemically activated date pit carbon R. Mansour et al. 10.1039/D0RA08488C
- Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants N. Mohd Najib et al. 10.1007/s40710-020-00431-w
- Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment S. McKelvey et al. 10.3389/fenve.2024.1401180
- An Intelligent Dosing Algorithm Model for Wastewater Treatment Plant X. Fang et al. 10.1088/1742-6596/2224/1/012027
- Performance prediction modeling of andesite processing wastewater physicochemical treatment via artificial neural network E. Yel et al. 10.1007/s12517-020-05940-4
- Towards sustainable and energy efficient municipal wastewater treatment by up-concentration of organics H. Guven et al. 10.1016/j.pecs.2018.10.002
- Coagulant dosage prediction in the water treatment process E. Tochio et al. 10.2166/ws.2023.219
- Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant S. Abba et al. 10.1016/j.jwpe.2019.101081
- Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization M. Achite et al. 10.1007/s10668-022-02523-z
- Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm H. Boumezbeur et al. 10.1007/s11356-023-27224-6
- Development of reservoir’s optimum operation rules considering water quality issues and climatic change data analysis B. Yaghoubi et al. 10.1016/j.scs.2020.102467
- A combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a drinking water treatment plant A. Chiavola et al. 10.1016/j.jprocont.2023.103068
- Application of cascade feed forward neural network to predict coagulant dose D. Wadkar et al. 10.1080/23249676.2021.1927210
- Application of soft computing in water treatment plant and water distribution network D. Wadkar et al. 10.1080/23249676.2021.1978881
- A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression S. Moeinizade et al. 10.1038/s41598-021-83634-x
- Application of a 2k–p Fractional Experimental Design in Coagulation-Flocculation Processes in the Treatment of Wastewater from a Slaughterhouse J. Carpintero et al. 10.3390/su141610402
- An efficient neural network model for aiding the coagulation process of water treatment plants C. Jayaweera & N. Aziz 10.1007/s10668-021-01483-0
- Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model S. Lin et al. 10.1016/j.watres.2023.119665
- Cationic Starch and Polyaluminum Chloride as Coagulants for River Nile Water Treatment S. Abdo et al. 10.1016/j.gsd.2020.100331
- Complementarity‐based selection strategy for genomic selection S. Moeinizade et al. 10.1002/csc2.20070
- Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management X. Fang et al. 10.1039/D2EW00560C
- Data to intelligence: The role of data-driven models in wastewater treatment M. Bahramian et al. 10.1016/j.eswa.2022.119453
- Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities S. Chowdhury & T. Karanfil 10.1016/j.chemosphere.2024.141958
- Potential application of natural coagulant extraction from walnut seeds for water turbidity removal T. Zedan et al. 10.2166/wpt.2022.019
- Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants A. Librantz & F. dos Santos 10.3390/su15086579
- Determination of coagulant dosages for process control using online UV-vis spectra of raw water Z. Shi et al. 10.1016/j.jwpe.2021.102526
29 citations as recorded by crossref.
- Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models M. Achite et al. 10.1007/s10668-022-02835-0
- A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment K. Wang et al. 10.1016/j.compchemeng.2021.107383
- Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC) D. Wadkar et al. 10.1007/s40899-021-00532-w
- Removal of brilliant green dye from synthetic wastewater under batch mode using chemically activated date pit carbon R. Mansour et al. 10.1039/D0RA08488C
- Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants N. Mohd Najib et al. 10.1007/s40710-020-00431-w
- Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment S. McKelvey et al. 10.3389/fenve.2024.1401180
- An Intelligent Dosing Algorithm Model for Wastewater Treatment Plant X. Fang et al. 10.1088/1742-6596/2224/1/012027
- Performance prediction modeling of andesite processing wastewater physicochemical treatment via artificial neural network E. Yel et al. 10.1007/s12517-020-05940-4
- Towards sustainable and energy efficient municipal wastewater treatment by up-concentration of organics H. Guven et al. 10.1016/j.pecs.2018.10.002
- Coagulant dosage prediction in the water treatment process E. Tochio et al. 10.2166/ws.2023.219
- Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant S. Abba et al. 10.1016/j.jwpe.2019.101081
- Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization M. Achite et al. 10.1007/s10668-022-02523-z
- Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm H. Boumezbeur et al. 10.1007/s11356-023-27224-6
- Development of reservoir’s optimum operation rules considering water quality issues and climatic change data analysis B. Yaghoubi et al. 10.1016/j.scs.2020.102467
- A combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a drinking water treatment plant A. Chiavola et al. 10.1016/j.jprocont.2023.103068
- Application of cascade feed forward neural network to predict coagulant dose D. Wadkar et al. 10.1080/23249676.2021.1927210
- Application of soft computing in water treatment plant and water distribution network D. Wadkar et al. 10.1080/23249676.2021.1978881
- A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression S. Moeinizade et al. 10.1038/s41598-021-83634-x
- Application of a 2k–p Fractional Experimental Design in Coagulation-Flocculation Processes in the Treatment of Wastewater from a Slaughterhouse J. Carpintero et al. 10.3390/su141610402
- An efficient neural network model for aiding the coagulation process of water treatment plants C. Jayaweera & N. Aziz 10.1007/s10668-021-01483-0
- Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model S. Lin et al. 10.1016/j.watres.2023.119665
- Cationic Starch and Polyaluminum Chloride as Coagulants for River Nile Water Treatment S. Abdo et al. 10.1016/j.gsd.2020.100331
- Complementarity‐based selection strategy for genomic selection S. Moeinizade et al. 10.1002/csc2.20070
- Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management X. Fang et al. 10.1039/D2EW00560C
- Data to intelligence: The role of data-driven models in wastewater treatment M. Bahramian et al. 10.1016/j.eswa.2022.119453
- Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities S. Chowdhury & T. Karanfil 10.1016/j.chemosphere.2024.141958
- Potential application of natural coagulant extraction from walnut seeds for water turbidity removal T. Zedan et al. 10.2166/wpt.2022.019
- Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants A. Librantz & F. dos Santos 10.3390/su15086579
- Determination of coagulant dosages for process control using online UV-vis spectra of raw water Z. Shi et al. 10.1016/j.jwpe.2021.102526
Latest update: 29 Jun 2024
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...
