Optimum coagulant forecasting by modeling jar test experiments using ANNs

Haghiri, Sadaf; Daghighi, Amin; Moharramzadeh, Sina

doi:https://doi.org/10.5194/dwes-11-1-2018

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.

https://doi.org/10.5194/dwes-11-1-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 1

Research article

|

29 Jan 2018

Research article |

| 29 Jan 2018

Optimum coagulant forecasting by modeling jar test experiments using ANNs

Sadaf Haghiri, Amin Daghighi, and Sina Moharramzadeh

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Final revised paper (published on 29 Jan 2018)
Preprint (discussion started on 05 Sep 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Stephen Mounce, 25 Sep 2017
- AC1: 'Answer of comment 1', Amin Daghighi, 30 Oct 2017
- AC4: 'Paper Version 3', Amin Daghighi, 30 Oct 2017
RC2: '-', Anonymous Referee #2, 02 Oct 2017
- AC2: 'Answer of comment 1', Amin Daghighi, 30 Oct 2017
- AC5: 'Paper Version 3', Amin Daghighi, 30 Oct 2017
RC3: 'Interactive comment on “Optimum Coagulant Forecasting with Modeling the Jar Test Experiments Using ANN” by Sadaf Haghiri et al.', Anonymous Referee #3, 03 Oct 2017
- AC3: 'Answer of comment 1', Amin Daghighi, 30 Oct 2017
- AC6: 'Paper Version 3', Amin Daghighi, 30 Oct 2017

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.