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Drinking Water Engineering and Science An interactive open-access journal
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A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant in Al Qusayr. Several models built and fitness detected for each, the network with the highest fitness selected, then the network was constructed. The network was able to predict turbidity with high accuracy, R (0. 9940) and a RMSE of 0.1078. And 4 equations for determining the value of the residual aluminum was obtained using Gene expression, the best equation results were, RMSE = 0.02, R = 0.9.
Preprints
https://doi.org/10.5194/dwes-2021-8
https://doi.org/10.5194/dwes-2021-8

  31 Mar 2021

31 Mar 2021

Review status: this preprint is currently under review for the journal DWES.

Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP

Ruba Alsaeed1, Bassam Alaji2, and Mazen Ebrahim3 Ruba Alsaeed et al.
  • 1Engineer at the Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
  • 2Prof. Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
  • 3Prof. Department of Engineering Management and Construction, Faculty of Civil Engineering, Damascus University, Damascus, Syria

Abstract. Turbidity is the most important parameter needed to check the status of drinking water, as it is an integrated parameter because its high values indicate high values of other parameters related to water quality. Coagulation and flocculation are the most essential processes for the removal of turbidity in drinking water treatment plants. Using alum coagulants increases the aluminum residuals in treated water, which have been linked to Alzheimer's disease pathogenesis.

In this paper, a hybrid algorithm (GA-ANN) used to predict the turbidity values in the drinking water purification plant in Al Qusayr was used.

The models were constructed using raw water data: turbidity of raw water, pH, conductivity, temperature, and coagulant dose, to predict the turbidity values coming out of the plant.

Several models built and fitness detected for each model, the network with the highest fitness was selected, and then a hybrid prediction network was constructed.

The selected network was the most able to predict turbidity of the outlet with high accuracy with a correlation coefficient (0. 9940) and a root mean square error of 0.1078.

And 4 equations for determining the value of the residual aluminum was obtained using Gene expression method, and the best equation produced results with very good accuracy, in this regard it can be referred to RMSE = 0.02 R = 0.9 for the best model.

Ruba Alsaeed et al.

Status: open (until 12 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on dwes-2021-8', fadi Asker, 02 Apr 2021 reply
    • AC1: 'Reply on CC1', Ruba Alsaeed, 02 Apr 2021 reply
  • CC2: 'Comment on dwes-2021-8', Salim Heddam, 03 Apr 2021 reply
    • AC3: 'Reply on CC2', Ruba Alsaeed, 12 Apr 2021 reply
    • AC4: 'Reply on CC2', Ruba Alsaeed, 12 Apr 2021 reply
  • CC3: 'Comment on dwes-2021-8', Lina khouri, 04 Apr 2021 reply
    • AC2: 'Reply on CC3', Ruba Alsaeed, 07 Apr 2021 reply
  • CC4: 'Comment on dwes-2021-8', Lilas Nabhan, 12 Apr 2021 reply

Ruba Alsaeed et al.

Ruba Alsaeed et al.

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Short summary
A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant in Al Qusayr. Several models built and fitness detected for each, the network with the highest fitness selected, then the network was constructed. The network was able to predict turbidity with high accuracy, R (0. 9940) and a RMSE of 0.1078. And 4 equations for determining the value of the residual aluminum was obtained using Gene expression, the best equation results were, RMSE = 0.02, R = 0.9.
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