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.
Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [ .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were constructed using data of the jar test experiments: turbidity, pH, alkalinity, and temperature, to predict the coagulant dose. The best GEP model gave very good results with a correlation coefficient (0.91) and a root mean square error of 1.8. Multi linear regression was used to be compared with the GEP results; it could not give good results due to the complex nonlinear relation of the process. Another round of experiments was done with high initial turbidity like the values that comes to the plant during floods and heavy rain. To give an equation for these extreme values, with studying the use of starch as a coagulant aid, the best GEP gave good results with a correlation coefficient of 0.92 and RMSE 5.1
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