A GENETIC ALGORITHM OPTIMIZATION MODEL FOR DIRECT PUMPING WATER SUPPLY INTAKES OPERATION

Rafa H. Al-Suhaili, Rasul M. Khalaf, Sanaa A.T. Al-Osmy

Abstract


An optimization model for direct water supply intakes operation was created to maximize the rate of recovery of river bed, i.e. minimizing the effects pumping on river bed morphology. The model adopted constraints such as the water volume that should be pumped daily, minimum and maximum pumping intake capacity, and maximum time of pumping per day, while the objective function maximized is the rate of recovery of the river bed . The decision variables are selected as the pumping rate to river flow ratio, and the intake operation time to time of non-operation ratio. The model combines the Genetic Algorthim and the Artificial Neural Networks techniques to find the optimum solution. The application indicated that the required number of the randomly generated solutions and the number of cross over process for a stable optimum genetic algorithm solution are (100) and (1) respectively. Sensitivity analysis of the effect of number of cycles of operation and non-operation periods of the intake per day and the river flow indicates that these variables have no effect on the objective function.

Keywords


River morphology, scouring, sedimentation, rate of recovery, artificial neural networks, genetic algorithm, optimization modlogy, scouring, sedimentation, rate of recovery, artificial neural networks, genetic algorithm, optimization models, water intakes.

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References


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DOI: https://doi.org/10.11113/mjce.v26n2.333

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