What is it about?
We have developed a pumping schedule optimization model based on genetic algorithms (GA) and artificial neural networks (ANN) to reduce the energy and operating costs of pumping stations. The model is called NNGA. The optimization model was compared to two pumping schedules: a water services schedule and a schedule generated by a real-time flow regulation model. We have also developed a list of energy performance indicators to evaluate and diagnose the pumping schedule.
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Why is it important?
We have developed a specific algorithm to provide acceptable solutions for the initial population of the genetic algorithm (GA), which greatly improves the performance of GA. The GA is also characterized by the non-use of the operation of the binary coding / decoding in order to reduce the calculation time. The water demand required for calculating the optimization constraints is provided by a seasonal NNA with single output and looped type without the use of bias. While the evaluation and analysis of the pumping variables is done using the performance indicators that we have developed.
Perspectives
I hope that through this study I contribute to the improvement of optimization techniques used in the field of supply of drinking water, and precisely pumping system. The tool developed is not yet perfect because it requires taking into account other parameters. I hope this article will pave the way for other ideas in order to propose other solutions for the problems of overconsumption of energy.
Dr ahcene bouach
Ecole Nationale Polytechnique
Read the Original
This page is a summary of: Energetic optimization and evaluation of a drinking water pumping system: application at the Rassauta station, Water Science & Technology Water Supply, May 2018, IWA Publishing,
DOI: 10.2166/ws.2018.092.
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