doi: 10.7763/IJMO.2012.V2.98
Genetic Algorithms for the Optimal Operation of Sprinkle Irrigation Systems under Deterministic Loading Conditions
Matteo Nicolini
- Department of Chemistry, Physics and Environment, University of Udine, Via Cotonificio, 114, 33100 Udine, Italy.
Abstract
The paper presents a methodology developed for the optimal management and operation of sprinkle irrigation networks. Some typical problems are presented and solved through Genetic Algorithms (GAs), assuming that the loads (demands) at nodes are cyclic and deterministically established. In particular, an algorithm for model calibration is first introduced, aimed at the minimization of the maximum errors between measured and calculated values. Since the operation of such systems is highly water and energy demanding, two algorithms for controlling pressure and pumps are described: the first is aimed at finding the optimal location and control of a set of devices (pressure reducing valves and/or closed gate valves) in order to maintain a desired range of pressure throughout the network, while the second is focused at finding the optimal regulation of inverters for variable speed pumps in order to minimize energetic costs. An application to a real system is finally presented.
Keywords
- Energy
- variable speed pumps
- water distribution networks
How to Cite
Matteo Nicolini, "Genetic Algorithms for the Optimal Operation of Sprinkle Irrigation Systems under Deterministic Loading Conditions," International Journal of Modeling and Optimization, vol. 2, no. 2, pp. 130-135, 2012. https://doi.org/ 10.7763/IJMO.2012.V2.98
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).