Genetic Algorithms for the Optimal Operation of Sprinkle Irrigation Systems under Deterministic Loading Conditions

2012 ◽  
pp. 130-135
Author(s):  
Matteo Matteo
Keyword(s):  
Genetic Algorithms ◽  
Optimal Operation ◽  
Loading Conditions ◽  
Irrigation Systems ◽  
Sprinkle Irrigation

scholarly journals Proposal of optimal operation strategy applied to water distribution network with statistical approach

2020 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Alex Takeo Yasumura Lima Silva ◽  
Fernando Das Graças Braga da Silva ◽  
André Carlos da Silva ◽  
José Antonio Tosta dos Reis ◽  
Claudio Lindemberg de Freitas ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Optimization Techniques ◽  
Experimental Techniques ◽  
Computational Optimization ◽  
Depth Analysis

 Inefficiency of sanitation companies’ operation procedures threatens the population’s future supplies. Thus, it is essential to increase water and energy efficiency in order to meet future demand. Optimization techniques are important tools for the analysis of complex problems, as in distribution networks for supply. Currently, genetic algorithms are recognized by their application in literature. In this regard, an optimization model of water distribution network is proposed, using genetic algorithms. The difference in this research is a methodology based on in-depth analysis of results, using statistics and the design of experimental tools and software. The proposed technique was applied to a theoretical network developed for the study. Preliminary simulations were accomplished using EPANET, representing the main causes of water and energy inefficiency in Brazilian sanitation companies. Some parameters were changed in applying this model, such as reservoir level, pipe diameter, pumping pressures, and valve-closing percentage. These values were established by the design of experimental techniques. As output, we obtained the equation of response surface, optimized, which resulted in values of established hydraulic parameters. From these data, the obtained parameters in computational optimization algorithms were applied, resulting in losses of 26.61%, improvement of 16.19 p.p. with regard to the network without optimization, establishing an operational strategy involving three pumps and a pressure-reducing valve.  We conclude that the association of optimization and the planning of experimental techniques constitutes an encouraging method to deal with the complexity of water-distribution network optimization.

scholarly journals Fuel Costs Minimization on a Steel Billet Reheating Furnace Using Genetic Algorithms

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Harley S. O. Santos ◽  
Paulo E. M. Almeida ◽  
Rodrigo T. N. Cardoso
Keyword(s):  
Genetic Algorithms ◽  
Fuel Consumption ◽  
Computational Simulation ◽  
Optimal Operation ◽  
Homogeneous Material ◽  
Reheating Furnace ◽  
Steel Billet ◽  
Discharge Temperature ◽  
Manufactured Products ◽  
Fuel Costs

Metallurgy industries often use steel billets, at a proper temperature, to achieve the desired metallurgical, mechanical, and dimensional properties of manufactured products. Optimal operation of steel billet reheating furnaces requires the minimization of fuel consumption while maintaining a homogeneous material thermal soak. In this study, the operation of a reheating furnace is modeled as a nonlinear optimization problem with the goal of minimizing fuel cost while satisfying a desired discharge temperature. For this purpose, a genetic algorithms approach is developed. Computational simulation results show that it is possible to minimize costs for different charge temperatures and production rates using the implemented method. Additionally, practical results are validated with actual data, in a specific scenario, showing a reduction of 3.36% of fuel consumption.

scholarly journals Artificial neural network model for simulation of water distribution in sprinkle irrigation

2015 ◽  
Vol 19 (9) ◽  
pp. 817-822
Author(s):  
Paulo L. de Menezes ◽  
Carlos A. V. de Azevedo ◽  
Eduardo Eyng ◽  
José Dantas Neto ◽  
Vera L. A. de Lima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Distribution ◽  
Field Trials ◽  
Operating Pressure ◽  
Ann Model ◽  
Irrigation Systems ◽  
Require Time ◽  
Artificial Neural ◽  
Sprinkle Irrigation

ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs) ranging from 0.92 to 0.97 and Willmott agreement index (d) from 0.950 to 0.991, between the observed and simulated values for ten analysed trials. The ANN model shows promise in the simulation of precipitation in sprinkle irrigation systems.

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