Optimal temperature control of solar heating systems

Uysal, Mithat; Tunç, Murat

doi:10.1002/er.4440150403

Cited by 1 publication

(1 citation statement)

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“…But in this study, GA was applied because of its intuitiveness, ease of implementation, the ability to effectively solve highly nonlinear and mixed integer optimization problems. 26,27 In the first step, the initial population of chromosomes is initialized. In the second step, the designed neural network was used as a fitness function.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm

Vadood

Semnani

Morshed

2010

J of Applied Polymer Sci

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Acrylic fibers are synthetic fibers with wide applications. A couple of methods can be utilized in their manufacture, one of which is the dry spinning process. The parameters in this method have nonlinear relationships, making the process very complex. To the best of the authors' knowledge, no comprehensive study has yet been conducted on the optimization of acrylic dry spinning production using computer algorithms. In this study, such parameters as extruder temperature in and around the head, solution viscosity, water content in the solution, formic acid content of the solution, and the retention time of the solution in the reactor were measured in an attempt to predict the behavior of the dry spinning process. The color index of the manufactured fibers was used as an indicator of production quality and statistical methods were employed to determine the parameters affecting the process. An artificial neural network (ANN) using the back propagation training algorithm was then designed to predict the color index. ANN parameters including the number of hidden layers, number of neurons in each layer, adaptive learning rate, activation functions, number of max fail epochs, validation and test data were optimized using a genetic algorithm (GA). The trial and error method was used to optimize the GA parameters like population size, number of generations, crossover or mutation rates, and various selection functions. Finally, an ANN with a high accuracy was designed to predict the behavior of the dry spinning process. This method is capable of preventing the manufacturing of undesired fibers. V C 2010 Wiley Periodicals, Inc. J Appl Polym Sci 120: [735][736][737][738][739][740][741][742][743][744] 2011

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Section: Genetic Algorithmmentioning

confidence: 99%