Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/65691
Type: Artigo de periódico
Title: Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network
Author: Rivera, EC
da Costa, AC
Regina, M
Maciel, W
Maciel, R
Abstract: In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem. A data-driven identification method based on MLPNN and optimal design of experiments is described in detail. The nonlinear model of an extractive ethanol process, represented by a MLPNN, is optimized using real-coded and binary-coded genetic algorithms to determine the optimal operational conditions. In order to check the validity of the computational modeling, the results were compared with the optimization of a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature.
Subject: alcoholic fermentation process
artificial intelligence
design of experiments
modeling
penalty function
Country: EUA
Editor: Humana Press Inc
Rights: fechado
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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