Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/196882
Type: Artigo de periódico
Title: Ethyl Alcohol Production Optimization By Coupling Genetic Algorithm And Multilayer Perceptron Neural Network.
Author: Rivera, Elmer Ccopa
da Costa, Aline C
Maciel, Maria Regina Wolf
Maciel Filho, Rubens
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: Algorithms
Bacteria
Bioreactors
Computer Simulation
Ethanol
Models, Biological
Models, Genetic
Neural Networks (computer)
Pattern Recognition, Automated
Quality Control
Rights: fechado
Identifier DOI: 
Address: http://www.ncbi.nlm.nih.gov/pubmed/16915705
Date Issue: 2006
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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