Artículos de revistas
Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network
Registro en:
Applied Biochemistry And Biotechnology. Humana Press Inc, v. 132, n. 41699, n. 969, n. 984, 2006.
0273-2289
1559-0291
WOS:000203005100009
Autor
Rivera, EC
da Costa, AC
Regina, M
Maciel, W
Maciel, R
Institución
Resumen
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. 132 41699 969 984