Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network

被引：0
|
作者：
Elmer Ccopa Rivera
Aline C. da Costa
Maria Regina Wolf Maciel
Rubens Maciel Filho
机构：
[1] DPQ/FEQ/UNICAMP,
来源：
Applied Biochemistry and Biotechnology | 2006年 / 132卷
关键词：
Alcoholic fermentation process; artificial intelligence; design of experiments; modeling; penalty function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：969 / 984
页数：15
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