Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network.
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.
Rivera
Elmer Ccopa
EC
DPQ/FEQ/UNICAMP, Campinas, SP, Brasil Cx. Postal 6066, 13081-970. elmer@feq.unicamp.br
da Costa
Aline C
AC
Maciel
Maria Regina Wolf
MR
Maciel Filho
Rubens
R
D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism*; D008954:Models, Biological; D008957:Models, Genetic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011786:Quality Control
D000431:Ethanol