title: Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network.
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.
authors.affiliation: DPQ/FEQ/UNICAMP, Campinas, SP, Brasil Cx. Postal 6066, 13081-970. elmer@feq.unicamp.br
authors.forename: Elmer Ccopa
authors.initials: EC
authors.lastname: Rivera
authors.forename: Aline C
authors.initials: AC
authors.lastname: da Costa
authors.forename: Maria Regina Wolf
authors.initials: MR
authors.lastname: Maciel
authors.forename: Rubens
authors.initials: R
authors.lastname: Maciel Filho
mesh_terms: 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
chemical_list: D000431:Ethanol