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

• Published in: Applied biochemistry and biotechnology Vol. 129-132; no. 1-3; pp. 969 - 984
• Main Authors: Rivera, Elmer Ccopa, da Costa, Aline C, Maciel, Maria Regina Wolf, Maciel Filho, Rubens
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 2006
• Subjects: Algorithms; Bacteria - metabolism; Bioreactors - microbiology; Computer applications; Computer Simulation; Design of experiments; Ethanol; Ethanol - metabolism; Genetic algorithms; Identification methods; Models, Biological; Models, Genetic; Multilayer perceptrons; Neural networks; Neural Networks (Computer); Optimization; Pattern Recognition, Automated - methods; Quality Control; Studies
• ISSN: 0273-2289; 0273-2289; 1559-0291
• DOI: 10.1385/ABAB:132:1:969

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.