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.
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Rivera, E.C., da Costa, A.C., Maciel, M.R.W. et al. Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network. Appl Biochem Biotechnol 132, 969–984 (2006). https://doi.org/10.1385/ABAB:132:1:969
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DOI: https://doi.org/10.1385/ABAB:132:1:969