Neural Net Based Hybrid Modeling of the Methanol Synthesis Process |
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Authors: | Potočnik Primož Grabec Igor Šetinc Marko Levec Janez |
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Affiliation: | (1) Laboratory of Technical Physics, Faculty of Mechanical Engineering, University of Ljubljana, Slovenia;(2) Laboratory of Technical Physics, Faculty of Mechanical Engineering, University of Ljubljana, Slovenia;(3) Laboratory for Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Slovenia;(4) Laboratory for Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Slovenia |
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Abstract: | A Hybrid modeling approach, combining an analytical model with a radial basis function neural network is introduced in this paper. The modeling procedure is combined with genetic algorithm based feature selection designed to select informative variables from the set of available measurements. By only using informative inputs, the model's generalization ability can be enhanced. The approach proposed is applied to modeling of the liquid–phase methanol synthesis. It is shown that a hybrid modeling approach exploiting available a priori knowledge and experimental data can considerably outperform a purely analytical approach. |
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Keywords: | hybrid modeling genetic algorithms feature selection methanol synthesis neural networks |
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