Neural net based hybrid modeling of the methanol synthesis process
Potočnik, Primož (Author), Grabec, Igor (Author), Šetinc, Marko (Author), Levec, Janez (Author)

URLURL - Presentation file, Visit http://dx.doi.org/10.1023/A:1009615710515 This link opens in a new window

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.

Keywords:neural networks, hybrid modeling, genetic algorithms, feature selection, methanol synthesis
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:str. 219-228
Numbering:Vol. 11, no. 3
ISSN on article:1370-4621
COBISS.SI-ID:2174490 Link is opened in a new window
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Record is a part of a journal

Title:Neural processing letters
Shortened title:Neural Process. Lett.
Publisher:D facto s.a.
COBISS.SI-ID:17174533 This link opens in a new window

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