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| Titre: | Neural network ARX model for gas conditioning tower |
| Auteur(s): | Haddouche, Rezki
Boukhemis, Chetate
Mohand Said, Boumedine |
| Mots-clés: | Gas conditioning tower
Artificial neural network
System identification
Dust collector |
| Date de publication: | 2019 |
| Editeur: | Taylor and Francis Online |
| Collection/Numéro: | International Journal of Modelling and Simulation, Volume 39, 2019 - Issue 3; |
| Résumé: | This work focuses on the identification of the gas conditioning tower (GCT) operating in a cement plant. It is an important element in the cement production line. Mathematical modeling of such a process proves to be very complex. This is due to the phenomena that occur during the operation of the system. An artificial neural network-based auto-regressive with exogenous inputs (NNARX) model is constructed with the aim to study the system as well as used to control the process. Resulted models are tested and validated using data extracted on a GCT operating at Chlef cement plant in Algeria. |
| URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6077 |
| ISSN: | 02286203
https://doi.org/10.1080/02286203.2018.1538848 |
| Collection(s) : | Publications Internationales
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