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Artificial neural network and grid search aided optimization of temperature profile of temperature gradient reactor for dimethyl ether synthesis from syngas / Kohji Omata in Industrial & engineering chemistry research, Vol. 48 N°2 (Janvier 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p. 844–849 Titre : Artificial neural network and grid search aided optimization of temperature profile of temperature gradient reactor for dimethyl ether synthesis from syngas Type de document : texte imprimé Auteurs : Kohji Omata, Auteur ; Masahiko Hashimoto Sutarto, Auteur ; Yamada, Muneyoshi, Auteur Année de publication : 2009 Article en page(s) : p. 844–849 Note générale : chemical engineering Langues : Anglais (eng) Mots-clés : temperature  gradient  reactor Résumé : The temperature setting of a fixed bed reactor with a temperature gradient (TGR, temperature gradient reactor) was optimized using an artificial neural network (ANN) and grid search to attain high one-pass CO conversion for one-step dimethyl ether (DME) synthesis from syngas (3CO + 3H2 → DME + CO2). In the TGR, the catalyst bed was divided into 5 zones in series, and the temperature of each zone was optimized. Experiments were designed using an orthogonal array, and the experimental result was used for training the ANN to correlate the temperature setting and CO conversion. A grid search on the trained ANN was applied to find the optimum temperature setting. TGR was effective in overcoming both the equilibrium limit of the reaction at high temperature and the low activity of the catalyst at low temperature. To attain high CO conversion, Cu−Zn−Al−Ti−Nb−V−Cr catalysts with the optimized composition for each reaction temperature and γ-alumina were packed into the 5 zones of the TGR. As a result, a high one-pass conversion of CO at 82% was attained at 1 MPa, W/F = 50 g-cat·h/mol by means of the combination of the optimum catalyst and TGR. The CO conversion is much higher in comparison to the 72% found in TGR with a standard Cu catalyst, and to 69.5% in the isothermal reactor at 523K with a standard Cu catalyst. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8008633 [article] Artificial neural network and grid search aided optimization of temperature profile of temperature gradient reactor for dimethyl ether synthesis from syngas [texte imprimé] / Kohji Omata, Auteur ; Masahiko Hashimoto Sutarto, Auteur ; Yamada, Muneyoshi, Auteur . - 2009 . - p. 844–849. chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p. 844–849 Mots-clés : temperature  gradient  reactor Résumé : The temperature setting of a fixed bed reactor with a temperature gradient (TGR, temperature gradient reactor) was optimized using an artificial neural network (ANN) and grid search to attain high one-pass CO conversion for one-step dimethyl ether (DME) synthesis from syngas (3CO + 3H2 → DME + CO2). In the TGR, the catalyst bed was divided into 5 zones in series, and the temperature of each zone was optimized. Experiments were designed using an orthogonal array, and the experimental result was used for training the ANN to correlate the temperature setting and CO conversion. A grid search on the trained ANN was applied to find the optimum temperature setting. TGR was effective in overcoming both the equilibrium limit of the reaction at high temperature and the low activity of the catalyst at low temperature. To attain high CO conversion, Cu−Zn−Al−Ti−Nb−V−Cr catalysts with the optimized composition for each reaction temperature and γ-alumina were packed into the 5 zones of the TGR. As a result, a high one-pass conversion of CO at 82% was attained at 1 MPa, W/F = 50 g-cat·h/mol by means of the combination of the optimum catalyst and TGR. The CO conversion is much higher in comparison to the 72% found in TGR with a standard Cu catalyst, and to 69.5% in the isothermal reactor at 523K with a standard Cu catalyst. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8008633