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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

Artificial neural network (ANN)-aided optimization of ZSM-5 catalyst for the dimethyl ether to olefin (DTO) reaction from neat dimethyl ether (DME) / Kohji Omata in Industrial & engineering chemistry research, Vol. 48 N° 13 (Juillet 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6256–6261 Titre : Artificial neural network (ANN)-aided optimization of ZSM-5 catalyst for the dimethyl ether to olefin (DTO) reaction from neat dimethyl ether (DME) Type de document : texte imprimé Auteurs : Kohji Omata, Auteur ; Yuichiro Yamazaki, Auteur ; Yuhsuke Watanabe, Auteur Année de publication : 2009 Article en page(s) : pp. 6256–6261 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : ZSM-5  catalyst  Dimethyl  ether  Olefin Résumé : A ZSM-5 catalyst for light olefin synthesis from dimethyl ether (the reaction of dimethyl ether (DME) to olefin, abbreviated as DTO) was developed. In comparison with the reaction of DTO from diluted dimethyl ether (abbreviated as Diluted-DTO), lower light olefin selectivity and shorter catalyst life are the drawbacks of the reaction of DTO from neat (90 vol %) DME (abbreviated as Neat-DTO). After the effects of Si/Al ratio of zeolite, DME concentration, gas hourly space velocity (GHSV) of gas feed, and Si/Al ratio on the activity of calcium-incorporated ZSM-5 were examined, additives were screened using the physicochemical properties of the additive elements and an artificial neural network. The addition of boron or phosphorus to ZSM-5 improved the catalyst life. The catalyst composition such as Si/Al, Si/Ca, Si/P, and Si/B was then optimized for longer catalyst life, using an L9 orthogonal array and an artificial neural network (ANN). Grid search was applied to find the maximum catalyst life. The catalyst life of H−Ca-ZSM-5 (Si/Al = 250, Si/Ca = 20, Si/P = 400, Si/B = 200) was 146 h when Neat-DTO was performed at 803 K and GHSV = 1000 h−1. The life is comparable to that of the catalyst supplied to Diluted-DTO. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801757p [article] Artificial neural network (ANN)-aided optimization of ZSM-5 catalyst for the dimethyl ether to olefin (DTO) reaction from neat dimethyl ether (DME) [texte imprimé] / Kohji Omata, Auteur ; Yuichiro Yamazaki, Auteur ; Yuhsuke Watanabe, Auteur . - 2009 . - pp. 6256–6261. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6256–6261 Mots-clés : ZSM-5  catalyst  Dimethyl  ether  Olefin Résumé : A ZSM-5 catalyst for light olefin synthesis from dimethyl ether (the reaction of dimethyl ether (DME) to olefin, abbreviated as DTO) was developed. In comparison with the reaction of DTO from diluted dimethyl ether (abbreviated as Diluted-DTO), lower light olefin selectivity and shorter catalyst life are the drawbacks of the reaction of DTO from neat (90 vol %) DME (abbreviated as Neat-DTO). After the effects of Si/Al ratio of zeolite, DME concentration, gas hourly space velocity (GHSV) of gas feed, and Si/Al ratio on the activity of calcium-incorporated ZSM-5 were examined, additives were screened using the physicochemical properties of the additive elements and an artificial neural network. The addition of boron or phosphorus to ZSM-5 improved the catalyst life. The catalyst composition such as Si/Al, Si/Ca, Si/P, and Si/B was then optimized for longer catalyst life, using an L9 orthogonal array and an artificial neural network (ANN). Grid search was applied to find the maximum catalyst life. The catalyst life of H−Ca-ZSM-5 (Si/Al = 250, Si/Ca = 20, Si/P = 400, Si/B = 200) was 146 h when Neat-DTO was performed at 803 K and GHSV = 1000 h−1. The life is comparable to that of the catalyst supplied to Diluted-DTO. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801757p

Screening of additives to a Co/SrCO3 catalyst by artificial neural network for preferential oxidation of CO in excess H2 / Kobayashi, Yasukazu in Industrial & engineering chemistry research, Vol. 49 N° 4 (Fevrier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 4 (Fevrier 2010) . - pp 1541–1549 Titre : Screening of additives to a Co/SrCO3 catalyst by artificial neural network for preferential oxidation of CO in excess H2 Type de document : texte imprimé Auteurs : Kobayashi, Yasukazu, Auteur ; Kohji Omata, Auteur ; Yamada, Muneyoshi, Auteur Année de publication : 2010 Article en page(s) : pp 1541–1549 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Catalysts  Co/SrCO3  Cobalt  carbonate. Résumé : Preferential oxidation (PROX) of CO in excess hydrogen was investigated over cobalt catalysts supported on SrCO3, which showed a high performance. From the results of X-ray diffraction, X-ray photoelectron spectra (XPS), and temperature-programmed desorption, it was concluded that the active species for the PROX of CO is not cobalt oxide but cobalt carbonate-like compound, which was formed in the catalyst preparation step from Co(NO3)2 precursor and SrCO3 support. On the basis of the multivariate analysis, characters by XPS analysis are the main factors to determine the CO conversion with Co/SrCO3 catalyst. The selectivity for CO oxidation was suppressed by the side reactions, such as H2 oxidation and reverse water-gas shift reaction. Therefore, new additive to the Co/SrCO3 catalyst for the retardation of the side reactions was investigated by using an artificial neural network (ANN). The activities of 17 mol % Co + 1.7 mol % X/SrCO3 (X = B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re and Tl) and 16 physicochemical properties of those 10 elements were used as training data of ANN, and the most optimal additive among 53 elements in periodic table was predicted by ANN. From the result of the prediction and experimental verification, boron addition to the catalyst was effective to increase the activity for CO oxidation at the reaction temperature range from 200 to 240 °C. Actually, 17 mol % Co + 5.1 mol % B/SrCO3 catalyst showed 99% CO conversion with 52% selectivity at 200 °C with a feed composition of 0.69 vol % CO, 0.69 vol % O2, 4.5 vol % N2, 10 vol % H2 O, 17 vol % CO2 and H2 as a balance at a space velocity of 3 g·h/mol, and its activity was stable for 50 h. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901435h [article] Screening of additives to a Co/SrCO3 catalyst by artificial neural network for preferential oxidation of CO in excess H2 [texte imprimé] / Kobayashi, Yasukazu, Auteur ; Kohji Omata, Auteur ; Yamada, Muneyoshi, Auteur . - 2010 . - pp 1541–1549. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 4 (Fevrier 2010) . - pp 1541–1549 Mots-clés : Catalysts  Co/SrCO3  Cobalt  carbonate. Résumé : Preferential oxidation (PROX) of CO in excess hydrogen was investigated over cobalt catalysts supported on SrCO3, which showed a high performance. From the results of X-ray diffraction, X-ray photoelectron spectra (XPS), and temperature-programmed desorption, it was concluded that the active species for the PROX of CO is not cobalt oxide but cobalt carbonate-like compound, which was formed in the catalyst preparation step from Co(NO3)2 precursor and SrCO3 support. On the basis of the multivariate analysis, characters by XPS analysis are the main factors to determine the CO conversion with Co/SrCO3 catalyst. The selectivity for CO oxidation was suppressed by the side reactions, such as H2 oxidation and reverse water-gas shift reaction. Therefore, new additive to the Co/SrCO3 catalyst for the retardation of the side reactions was investigated by using an artificial neural network (ANN). The activities of 17 mol % Co + 1.7 mol % X/SrCO3 (X = B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re and Tl) and 16 physicochemical properties of those 10 elements were used as training data of ANN, and the most optimal additive among 53 elements in periodic table was predicted by ANN. From the result of the prediction and experimental verification, boron addition to the catalyst was effective to increase the activity for CO oxidation at the reaction temperature range from 200 to 240 °C. Actually, 17 mol % Co + 5.1 mol % B/SrCO3 catalyst showed 99% CO conversion with 52% selectivity at 200 °C with a feed composition of 0.69 vol % CO, 0.69 vol % O2, 4.5 vol % N2, 10 vol % H2 O, 17 vol % CO2 and H2 as a balance at a space velocity of 3 g·h/mol, and its activity was stable for 50 h. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901435h

Screening of new additives of active - carbon - supported heteropoly acid catalyst for friedel - crafts reaction by gaussian process regression / Kohji Omata in Industrial & engineering chemistry research, Vol. 50 N° 19 (Octobre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 10948-10954 Titre : Screening of new additives of active - carbon - supported heteropoly acid catalyst for friedel - crafts reaction by gaussian process regression Type de document : texte imprimé Auteurs : Kohji Omata, Auteur Année de publication : 2011 Article en page(s) : pp. 10948-10954 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Friedel  Crafts  reaction  Acid  catalysis  Additive Résumé : Activity ofheteropoly acid catalyst supported on active carbon for Friedel―Crafts reaction was predicted by Gaussian process regression (GPR), using the five main principal components of physicochemical properties of elements of the additives. Effective additives that promote the activity were predicted by the regression model and verified by experiments. The performance and accuracy of the regression model was increased using the expected improvement, which can suggest the additional experiments necessary for the improvement of the regression model. The performance of the regression model by GPR was superior to that of the radial basis function network (RBFN) or the support vector machine (SVM). In addition to the results by RBFN and SVM, an excellent effect of Pt addition was discovered by GPR. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573289 [article] Screening of new additives of active - carbon - supported heteropoly acid catalyst for friedel - crafts reaction by gaussian process regression [texte imprimé] / Kohji Omata, Auteur . - 2011 . - pp. 10948-10954. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 10948-10954 Mots-clés : Friedel  Crafts  reaction  Acid  catalysis  Additive Résumé : Activity ofheteropoly acid catalyst supported on active carbon for Friedel―Crafts reaction was predicted by Gaussian process regression (GPR), using the five main principal components of physicochemical properties of elements of the additives. Effective additives that promote the activity were predicted by the regression model and verified by experiments. The performance and accuracy of the regression model was increased using the expected improvement, which can suggest the additional experiments necessary for the improvement of the regression model. The performance of the regression model by GPR was superior to that of the radial basis function network (RBFN) or the support vector machine (SVM). In addition to the results by RBFN and SVM, an excellent effect of Pt addition was discovered by GPR. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573289