Artificial Neural Network-Aided Catalyst Research for Low-Pressure DME Synthesis from Syngas

Yamazaki

Watanabe

et al. 2009

Self Cite

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 L 9 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.

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network (ANN)-Aided Optimization of ZSM-5 Catalyst for the Dimethyl Ether to Olefin (DTO) Reaction from Neat Dimethyl Ether (DME)

Yamazaki

Watanabe

et al. 2009

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“…(i) methanol carbonylation with Ni-(Cu, Zr, V, Ag, Pb, Sb, Bi, Zn, Tl, K, Mg)/active carbon catalyst; 38 (ii) methanol synthesis with Cu-Zn-(Li, B, Mg, Al, Ca, Sc, Ti, Cr, Mn, Ga, Y, Zr, La, Ce, Pr, Nd, Sm, Gd, Dy, Ho, Er, Yb); 39 (iii) DME synthesis with Cu-Zn-(B, K, Nb, Re, Cd, Ce, Sm, Tl); 40 (iv) CO PROX reaction with Co-(B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re, and Tl)/SrCO 3 ; 29 (v) oxidative reforming of methane with Ni-(Ca, B, Mn, Fe, P, Cd, Re, Ce, Gd)/R-Al 2 O 3 . 41 For good prediction, the training data should be dispersed in the periodic table.…”

Section: Resultsmentioning

confidence: 99%

Screening of Additives to a Co/SrCO₃ Catalyst by Artificial Neural Network for Preferential Oxidation of CO in Excess H₂

Kobayashi

Yamada

2010

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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.

“…Methanol synthesis from syngas at high temperature was studied, and Ti, Nb, V, and Cr were suggested as effective additives for mitigating the deactivation of a Cu/Zn oxide catalyst. The composition of Cu/Zn/Al/Ti/Nb/V/Cr oxide catalyst was then optimized at 1 MPa and at 548, 530, 519, 514, and 509 K, respectively . The result is summarized in Table .…”

Section: Resultsmentioning

confidence: 99%

“…The composition of Cu/Zn/Al/Ti/Nb/V/Cr oxide catalyst was then optimized at 1 MPa and at 548, 530, 519, 514, and 509 K, respectively. 20 The result is summarized in Table 5.…”

Section: First Training Of Rbfn For the Optimization Of Temperature S...mentioning

confidence: 99%

Artificial Neural Network and Grid Search Aided Optimization of Temperature Profile of Temperature Gradient Reactor for Dimethyl Ether Synthesis from Syngas

Hashimoto

Sutarto³

et al. 2008

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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 + 3H 2 f DME + CO 2 ). 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.