A hybrid catalyst consisting of Cu-Zn-X oxide and γ-alumina was prepared for single-stage dimethyl ether synthesis. An artificial neural network (ANN) was applied to find the effective additives for the hybrid catalyst. For the training of ANN, elements (X)sB, K, Nb, Re, Cd, Ce, Sm, and Tlswere selected. The activity change with time on stream of the hybrid catalyst was fitted to a generalized power law equation (GPLE). The resultant GPLE parameters and the physicochemical properties of the eight elements were used as training data for ANN. After the training, the trained ANN was used to predict the activity of the hybrid catalyst containing various X elements as Cu-Zn-X. Elements Al, Ti, V, and Nb were predicted as promising, and the composition of hepternary oxide catalyst was optimized by the combination of design of experiment, ANN, and grid search. The catalyst with the optimized composition showed stable and high activity.

The activity of Cu _ Zn oxide catalysts for methanol synthesis from syngas varies depending on the additives to the oxide, and optimum composition is sensitive to the reaction conditions. An artificial neural network (ANN) was applied to identify the most effective additives based on the experimental results already reported. The physicochemical characters of element X, such as ionic radii and ionization energy, and the activity of Cu _ Zn _ X oxide catalyst were correlated using the ANN. Twenty-two types of X were supplied for the training of the ANN, and 29 activities of Cu _ Zn _ X, the X of which was not included in the training data, were predicted. Beryllium was predicted as the most effective additive, which was verified experimentally.

1.HTS: high-throughput screening The high-throughput screening reactor for high pressure oxidative reforming of methane requires a new simple syngas detector operating under high pressure, because the number of parallel reactors with the conventional detection system is limited by the complexity of the pressure reducing unit. Reduction of metal oxide with color change was applied to the detection system. Copper oxide was supported on the fi lter made of alumina, and the fi lter was placed underneath the catalyst bed. After oxidative reforming of methane was carried out under 1 MPa at 650 C, color change of spots from dark brown to light brown was observed just under the catalyst caused by copper oxide reduction. The color change disk can be used to detect hydrogen formation ability of the reforming catalyst under pressure.

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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.