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Dimethyl ether (DME) from natural gas or coal via syngas (3CO + 3H 2 f DME + CO 2 ) attracts much attention as a high quality diesel fuel of the next generation. Considering the compact process based on the small-scale carbon resources, both low-pressure operation (at 1-3 MPa) and high one-pass conversion (90% CO conversion) are required to produce DME economically. A temperature gradient reactor (TGR) was effective for overcoming both the equilibrium limit of the reaction at high temperature and the low activity of the catalyst at low temperature. For example, 90% CO conversion and high STY (1.1 kg-MeOH eq./kg-cat./h) was attained at the same time in TGR at 280-240 °C, 3 MPa. For higher performance of the TGR, in the next step, optimization of the temperature gradient was required. A homemade program according to a genetic algorithm (GA) was used for the optimization. The catalyst bed was divided into 5 zones in series. The temperature of each zone was encoded to "gene" and the fitness of the "gene" was evaluated by CO conversion obtained in the reactor of which temperatures were set according to the gene. After a few generations of "evolution", CO conversion (70%) higher than that in a conventional isothermal reactor (66%) was achieved at 1 MPa. For higher CO conversion, neural network, trained by the results in the preceding generations, was used to evaluate the "gene". By this combination of GA and neural network, evolution of the temperature profile was accelerated and successfully optimized to give 71% CO conversion.

Optimization of a Cu−Zn−Al−Sc oxide catalyst for methanol synthesis from syngas was performed by a combinatorial approach using a genetic algorithm (GA) with/without various neural networks (NNs). Performance of optimum catalysts found by the various methods was compared. The catalyst with maximum activity was found by the combination of GA and radial basis function network (RBFN) in the optimization of Cu−Zn−Al−Sc oxide catalyst composition. Therefore this method was found to be the most efficacious method. In addition, we conducted the optimization on the RBFN with a larger population in the GA program to find the best catalyst in the early stage of evolution. On the other hand, we also tried to optimize simultaneously both composition and calcination temperature of a Cu−Zn oxide catalyst. In that case, the optimum catalyst was found by the combination of GA and back-propagation network. Thus, GA is a more robust tool when it is combined with NNs.

ABSTRACT-In 1998 and 1999, severe episodes of mortality, often reaching 90%, were recorded among cultured populations of ayu Plecoglossus altivelis reared in Japan. The diseased fish showed appetite reduction and abnormal swimming behavior. Histopathological examination revealed proliferative branchitis with enlarged and atypical epithelial cells. Abundant electron-dense, virus-like particles were observed within the cells under transmission electron microscopy. The particles had a cocoon-like shape and ranged in length from 200 to 300 nm, indicating a member of the poxvirus group. These findings suggest the possibility that the mortality events are related to infection of a poxvirus-like virus.

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