Grant-in-Aid for Scientific Research (B)
|Allocation Type||Single-year Grants|
|Research Institution||Nagoya University|
HATTORI Tadashi Nagoya University, Deparment of Applied Chemistry, Professor, 工学部, 教授 (50023172)
YOSHIDA Hisao Nagoya University, Department of Applied Chemistry, Research Associate, 工学部, 助手 (80273267)
SATSUMA Atsushi Nagoya University, Department of Applied Chemistry, Associate Professor, 工学部, 講師 (00215758)
KITO Shigeharu Aichi Institute of Technology, Department of Industrial Engineering, Professor, 工学部, 教授 (20023343)
|Project Period (FY)
1994 – 1996
Completed(Fiscal Year 1996)
|Budget Amount *help
¥4,000,000 (Direct Cost : ¥4,000,000)
Fiscal Year 1996 : ¥400,000 (Direct Cost : ¥400,000)
Fiscal Year 1995 : ¥1,300,000 (Direct Cost : ¥1,300,000)
Fiscal Year 1994 : ¥2,300,000 (Direct Cost : ¥2,300,000)
|Keywords||Neural Network / Prediction / Catalytic Activity / Selectivity / Catalyst Design / Computer / Reduction of NO. / Zeolite / ニューラルネットワーク / ゼオライト / アルカン設計 / 窒素酸化物 / 還元 / コンピュータ / 触媒 / 性能予測 / 選択的還元|
The present research aims at examining the possibility of neural network system to predict optimum catalyst extracting knowledge required for catalyst design from various experimental results, and the following resuts were obtained.
(1) Prediction of Catalytic Performance by Neural Network
Neural network was applied for the prediction of catalytic activity and selectivity by taking as an example the oxidative dehydrogenation of ethylbenzene on 19 promoted tin oxide catalysts. The predicted activity and selectivities of five products were in good agreement with those measured experimentally within reasonable experimental error.
The possibility of extrapolative prediction was examined by taking as an example the oxidation of alkanes on a series of lanthanide oxides, in which catalytic performance is known to be a function of fourth ionization potential of lanthanide ions. In butane oxidation, where a monotonous correlation had been empirically established between the catalytic activity and
the fourth ionization potential, the neural network well predicted the activities in both ends of the correlation as well as those in between. The neural network predicted, in methane oxidation, even volcano type changes of the activity and the selectivities of C_2 hydrocarbons, CO and CO_2. These results indicate that the neural network can be applied to both of the interpolative prediction for optimization of catalysts and the extrapolative prediction, at least for improvement of catalysts.
(2) Estimation of Factors Controlling Catalytic Performance by Neural Network
In the prediction of catalytic activities of lanthanide oxides in butane oxidation, it was found that the fourth ionization potential is a key controlling factor, because the prediction accuracy was high only when input data include ionization potential. These results suggest that a leave-one-out test of input data would be effective for the selection of controlling factors : The importance of each input data could be estimated by leaving one of the input data out of the training set.
On the basis of these results, some remarks were given on the estimation of the key factor controlling catalytic performance by neural network and on the catalyst development by using neural network.
(3) Catalytic Experiments in Selective Reduction of Nitrogen Oxide
As an example of novel catalytic reaction, the reduction of nitrogen oxide with propylene and methane was conducted by using ion-exchanged zeolite catalysts and oxide catalysts, and the preliminary test was conducted to apply the neural network system for the catalyst design of reduction of nitrogen oxide. Less