Development of Neural Network System for Prediction of Catalytic Performance

催化性能预测神经网络系统的开发

基本信息

批准号：
06555242
负责人：
HATTORI Tadashi
金额：
$ 2.56万
依托单位：
Nagoya University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1996
项目状态：
已结题

项目摘要

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 NetworkNeural 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 … More 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 NetworkIn 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 OxideAs 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

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 rests were obtained.(1) Prediction of Catalytic Performance by Neural NetworkNeural network was applied for the prediction of catalytic activity and selectivity by taking as an example the oxide dehydrogenation of ethylbenzene on 19 promoted tin oxide catalysts.五种产物的预测活性和选择性与在合理的实验误差中实验测量的活性和选择性非常吻合。通过以示例为例，可以检查外外预测的可能性。烷烃在一系列型腺苷酸氧化物上的氧化，其中催化性能是六烷基离子离子电源的四个离子化电位的功能。在丁烷氧化中，在催化活性和……第四个电离潜力之间已经实证建立了单调相关性，神经元网络很好地预测了相关性的两端以及之间的活性。神经元网络在氧化甲烷中预测了活性的火山类型变化以及C_2碳氢化合物的选择性，CO和CO_2。 These results indicate that the neuronal 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 NetworkIn 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仅当输入数据包括电离电位时高。这些结果表明，对输入数据的一对一测试将有效地选择控制因素：可以通过将一个输入数据排除在培训设置的基础上来估算每个输入数据的重要性。在这些结果的基础上，对神经网络和使用神经网络进行催化的催化型的关键因素的估计，给出了一些提醒。新型催化反应的例子，通过使用离子交换的沸石催化剂和氧化催化剂进行丙烯和甲烷的氮还原，并进行了初步测试以将神经网络系统应用于减少氮氧化物的催化剂设计。较少的