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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

本研究旨在探索神经网络系统从各种实验结果中提取催化剂设计所需的知识来预测最优催化剂的可能性，得到以下结果：(1)神经网络预测催化性能以19种助剂氧化锡催化剂上的乙苯氧化脱氢为例，应用神经网络预测催化活性和选择性。以烷烃在一系列稀土氧化物上的氧化反应为例，考察了预测的可能性，其催化性能与稀土离子的第四电离能有关。在丁烷氧化中，已经经验地在催化活性和…之间建立了单调关联在第四个电离势中，神经网络较好地预测了关联两端以及关联之间的活动。神经网络对甲烷氧化反应中C_2烃、CO和CO_2的活性和选择性的火山类型变化进行了预测。这些结果表明，神经网络既可以用于催化剂优化的内插预测，也可以用于外推预测，至少可以用于催化剂的改进。(2)神经网络对催化性能控制因素的估计在预测稀土氧化物在丁烷氧化中的催化活性时，发现第四电离势是一个关键的控制因素，因为只有当输入数据包括电离势时，预测精度才较高。这些结果表明，输入数据的留一检验对于控制因素的选择是有效的：每个输入数据的重要性可以通过从训练集中剔除一个输入数据来估计。在这些结果的基础上，给出了用神经网络估计控制催化性能的关键因素和用神经网络进行催化剂开发的一些意见。(3)氮氧化物选择还原催化实验作为一种新型催化反应的例子，用离子交换沸石催化剂和氧化物催化剂进行了丙烯和甲烷还原氮氧化物的反应，并对神经网络系统在氮氧化物还原催化剂设计中的应用进行了初步试验。较少