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作 者:刘迎新[1,2] 未作君[3] 陈吉祥[1] 张继炎[1]
机构地区:[1]天津大学化工学院催化科学与工程系 [2]北京大学化学与分子工程学院应用化学系,北京100871 [3]北京大学环境学院环境科学系
出 处:《催化学报》2005年第1期20-24,共5页
摘 要:采用BP神经网络对间二硝基苯液相催化加氢制备间苯二胺的反应条件进行优化 .首先利用均匀设计的实验结果 ,采用LM算法对BP神经网络进行训练 .再利用训练好的BP神经网络对各种实验因素水平组合条件下间二硝基苯的转化率和间苯二胺的收率进行预测 .结果表明 ,采用苯作溶剂时 ,间二硝基苯的转化率较高 ;采用乙醇作溶剂时 ,间苯二胺的收率较高 ;当采用乙醇为溶剂 ,催化剂用量为 2 0 % ,反应温度和压力分别为 36 5K和 2 9MPa时反应效果较好 ,间苯二胺的收率高达 95 8% .进一步的实验验证表明 ,用神经网络模型模拟的结果与实验结果基本吻合 .Catalytic hydrogenation of m-dinitrobenzene in liquid phase is an attractive routine for the preparation of m-phenylenediamine. The reaction is restricted by different reaction parameters such as the solvent type, the reaction temperature, the hydrogen pressure and the catalyst amount. In order to obtain the best result, an artificial neural network was applied to optimize the reaction conditions. A BP (back propagation) neural network was trained by the experimental results of homogeneous design using Levenberg-Marquardt (LM) algorithm, and the training absolute error was less than 1 X 10(-5). The trained BP neural network was used to predict the conversion of m-dinitrobenzene and the yield of m-phenylenediamine under different reaction condition schemes. The results showed that higher conversion of m-dinitrobenzene was obtained when benzene was used as solvent, but higher yield of m-phenylenediamine was gained when ethanol was used as solvent. The most favorable reaction conditions were ethanol as solvent, reaction temperature of 365 K, hydrogen pressure of 2.9 MPa and catalyst amount of 20%. Under the above conditions, the yield of m-phenylenediamine was high up to 95.8%. The simulation results were verified by experiment, and they were in good agreement with the experimental data. Therefore, the BP neural network can be effectively used to optimize the reaction conditions for the hydrogenation of m-dinitrobenzene.
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