应用Kohonen神经网络对ZSM-5型沸石催化甲苯与甲醇烷基化反应数据进行分析

J Petit , J Zupan , L Leherte , D.P Vercauteren
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引用次数: 4

摘要

对二甲苯在化学工业中有着广泛的应用。以ZSM-5沸石为催化剂,甲苯与甲醇进行烷基化反应。对二甲苯在其他异构体和其他反应副产物中的比例取决于反应条件。由于这一过程在很大程度上仍然是经验的,我们试图建立一个能够预测特定反应条件下对二甲苯产率的理论模型。因此,我们从文献中收集了有关该反应的数据,并利用了特定人工神经网络(ANN)的潜力,即基于Kohonen技术的反传播ANN。结果表明,该方法适用于建立基于反应参数的对二甲苯产率预测模型。通过考虑更大的有价值的数据集,例如包括以对二甲苯产率低为特征的实验,可以进一步提高模型的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a Kohonen neural network to the analysis of data regarding the alkylation of toluene with methanol catalyzed by ZSM-5 type zeolites

para-Xylene is widely used in chemical industry. It can be synthesized by alkylation of toluene with methanol using zeolite ZSM-5 as catalyst. The proportion of para-xylene, among its other isomers and other reaction byproducts, depends on the reaction conditions. As this process still remains largely empirical, we attempted to build a theoretical model able to predict the para-xylene yield under specific reaction conditions. We have consequently collected data regarding this reaction from the literature and exploited the potency of a particular artificial neural network (ANN), the counter-propagation ANN based on the Kohonen technique. The results show that such an approach is suitable to establish a predictive model of the yield in para-xylene on the basis of reaction parameters. The quality of the model could be further improved by considering a larger valuable data set, e.g. including experiments characterized by a low yield in para-xylene.

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