Abstract: The conversion rate of dehydrogenation of ethylbenzene to styrene is important for the decision-making in the management of the chemical plant. In this paper，we applied a modified radial basis function (RBF) neural networks that could endure better fault-tolerace for this problem. The modified RBF neural networks was tested by six case history data sets，i.e.，the feed of the ethylbenzene，the temperatures of the first reactor and second reactor，the output pressure of the second reactor，the ratio of steam to ethylbenzene，the selectivity of the catalyst. The experiment showed that this method could ensure high accuracy in predicting the conversion rate of the process.

摘要： 针对乙苯催化脱氢过程的特点，选用历史生产数据即乙苯进料、一反温度、二反温度、二反出口压力、水比、脱氢选择性，利用改进的径向基函数神经网络（RBFNN）来构建乙苯催化脱氢过程模型，通过企业实际生产数据对该网络进行测试，其结果表明该模型可真实模拟实际乙苯脱氢生产过程，为后续乙苯催化脱氢系统实施先进控制优化技术奠定了基础.