基于苯乙烯过程生产数据驱动的软测量模型研究

Soft Sensor Model Based on the Real-Time Data of the Styrene Process

针对苯乙烯生产过程的特点,引入软测量技术在线预测苯乙烯生产过程的一些关键参数,介绍了人工智能BP神经网络和部分最小二乘方法的软测量建模方法,基于企业生产数据研究了乙苯脱氢转化率、第一脱氢反应器脱氢转化率、第二脱氢反应器脱氢转化率和苯乙烯选择性等关键变量的软测量方法,对比了BP神经网络和部分最小二乘方法建模优缺点,应用结果表明,基于BP神经网络所建立的关键参数的软测量模型可真实再现实际苯乙烯生产过程,为安全可靠监控苯乙烯生产过程及未来实施先进及优化控制技术奠定了基础。

Aiming at the features of styrene,soft sensor based on the real-time data was applied to predict some key process variables.Two methods,BP neural network and partial least squares,were used to model the unmeasured process variables.At the same time,real-time process data were used to train the model in order to make sure the constructed model endure a good fault-tolerance.The experiments for total conversion,first reactor conversion,second rector conversion of ethylbenzene and selectivity of styrene were given based on BP neural networks and PLS,which showed the proposed methods could predict the dynamic performance of some key variables.It is very useful to monitor the important variables for the advanced control and optimization of styrene production.