基于神经网络辨识的灰色预测在精馏塔中的应用

Application of Grey AR Prediction Based on Neural-Network-Identification in Distillation Column

石油化工生产中常用精馏塔的控制是一种滞后时间长、滞后常数不定的典型不确定滞后对象。这种对象控制困难,系统精度要求高,要对其进行有效控制就必须高精度预测它的输出,因为对象的不确定滞后特性,对其进行精确的输出预测始终是一个难题。针对精馏塔输出预测上的困难,提出利用神经网络首先辨识系统的滞后时间,之后在此基础上采用AR(p)(自回归)模型拟合残差的改进型灰色预测方法预测输出,基本灰色预测模型采用变步长单步灰色预测。将上述方法应用在精馏塔模型输出预测中,仿真结果表明改进后的预测方法对具有滞后、时变的系统有良好的预测效果,而且对系统参数突变、漂移等非失效型故障有一定的鲁棒性、容错性,比其他灰色预测方法更具优越性。

The controling of distillation column used in petrochemical industry is a typical uncertain systems with long - time delay and unstable constant of delay. The requested precision for system of this kind is very high. To control the system effectively, high precision of prediction is necessary. It is a problem to forecast exactly the output of system for its uncertain time delay characteristic, In order to overcome the difficulty in modeling, neural network was proposed to identify the delay time of such a system firstly. Moreover, the advanced grey prediction algorithm in which error was fitted with AR（p） modle was used to predict future behaviors of uncertain systems with time - delay. The based grey prediction model is one - mutative - step grey prediction. In computer simulation of distillation column＇ s output prediction, the results show that the way introduced has higher prediction prevision and better application than other models to time - delay and uncertain systems, it aiso has robustness and fault - tolerant towards uninvalidation troubles as parameters jump and excursion fault, and the method introduced is better than other grey prediction ways,