DMS中人工神经网络的在线训练法

基于ANN(artificialneuralnetwork)的分馏塔侧线质量指标动态在线检测系统 ,简称DMS (dynamicon -linemonitoringsystem) ,成功地应用于炼油厂粗汽油干点、柴油凝点、Reid蒸汽压、闪点、冰点等的实时、在线、动态检测 ,达到很高的预测精度 ,极大地改善了分馏塔的控制性能 ,实现了分馏塔的操作优化 .本文提出一种新的网络训练方法———在线训练法 ,提高了在线检测系统的预测精度 .实际应用表明 ,这种方法可以有效克服人工神经网络离线训练的不足 ,提高动态在线检测系统的可靠性 ,并且省时省力

ON-LINE TRAINING METHOD OF ANN IN DMS

The dynamic on-line monitoring system (DMS) based on on-line training of ANN has been implemented successfully in a refinery for real-time,on-line and dynamic estimation of quality indexes of fractionator side draw,such as end point of raw-gasoline,pour point of diesel-oil,Reid vapor pressure,flash point,freezing point.It improves the control performance and optimizes the operation of the fractionator.To improve the predicted accuracy of DMS and fit the new production cases,a novel ANN training method, on-line training method,has been proposed in this article.This method overcomes the disadvantages of off-line training of ANN effectively.It can save lots of time and effort in contrast to the conventional off-line training of ANN.It improves the performance of DMS.This method includes three main steps:judge when ANN needs to be trained again;get new training patterns on-line;train ANN on-line.Steady state determination of the process is a key procedure in on-line training.A method based on the mathematical theory of evidence is used in steady state determination.This improves the training results of ANN and the reliability of DMS.