基于人工神经网络的中速磨存煤量软测量方法  

Artificial Neural Network Based Soft-Sensing for Quantity of Reserving Coal in Medium Speed Mill

作　　者：张自成[1] 费敏锐[1] 

机构地区：[1]上海大学机电工程与自动化学院,上海200072

出　　处：《自动化仪表》2006年第z1期59-62,共4页Process Automation Instrumentation

摘　　要：中速磨直吹式制粉系统是火力发电厂锅炉运行稳定的一个重要环节,其磨机内存煤量能否获得可靠监测对发电厂的自动控制、经济运行和安全生产均有十分重要的意义。通过基于工艺机理知识,分析了中速磨直吹式制粉系统筒内存煤量的各相关因素,提出了利用人工神经网络进行存煤量软测量的方法,给出了基于BP改进算法的回归神经网络与延时神经网络综合模型,为中速磨存煤量的检测提供了新方法。离线训练与计算机仿真结果证实了软测量方法的可行性。Powdered system of straight blow medium speed mills is one of the important sections in fossil-fired power plants.Reliable monitoring for the quantity of reserving coal in the mills is significant to automation control,economical operation and safe production.Based on the knowledge of technique mechanism,various related factors to the quantity of reserving coal are analyzed.The method of soft-sensing by using artificial neural network is proposed and the integrated model of regressive neural network and time delay neural network based on BP improved algorithm are given.This provided a new method for detecting the reserving coal for medium speed mills.Through off-line training and computerized simulation,the results prove that the soft-sensing method is feasible.

关 键 词：中速磨煤机 存煤量 软测量 改进算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]