摘要
针对高碳铬铁电炉电极控制系统具有非线性、时变、模型不确定、滞后、多输入多输出耦合的特点,提出了一种基于神经网络预测技术检测电极电流并预测电极位置的方法,其预测结果是对PID控制器的输出进行调整,使系统较好地适应负载和外扰的变化,实现了高碳铬铁炉电极的智能控制,提高了电炉的热效率,节约了能耗。
Owing to non-linear,time-varying,model uncertainty,large lag,multi-input multi-output coupling characteristics of electrode control system of HC ferrochrome furnace,a method of detecting electrode current based on neural network prediction technology to predict the electrode position has been proposed,the prediction results adjust the PID controller＇s output,allowing the system to better adapt to load changes and external disturbance,the furnace electrode,intelligent control has been achieved,improving the thermal efficiency of the furnace,saving the energy consumption.
出处
《铁合金》
2011年第4期35-38,共4页
Ferro-alloys
基金
国家科技支撑计划项目(2007BAE17B02)
吉林省科技发展计划项目(2006042-1)
关键词
高碳铬铁
神经网络
预测
电流
电极控制
high carbon ferrochrome
neural network
prediction
current
electrode
