摘要
烧结混合料水分控制是烧结矿生产过程中十分重要的环节,其主要存在大滞后、多变量干扰及数据波动等控制难点。本文通过分析烧结混合料加水混合工艺与特点,针对一、二次混合制粒不同的工况条件,提出多级前馈联合周期梯度寻优控制策略,同时结合当前配料成分与透气性指数等关键参数,采用BP神经网络模型预测适合当前烧结的最优水分率,并从连续生产的角度识别停机、开车与模式切换等运行状态,最终替代人工控制实现加水混合全过程自动化。仿真结果和工业应用表明,本文提出的控制方法的性能优于常规PID控制,系统投运后能显著提升烧结自动化水平。
The moisture control of sintering compound is one of the very important links in the sinter production process,and there are mainly such control difficulties as large lag,multivariable interference and data fluctuation.By analyzing the water mixing process and characteristics of sintering compound,a multi-stage feed-forward combined periodic gradient optimization control strategy is proposed according to the different working conditions of primary and secondary mixing granulation.Combined with the current ingredient composition and air permeability index and other key parameters,the BP neural network model is used to predict the optimal moisture rate suitable for the current sintering,and the operating states such as shutdown,start-up and mode switching are identified from the perspective of continuous production,and finally the whole process of water mixing is automated instead of manual control.Simulation results and industrial applications show that the performance of the control method proposed is better than that of conventional PID control,and the sintering automation level can be significantly improved after the system is put into operation.
作者
王瑞林
李自成
熊涛
肖高兴
张先玲
WANG Ruilin;LI Zicheng;XIONG Tao;XIAO Gaoxing;ZHANG Xianling(School of Electrical and Information Engineering,Wuhan Institute of Technology,Wuhan 430205,Hubei,China;Ironmaking Division of Nanjing Iron&Steel Co.,Ltd.,Nanjing 210044,Jiangsu,China)
出处
《烧结球团》
北大核心
2023年第4期38-45,共8页
Sintering and Pelletizing
基金
国家自然科学基金资助项目(41727801)。
关键词
烧结自动化
水分控制
多级前馈控制
周期梯度寻优
BP神经网络
sintering automation
moisture control
multi-level feed-forward control
periodic gradient optimization
BP neural network
