摘要
针对大型浮选设备对矿浆液面自动控制系统的高要求,设计了矿浆液面自动控制系统中的执行器,提出了一种符合现场复杂情况、基于神经元网络技术的软测量方法,在基于过程补余量算法中,引入利用不同形状的反馈凸轮片产生不同非线性来改变控制阀的原有流量特性,结合比例系数得到了具有线性流量特性的调节阀曲线函数的一种软测量模型建模和优化的过程。该设计方法经现场的实际使用验证效果良好。
Based on big flotation machine that is demanding for liquid level of automation control, the control valve of liquid level of automation control was designed, and a soft measurement technique using neural networks was proposed to accord with the complex site conditions. By using the characteristic that different form of feedback cam brought different nonlinear flow rate to change the former flow rate of control valve, a soft measurement modeling and optimizing process of control valve curve function that is of linear character of flow rate was introduced combining with proportional coefficient. The efficiency of this design has been verified by practice.
出处
《重庆邮电大学学报（自然科学版）》
北大核心
2009年第5期672-675,共4页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家高技术产业发展计划项目(发改办高技[2005]1899号)
国家高技术产业发展计划项目(甘发改高技[2005]291号)
甘肃政法学院科研资助重点项目(GZF2009XZDLW16)
关键词
软测量
神经网络模型
液面自动控制
执行器
流量特性
比例系数
soft measurement technique
neural network model
liquid level of automation control
control valve
character of flow rate
proportional coefficient
