强磁选过程优化运行的智能设定控制方法

赤铁矿强磁选优化运行过程中,工艺指标精矿与尾矿品位难以在线实时检测,而且这两个工艺指标与底层回路控制器的漂洗水流量、励磁电流和给矿浓度等关键过程变量之间存在强非线性、强耦合、时变等动态特性,很难建立精确的数学模型,因此常规的基于数学模型的控制方法难以应用.针对上述问题,本文融合数据与知识的方法,提出了一种智能设定控制方法,其中包括基于案例推理的回路预设定模型、基于主元分析与RBF神经网络的品位软测量模型以及基于规则推理的动态补偿模型.该方法通过响应边界条件的变化,自动在线调整适宜工作点.工业试验表明该方法可有效提高精矿品位,降低尾矿品位.

Hybrid Intelligent Setting Control for Optimal Operation of Intensity Magnetic Separation Process

In the operation of intensity magnetic separation process(IMSP),it’s difficult to use any accurate mathematical models to describe the dynamic characteristics such as the strong nonlinearity,severe coupling and time variability between the technical indices,namely the concentrate grade and the tailing grade,and the key controlling variables,i.e.the exciting current,the rinsing water flow and the feed density.Moreover,these two indices cannot be measured continuously.So,the conventional model-based control approach can’t be used here.Focusing on this practical challenge,this paper proposes an intelligent setting control approach that consists of a case-based reasoning(CBR) control loop pre-set module,a soft-sensor module based on principal component analysis(PCA) and radial basis function neural network(RBFNN),and an rule-based reasoning dynamic compensator.This intelligent system can automatically adjust the operating points for the IMSP in response to the changes in boundary conditions online.Industrial tests show that the approach proposed can improve the concentrate grade,while reduce the tailing grade.