竖炉焙烧过程多变量智能优化设定方法  

Multivariable Intelligent Optimizing Setting Method for Shaft Furnace Roasting Process

作　　者：严爱军[1] 柴天佑[2] 王普[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100022 [2]东北大学自动化研究中心,辽宁沈阳110004

出　　处：《系统仿真学报》2008年第8期2044-2047,共4页Journal of System Simulation

基　　金：国家重点基础研究发展规划资助项目(2002CB312201);北京工业大学博士科研启动基金资助项目(52002017200701)

摘　　要：赤铁矿竖炉焙烧过程关键被控变量的优化设定值不易获得,使得生产指标难以控制在其目标值范围内。将案例推理、参量预报和专家系统技术相结合,提出一种竖炉焙烧过程的多变量智能优化设定方法。预设定模型给出控制回路的预设定值,案例评价模型和案例修正模型分别对预设定值进行评价和校正,从而实现了控制回路设定值的在线自动调整。实验及应用表明了方法的有效性,能够适应工况的频繁变化,实现了生产指标的优化控制,取得明显成效。The optimizing setting values of the key controlled variables are difficult to find in the shaft furnace roasting process for the hematite ores, which results in difficulty to control production indices within the target ranges. A multivariable intelligent optimizing setting method was developed by the combination of case-based reasoning （CBR）, variables prediction and expert system （ES）. First a pre-setting model presented the pre-setting points for the control loops. Next, the pre-setting points were evaluated and adjusted respectively by a case evaluating model and a case revising model. Thereby the set points of control loops of process were automatically adjusted online. The industrial application has proved that it can adapt the frequently changed working conditions while fulfilling the optimal control for the production indices. Prominent effects have been observed by the proposed method.

关 键 词：优化设定 案例推理 专家规则 指标预报 竖炉焙烧 生产指标 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]