基于案例推理的浮选过程智能优化设定

Intelligently Optimal Index Setting for Flotation Process by CBR

针对在浮选过程中表征关键工艺指标的精矿品位和尾矿品位难以建立精确数学模型,而常规控制方法又难以进行有效控制的难题,将案例推理和常规控制相结合,提出了基于案例推理技术的浮选过程工艺指标优化设定方法.该方法从浮选过程积累的大量过程历史数据中获取过程知识,将典型生产工况总结成案例的形式来构造案例库,并对案例检索、案例重用、案例修正及存储进行了论述.由智能优化设定模型自动更新基础控制回路的设定值,避免了人工手动设定的主观性和随意性.该方法应用于某选矿厂浮选过程,取得了显著的应用效果,表明了该方法的有效性.

In the flotation process it is hard to develop an accurate mathematical model for both concentrate grade and tailing grade which characterize the key technical indices. However, it is also hard to control efficiently the process by conventional methods. A new way is therefore recommended combining the case-based reasoning （CBR） with conventional control methods, i.e. the intelligently optimal index setting by CBR. In this way the experience of flotation process can be grasped more profoundly from lots of historical process data so as to build a case database which summaries typical operation conditions. The retrieval, reuse, revision and store of those cases are discussed in CBR. The intelligently optimal index setting model can update automatically all setpoints in every control loop so as to avoid the subjectivity and randomness due to arbitrary manual control setting. This approach has been successfully applied to a flotation process in a mineral processing plant with its effectiveness actually proved.