浮选工艺指标KPCA-ELM软测量模型及应用

精矿品位和尾矿品位是浮选过程重要的工艺技术指标,其难以实现在线检测,且与过程控制变量具有强非线性、不确定性等综合复杂特性,难以直接采用精确的数学模型描述,主要依靠人工化验分析。人工采样化验周期较长,难以满足控制要求,使得浮选精矿品位偏低,尾矿品位偏高,因此建立浮选品位指标的软测量方法受到工业界广泛关注。在分析浮选过程工艺指标相关影响因素的基础上,建立一种基于主元分析KPCA(kernel principal component analysis)和极限学习机ELM(extreme learning machine)的软测量模型。为了消除离群点对软测量模型精度的影响,采用基于稳健位置估计的方法识别离群点,利用核主元分析对软测量模型的输入数据进行降维,提取非线性主元,然后用极限学习机进行建模。该建模方法已成功应用于中国西北某选矿厂浮选车间,工业应用结果表明该方法有很高的预报精度,对生产有一定的指导意义。

Soft sensor of technical indices based on KPCA-ELM and application for flotation process

In the flotation process,the concentrate grade and the tailing grade are crucial technical indices which can not be measured online continuously.They can hardly be described using accurate mathematical model for strong nonlinearities and uncertainties among technical indices and operating variables,mainly measured off-line by artificial laboratory.The long cycle of artificial laboratory is difficult to meet the control requirement of grade indices,so study of grade indices soft measurement method attracts more attention.By analyzing the relations between the technical indices and such boundary variables,a soft sensor model of technical indices based kernel principal component analysis(KPCA)and extreme learning machine(ELM)was proposed innovatively to estimate the concentrate grade and the tailing grade.To solve the outliers,missing data points of the outliers and deviation from normal values are detected.KPCA is applied to compress the input data,and select the nonlinear principle component.ELM is used to process regression modeling.The proposed model is successfully applied to the flotation process of a hematite ore processing plant in China.Industrial application results show that the soft sensor model has high accuracy and guidance to real production.