基于SPA-SVR模型的LIBS铁精矿矿浆中铁品位的在线测量

Online measurement of iron grade in iron concentrate slurry by LIBS based on SPA‐SVR model

浮选是选矿过程中的重要一步,浮选过程中矿浆品位是选矿工艺需要实时掌握的重要指标。实验室开发了基于激光诱导击穿光谱的在线矿浆成分分析仪SIA-LIBSlurry,可以通过采集光谱数据对浮选过程中矿浆的各元素含量进行实时测量。但铁矿浆光谱数据维度较高,数据间强烈的多重共线性和非线性问题增加了建模的复杂度。为了解决该问题,本工作比较了两种变量选择算法:竞争性自适应重加权算法(CARS)和连续投影算法(SPA),并结合支持向量机回归(SVR)建立定量分析模型。研究结果表明:全谱6116个变量建立的SVR模型精度较低,预测均方根误差为1.45%;CARS筛选出的231个变量建立的CARS-SVR模型的预测能力有所提高,预测均方根误差为1.09%;SPA筛选出的12个变量建模,SPA-SVR模型取得了最佳预测效果,预测均方根误差降到了0.97%。说明SPA-SVR模型具有较高的预测准确率,有助于提高SIA-LIBSlurry分析仪在线监测的准确性。

Flotation is an important step in the ore dressing process,and the slurry grade during the flotation process is an important indicator that needs to be grasped in real-time in the ore dressing process.The authors'laboratory has developed an online slurry composition analyzer based on laserinduced breakdown spectroscopy(LIBS),SIA-LIBSlurry,which can measure the content of each element in the slurry during the flotation process in real time by collecting spectral data.However,the spectral data of iron ore slurry are of high dimensionality,and the strong multiple covariance and nonlinearity between the data increase the complexity of modeling.To address this issue,two variable selection algorithms are compared:the competitive adaptive reweighting algorithm(CARS)and the successive projection algorithm(SPA),and then the two algorithms with support vector machine regression(SVR)are combined to establish a quantitative analysis model.The results show that the SVR model built with the full spectrum of 6116 variables has low accuracy,with a root mean square error of prediction of 1.45%;the CARS-SVR model built with the 231 variables screened by CARS had improved predictive ability,with a root mean square error of prediction of 1.09%;and the best prediction is achieved by the SPA-SVR,a model built with the 12 variables screened by SPA,with a root mean square error of prediction down to 0.97%.Therefore,it is indicated that the SPA-SVR model has a high prediction accuracy,which helps to improve the accuracy of online monitoring of the SIA-LIBSlurry analyzer.