Chen Tong, Sun Lanxiang, Yu Haibin, Qi Lifeng, Zhang Peng, Dong Haiyan
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Analyst. 2024 Aug 19;149(17):4407-4417. doi: 10.1039/d4an00631c.
Real-time Fe content monitoring in iron ore slurry is crucial for evaluating concentrate quality and enhancing mineral processing efficiency. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the online monitoring of elemental content at industrial sites. However, LIBS measurements are hampered by the matrix effect and the self-absorption effect, limiting the precision of linear analytical processes. To overcome this, we propose to introduce a nonlinear processing unit based on the S-transform to incorporate nonlinearity into the data analysis process. This approach integrates a feature selection unit based on the spectral distance variable selection method (SDVS), a nonlinear processing unit based on the S-transform (ST), and a partial least squares regression model (PLS). To demonstrate the improvement in accuracy achieved through nonlinear processing, a comparative analysis involving five models, Raw-PLS, SDVS-PLS, ST-PLS, SDVS-ANN, and SDVS-ST-PLS, is conducted. The results reveal a significant improvement in the performance of the SDVS-ST-PLS model, effectively facilitating the successful application of the LIBSlurry analyzer to the mineral flotation process.
实时监测铁矿石矿浆中的铁含量对于评估精矿质量和提高选矿效率至关重要。激光诱导击穿光谱法（LIBS）是一种在工业现场在线监测元素含量的有前景的技术。然而，LIBS测量受到基体效应和自吸收效应的阻碍，限制了线性分析过程的精度。为克服这一问题，我们建议引入基于S变换的非线性处理单元，将非线性纳入数据分析过程。该方法集成了基于光谱距离变量选择法（SDVS）的特征选择单元、基于S变换（ST）的非线性处理单元和偏最小二乘回归模型（PLS）。为证明通过非线性处理实现的精度提升，对包括原始PLS、SDVS-PLS、ST-PLS、SDVS-ANN和SDVS-ST-PLS在内的五个模型进行了对比分析。结果表明，SDVS-ST-PLS模型的性能有显著提升，有效推动了LIBS矿浆分析仪在矿物浮选过程中的成功应用。
