基于LIBS与GA-PLS的钢铁中Mn，Ni元素定量分析研究

钢材炼制过程锰、镍元素的含量均会对最终产品的硬度脆度产生影响，但由于其添加的含量需要进行严格控制，同时传统的钢铁成分检测的设备成本高、效率低、速度慢，因此需要一种高精度的快速实时分析方法。利用遗传偏最小二乘法（GA-PLS）结合LIBS技术对钢铁样品光谱中的Mn和Ni两种元素进行定量检测，并且与传统PLS的定量分析结果进行对比，以验证GA-PLS模型预测性能。采用购置于钢材市场的12个钢铁样品，其中9个样品的光谱信息作为校正集训练模型，3个作为测试集验证模型定量性能。GA-PLS通过不断提高变量被选频率的阈值，用不同阈值下的变量建立PLS模型，对比选出最低RMSECV时的阈值（Mn和Ni元素的光谱输入变量被选频率的最佳阈值分别为8和7）。结果显示：GA-PLS锰元素预测结果的R2_P和RMSE_P分别是0.999 0和1.347 3，相对分析误差（RPD）为2.5；镍元素预测结果的R2_P和RMSE_P分别是0.999 5和0.525 4，RPD为8.6，最终预测的结果优于PLS。该结果表明了GA-PLS算法在冶金金属元素分析领域具有可持续性挖掘的潜力，同时也将促进LIBS技术在钢铁冶炼领域更深层次的应用。

Quantitative Analysis of Mn and Ni Elements in Steel Based on LIBS and GA-PLS

The content of manganese and nickel in the steel refining process will affect the hardness and brittleness of the final product, but the added content needs to be strictly controlled. At the same time, the traditional steel composition detection equipment had a high cost, low efficiency and slow speed. Therefore, a high-precision, fast and real-time analysis method is needed. This article used genetic partial least squares (GA-PLS) combined with LIBS technology to quantitatively detect the two elements of Mn and Ni in the spectrum of steel samples and compared the results with the quantitative analysisof traditional PLS to verify the predictive performance of the GA-PLS model. This experiment used 12 steel samples purchased in the steel market, the spectral information of 9 samples was used as the calibration set training model, and the spectral information of 3 samples was used as the test set to verify the quantitative performance. GA-PLS continuously raised the threshold of the selected frequency of the variable, established the PLS model with the variables under different thresholds, and compared the threshold when the lowest RMSECV was selected (the optimal thresholds for the selected frequency of the spectral input variables of Mn and Ni were 8 and 7 respectively). The results of GA-PLS showed that the R2_P and RMSE_P of the GA-PLS manganese prediction results were 0.999 0 and 1.347 3, and the relative analysis error (RPD) was 2.5; the R2_P and RMSE_P of the nickel prediction results were 0.999 5 and 0.525 4, respectively, and the RPD was 8.6. The final predicted result was better than PLS. The results show that the GA-PLS algorithm has the potential for sustainable mining in metallurgical metal element analysis, and will also promote the deeper application of LIBS technology in the field of steel smelting.