煤中激光诱导击穿光谱的碳元素定量分析

Quantitative analysis of carbon in coal based on laser-induced breakdown spectroscopy

在定量分析煤样品中碳元素含量时,为了克服受基体效应影响较大且预测精度低的问题,在最优实验条件下,获得14个标准煤样品经激光诱导击穿光谱(LIBS)试验后的光谱数据,并选取独立性好、不受相邻谱线干扰的CⅠ193.09nm波长,将积分强度作为输入变量,采用基本曲线定标法以及神经网络定标法,对煤样品进行定量分析。结果表明,当采用基本定标曲线法时,受噪声干扰以及基体效应的影响较大,平均相对误差为15.39%;当采用神经网络定标法时,验证样品的相对误差平均降低了7.54%;采用神经网络定标法能有效减小定量分析误差,提高LIBS对煤中碳元素含量的预测能力。该研究可为定量分析煤中碳元素含量提供指导。

In quantitative analysis of carbon content in coal samples,matrix effect has great influence and prediction accuracy is low.In order to solve this problem,under the optimum experimental conditions,spectral data of 14 standard coal samples after laser-induced breakdown spectroscopy(LIBS)test were obtained.CⅠ193.09nm wavelength with good independence and without interference from adjacent spectral lines was selected.Integral strength was taken as input variable.Basic curve calibration method and neural network calibration method were used to carry out quantitative analysis of coal samples.The results show that,when basic calibration curve method is used,it is greatly affected by noise interference and matrix effect.Average relative error is 15.39%.When neural network calibration method is used,relative error of the validated samples decreases by 7.54%on average.Neural network calibration method can effectively reduce the quantitative analysis error and improve the ability of LIBS to predict carbon content in coal.This study can provide guidance for quantitative analysis of carbon content in coal.