Semi-coke, as a kind of special coal resource with a relatively high concentration of carbon and low volatility, plays an important role in the coal chemical industry and in creating clean cities. Laser-induced breakdown spectroscopy (LIBS) has been proved to be an effective way to conduct online analysis of coal products. However, the lower volatility of semi-coke makes it hard to press into a slice to obtain a smooth surface for uniform laser-irradiation. Therefore, it is necessary to find an effective way to realize high-accuracy LIBS detection for semi-coke applications. Herein, two feasible methods of sample preparation are attempted, one easy way involves directly painting semi-coke powder onto tape that is suitable for online fast monitoring, and the other more complicated way is to mix a binder into the semi-coke powder so that uniform and tight coal slices are obtained, to improve the repeatability of the measurements. Moreover, a totally new algorithm, a support vector machine (SVM) combined with partial least square (PLS) regression (SVM-PLS), was utilized to establish an effective prediction model to give a high predictive accuracy. The coefficient of determination (R²), root mean square error of prediction (RMSEP), and average relative error (ARE) are 0.944, 0.90%, and 0.80%, respectively. In comparison with the results from the traditional PLS model, SVM residual correction greatly improves the quality of the calibration curve and the RMSEP and ARE values are reduced to 0.17%, thus improving the prediction accuracy, which is much better than the basic PLS regression. Meanwhile, the prediction error from the binder mixed semi-coke slice is significantly reduced compared to that of the directly painted samples on tape. The maximum relative errors (MREs) are 2.71% and 5.19%, and the average RSD values of the characteristic peaks are 12.1% and 16.2%, respectively, indicating that the easy way of painting a sample on tape has some prediction uncertainties. Finally, in a three-day random test, the average RMSEP was found to be 1.89% and the average ARE was 1.74%, which also proves that the binder additive can effectively reduce the matrix effect and enhance the stability of the spectrum for semi-coke measurements. The results indicate that appropriate LIBS analysis on semi-coke is a feasible and promising approach for online predictions using this kind of coal sample.

中文翻译：

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激光诱导击穿光谱法高精度预测半焦碳含量

半焦作为一种特殊的煤炭资源，具有较高的碳含量和较低的挥发性，在煤炭化学工业和创建清洁城市中发挥着重要作用。激光诱导击穿光谱法（LIBS）已被证明是进行煤产品在线分析的有效方法。但是，半焦的挥发性较低，因此很难压成薄片以获得平滑的表面以进行均匀的激光辐照。因此，有必要找到一种有效的方法来实现半焦应用的高精度LIBS检测。本文尝试了两种可行的样品制备方法，一种简单的方法是将半焦粉直接涂在适合在线快速监控的胶带上，另一种更复杂的方法是将粘合剂混入半焦粉中，以获得均匀而紧密的煤片，以提高测量的可重复性。此外，还采用了一种全新的算法，即支持向量机（SVM）与偏最小二乘（PLS）回归（SVM-PLS）相结合，以建立有效的预测模型，从而提供较高的预测精度。确定系数（R ²），预测的均方根误差（RMSEP）和平均相对误差（ARE）分别为0.944、0.90％和0.80％。与传统PLS模型的结果相比，SVM残差校正极大地改善了校准曲线的质量，并且RMSEP和ARE值降低到0.17％，从而提高了预测精度，这比基本的PLS回归要好得多。同时，与直接涂在胶带上的样品相比，粘合剂混合半焦切片的预测误差显着降低。最大相对误差（MRE）为2.71％和5.19％，特征峰的平均RSD值分别为12.1％和16.2％，这表明在磁带上绘制样本的简便方法具有一些预测不确定性。最后，在三天的随机测试中，结果表明，平均RMSEP为1.89％，平均ARE为1.74％，这也证明该粘合剂添加剂可以有效地降低基体效应，并提高半焦测量光谱的稳定性。结果表明，对半焦进行适当的LIBS分析是一种使用此类煤样进行在线预测的可行且有前途的方法。