High-accuracy prediction of carbon content in semi-coke by laser-induced breakdown spectroscopy
Semi-coke, as one kind of special coal resource with relatively high concentration carbon and low volatility, plays an important role in the coal chemical industry and city clean. LIBS has been proved as an effective way to make an online analysis for the coal products. However, the lower volatility of semi-coke makes it hard to be pressed into a slice to get a smooth surface for a uniform laser-irradiation. Therefore, it is necessary to find an effective way to realize a high-accuracy LIBS detection for semi-coke application. Herein, two feasible ways of sample preparation are tried, one easy way is directly painting semi-coke powders on a tape that suitable for online fast monitoring, and the other complicated way is to mix binder into the semi-coke powder then that the uniformly and tightly coal slices are obtained, thus to improve the repeatability of measurement. Moreover, a totally new algorithm, SVM combined with PLS regression, is utilized to establish an effective prediction model to make a high prediction accuracy. The R2, RMSEP, and average relative error (ARE) are 0.944, 0.90%, and 0.80%, respectively. In comparison with the result of the traditional PLS model, the SVM residual correction greatly improves the quality of the calibration curve and makes RMSEP and ARE reduced 0.17%, thus improves the prediction accuracy, which is much better than basic PLS regression. Meanwhile, the prediction error from binder mixed semi-coke slice is significantly reduced compared to that with directly painting samples on a tape. The maximum relative errors (MRE) are 2.71% and 5.19%, and the average RSD of the characteristic peaks are 12.1% and 16.2%, respectively, indicating that the easy way with painting sample on tape has little prediction uncertainties. Finally, in a three-day random test, the average RMSEP is 1.89% and average ARE is 1.74%, which also proves the binder additive can effectively reduce the matrix effect and enhance the stability of the spectrum for semi-coke measurement. The result proposes the proper LIBS analysis on semi-coke is a feasible and promising approach for on-line prediction of such kind of coal sample.