Impact of external moisture on quantitative analysis of coal and rapid drying by a CO2 laser to improve prediction accuracy based on laser induced breakdown spectroscopy.

Abstract

For high-accuracy determination of ash, fixed carbon, and volatile matter in coal, generally, the impact of external moisture content in coal must be considered and removed in quantitative modeling based on laser-induced breakdown spectroscopy (LIBS). Herein, the ash, fixed carbon, and volatile of coals with moisture contents from 3% to 15% are quantitatively assessed using partial least squares based on principal component analysis (PCA-PLS), and the predictive limit of this model for moist coals is explored based on LIBS. To validate the industrial feasibility of the method, an infrared CO2 laser based heating technique is employed to rapidly dry the moist coals before laser ablation. The results demonstrate that root mean square error of prediction (RMSEP) of all ash, volatile carbon, and fixed carbon become higher with moisture content increase. Nevertheless, as the moisture content reaches 3%, the prediction model retains an acceptable predictive capability with mean absolute error (MAE) of ash, volatile matter, and fixed carbon of 1.85%, 1.5%, and 1.79%, respectively. When the IR laser for drying is employed to irradiating for 40 ms, external water in coal with an original moisture content of 15% can be quickly removed, resulting in a decrease in RMSEP of ash, volatile matter, and fixed carbon from 3.06%, 5.42%, and 6.22% to 1.47%, 3.16%, and 3.34%, respectively. This method provides a potential technical solution for the use of infrared laser-assisted LIBS real-time online rapid detection of indicators in raw coal with high moisture content.