Abstract：The traditional determination methods of volatile content mainly by gravimetry have some shortcomings, such as poor analysis accuracy, long detection time and complicated sample pretreatment. Ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy technology has the advantages of fast analysis speed and simple operation. It combined with chemometrics method could effectively reduce the interference of quantitative analysis accuracy of volatile content caused by the complexity of coal sample system, and achieve accurate quantitative analysis of volatile content in coal samples. The volatile contents in coal were rapidly and quantitatively analyzed by UV-Vis-NIR spectroscopy technology combined with chemometrics. Firstly, the spectral data of coal samples with different volatile contents were obtained by UV-Vis-NIR spectroscopy technology. Secondly, the latent variables (LVs) of partial least squares (PLS) model, the smoothing points of first derivative (1st Der), the smoothing points and polynomial order of Savitzky Golay smoothing (SG smoothing) were optimized. Finally, the modeling was conducted using three preprocessing methods and their ensemble methods, namely standard normal transformation (SNV), 1st Der, and SG smoothing. Meanwhile, their impacts on the model prediction results were investigated. The results showed that the PLS correction model based on ensemble preprocessing method of SNV-1st Der-SG smoothing had the best predicting performance. When the number of LVs was 15, the determinant coefficient (R2cv) for the leave-one-out cross validation (LOOCV) increased to 0.974 7, and the root-mean-square error (RMSEcv) was reduced to 1.193%. The determinant coefficient (R2p) for external verification was 0.862 5, and the root-mean-square error (RMSEp) was 2.767%. This study provided experimental and theoretical basis for the rapid and accurate analysis of coal properties and the efficient utilization of fossil fuels.
[1] 张占仓.科学稳健实施绿色低碳转型战略路径研究[J].改革与战略(Reformation & Strategy),2022,38(4):1-13. [2] 中国企业改革与发展研究会.中共中央国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见[C]//中国企业改革发展2021蓝皮书.北京:中国商务出版社,2021:451-456. [3] 陈浮,王思遥,于昊辰,等.碳中和目标下煤炭变革的技术路径[J].煤炭学报,2022,47(4):1452-1461. CHEN Fu,WANG Siyao,YU Haochen,et al.Technological innovation paths of coal industry for achieving carbon neutralization[J].Journal of China Coal Society,2022,47(4):1452-1461. [4] 韩立亭.煤炭试验方法标准及其说明[M].4版.北京:中国标准出版社,2015. [5] Lee J M,Kim D W,Kim J S.Reactivity study of combustion for coals and their chars in relation to volatile content[J].Korean Journal of Chemical Engineering,2009,26(2):506-512. [6] 曹东冬.煤质检验中主要指标误差及解决方法研究[J].中国管理信息化(China Management Informationization),2022,25(24):154-156. [7] LE Ba Tuan,肖冬,毛亚纯,等.可见、近红外光谱和深度学习CNN-ELM算法的煤炭分类[J].光谱学与光谱分析,2018,38(7):2107-2112. LE Ba Tuan,XIAO Dong,MAO Yachun,et al.Coal classification based on visible, near-infrared spectroscopy and CNN-ELM algorithm[J].Spectroscopy and Spectral Analysis,2018,38(7):2107-2112. [8] Mao Y,Le B T,Xiao D,et al.Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine[J].Optics & Laser Technology,2019,114:10-15. [9] Pei Y F,Zuo Z T,Zhang Q Z,et al.Data fusion of Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopies to identify geographical origin of wild Paris polyphylla var. yunnanensis[J].Molecules,2019,24(14):2559. [10] Zhang T L,Wu S,Tang H S,et al.Progress of chemometrics in laser-induced breakdown spectroscopy analysis[J].Chinese Journal of Analytical Chemistry,2015,43(6):939-948. [11] Hao H,Cheng S,Ren Z,et al.Rapidly and accurately determining the resin and volatile content of CF/PPBESK thermoplastic prepreg by NIR spectroscopy[J].Composites Part A:Applied Science and Manufacturing,2023,169:107517. [12] 褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011. [13] Tao L,Via B,Wu Y,et al.NIR and MIR spectral data fusion for rapid detection of Lonicera japonica and Artemisia annua by liquid extraction process[J].Vibrational Spectroscopy,2019,102:31-38.
