燃料化学学报（中英文）2026，Vol.54Issue(4)：17-28,12.DOI:10.1016/S1872-5813(25)60620-7
近红外光谱智能分析煤直接液化柴油组分
Intelligent analysis of direct coal liquefaction diesel components by near-infrared spectroscopy
摘要
Abstract
Diesel accounts for over 60％of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of ≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models-Lasso,SVR and XGBoost-was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R2 from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R2 values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98％,with an absolute prediction error below 0.2％.Thus,rapid analysis of DCL diesel components was successfully realized.关键词
煤直接液化油/光谱实时检测/机器学习/特征提取/组分预测Key words
direct coal liquefaction diesel/real-time spectral detection/machine learning/feature extraction/component prediction分类
化学化工引用本文复制引用
王喜武,李皓玮,齐振东,王兴宝,冯杰,祝一蒙,李文英..近红外光谱智能分析煤直接液化柴油组分[J].燃料化学学报（中英文）,2026,54(4):17-28,12.基金项目
Supported by National Natural Science Foundation of China(U24B6018,22178243). （U24B6018,22178243）
