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 di...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 R^(2) 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 R^(2) 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.展开更多
为探究煤液化柴油理化特性对整车实际道路排放特性和油耗的影响,选用石油基国六柴油(G6)、煤直接液化柴油(diesel of direct coal liquefaction,DDCL)以及煤直接液化和间接液化调和柴油(diesel of blend coal liquefaction,DBCL)三种油...为探究煤液化柴油理化特性对整车实际道路排放特性和油耗的影响,选用石油基国六柴油(G6)、煤直接液化柴油(diesel of direct coal liquefaction,DDCL)以及煤直接液化和间接液化调和柴油(diesel of blend coal liquefaction,DBCL)三种油品,在满足国Ⅵ排放法规的柴油车上依据GB 17691—2018,开展实际道路排放测量方法(PEMS)试验。结果表明:DDCL在三种油品中具有最低的硫含量、多环芳烃含量、运动黏度和t90/t95温度(燃料90%和95%回收温度),这些特性使其在燃油排放和油耗性能方面表现出色,因较低的硫与多环芳烃的含量可减少有害物生成,较低的运动黏度与t90/t95温度能提升雾化和燃烧效率,降低油耗并改善排放;在排放性能方面,与G6相比,DDCL和DBCL的NO_(x)排放分别下降了63.82%和39.11%,DDCL的PN(污染物的粒子数量)排放下降了40.54%,DDCL和DBCL的CO排放分别下降了97.24%和96.02%;在油耗方面,与G6相比,DDCL和DBCL燃油的百公里油耗分别下降了7.81%和2.34%。展开更多
基金Supported by National Natural Science Foundation of China(U24B6018,22178243)。
文摘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 R^(2) 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 R^(2) 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.
文摘为探究煤液化柴油理化特性对整车实际道路排放特性和油耗的影响,选用石油基国六柴油(G6)、煤直接液化柴油(diesel of direct coal liquefaction,DDCL)以及煤直接液化和间接液化调和柴油(diesel of blend coal liquefaction,DBCL)三种油品,在满足国Ⅵ排放法规的柴油车上依据GB 17691—2018,开展实际道路排放测量方法(PEMS)试验。结果表明:DDCL在三种油品中具有最低的硫含量、多环芳烃含量、运动黏度和t90/t95温度(燃料90%和95%回收温度),这些特性使其在燃油排放和油耗性能方面表现出色,因较低的硫与多环芳烃的含量可减少有害物生成,较低的运动黏度与t90/t95温度能提升雾化和燃烧效率,降低油耗并改善排放;在排放性能方面,与G6相比,DDCL和DBCL的NO_(x)排放分别下降了63.82%和39.11%,DDCL的PN(污染物的粒子数量)排放下降了40.54%,DDCL和DBCL的CO排放分别下降了97.24%和96.02%;在油耗方面,与G6相比,DDCL和DBCL燃油的百公里油耗分别下降了7.81%和2.34%。
