RAPID PREDICTION OF HYDROCARBON MOLECULAR COMPOSITION OF NAPHTHA BASED ON NEAR INFRARED SPECTROSCOPY

Abstract

Abstract: A rapid method for the determination of molecular level composition of naphtha was provided. Based on the data samples of group composition (PINA) and single hydrocarbon composition of naphtha by gas chromatography in laboratory, the distribution ratio library of single hydrocarbon in naphtha was established. A large number of virtual samples were generated by sample enhanced (DA) method. A prediction model of naphtha PINA composition was established by using near infrared spectroscopy (NIR) as input variables and partial least squares (PLS) method. A prediction model of naphtha mono-hydrocarbon distribution ratio was established by K-nearest neighbor regression (KNR) method. The prediction results show that within the range of experimental data samples, the model can be used to quickly determine the monomer hydrocarbon molecular composition of naphtha.

Key words: naphtha, detailed composition, virtual sample, near infrared spectroscopy

References

[1]杨利斌, 宋帮勇, 孔祥冰, 　等.石脑油中单体组分在碳数分布上对乙烯收率的影响[J].现代化工, 2011, 31(S1):164-165 [2]Silva V, Reboucas M V, Salles A R, et al.Determination of naphtha composition by near infrared spectroscopy and multivariate regression to control steam cracker processes[J].Fuel Processing Technology, 2015, 131:230-237 [3] 张利军, 张永刚, 王国清, 　等.石脑油的组成预测方法[J].化工进展, 2011, 30(02):278-283 [4]Chung H, Lee J S, Ku M S.Detailed compositional analysis of naphtha and reformate by near-infrared spectroscopy[J].American Laboratory, 1999, 31(23):24-25 [5]Bueno A, Baldrich C A, Daniel M V.Characterization of Catalytic Reforming Streams by NIR Spectroscopy[J].Energy & Fuels, 2009, 23(3):3172-3177 [6]丁立国, 孙双, 马发明, 　等.石脑油族组成分析测定的条件探讨[J].化工管理, 2018, 000(012):95-95 [7]张丽静.气相色谱法测定石脑油中组成的研究[J].化工管理, 2018, 000(001):42-42 [8]陆海萍.多维气相色谱法测定石脑油族组成[J].石化技术与应用, 2011, 29(5):457-461 [9] 宋光, 邱彤, 田亮.一种基于常规物性的石脑油组成的预测方法[J].计算机与应用化学, 2011, 28(11):1367-1371 [10]刘莎, 朱虹, 褚小立, 　等.汽油族组成的近红外光谱快速测定[J].分析测试学报, 2002, 21(001):40-43 [11]Silva N, Rosa C, Domingos D, et al.NIR-based octane rating simulator for use in gasoline compounding processes[J].Fuel, 2019, 243(MAY 1):381-389 [12]Chung H, Cho S, Toyoda Y, et al.Moment combined partial least squares (MC-PLS) as an improved quantitative calibration method: application to the analyses of petroleum and petrochemical products[J].Analyst, 2006, 131(5):684-691 [13]Ku M S, Chung H.Comparison of Near-Infrared and Raman Spectroscopy for the Determination of Chemical and Physical Properties of Naphtha[J].Applied Spectroscopy, 1999, 53(5):557-564 [14]褚小立.近红外光谱快速测定重整汽油详细族组成[J].石油化工, 2001, 30(11):866-870 [15] Lambert D, Williams J, Dastillung R, et al.Steam Cracker Optimisation through On-Line TOPNIR Analysis and Rigorous Kinetic Models[C]// Aiche Spring National Meeting. 2010. [16]于旭, 杨静, 谢志强.虚拟样本生成技术研究[J].计算机科学, 2011, 038(003):16-19 [17]朱宝, 陈忠圣, 余乐安.一种新颖的小样本整体趋势扩散技术[J].化工学报, 2016, v.67(03):142-148 [18]Conlin A K, Martin E B, Morris A J.Data augmentation: an alternative approach to the analysis of spectroscopic data[J].Chemometrics and Intelligent Laboratory Systems, 1998, 44(1):161-173 [19] Georgouli K, Osorio M T, Jesus M, et al.Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement.[J]. Journal of Chemometrics, 2018, 32(3):e3004-e3004 [20]M.J. Sáiz-Abajo a,B-H. Mevik b,B V H S,et al. Ensemble methods and data augmentation by noise addition applied to the analysis of spectroscopic data[J].Analytica Chimica Acta, 2005, 533(2):147-159 [21]E Martínez, Huerta S, H Ménez, et al.Comparison of chemometric technique applied to near infrared spectra for a gasoline blending control[J].Journal of Near Infrared Spectroscopy, 2008, 16(3):297-297 [22]Lee Y, Chung H, Kim N.Spectral Range Optimization for the Near-Infrared Quantitative Analysis of Petrochemical and Petroleum Products: Naphtha and Gasoline[J].Applied Spectroscopy, 2006, 60(8):892-892