Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

doi:10.1016/j.cjche.2021.06.007

Abstract

Abstract: Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.

Key words: Vacuum gas oil, Molecular reconstruction, Model, Algorithm, Optimization

摘要： Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.

关键词: Vacuum gas oil, Molecular reconstruction, Model, Algorithm, Optimization

Na Wang, Chong Peng, Zhenmin Cheng, Zhiming Zhou. Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization[J]. Chinese Journal of Chemical Engineering, 2022, 48(8): 21-29.

Na Wang, Chong Peng, Zhenmin Cheng, Zhiming Zhou. Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization[J]. 中国化学工程学报, 2022, 48(8): 21-29.

References

[1] C.I.C. Pinheiro, J.L. Fernandes, L. Domingues, A.J.S. Chambel, I. Gra?a, N.M.C. Oliveira, H.S. Cerqueira, F.R. Ribeiro, Fluid catalytic cracking (FCC) process modeling, simulation, and control, Ind. Eng. Chem. Res. 51 (2012) 1–29
[2] R. Sahu, B.J. Song, J.S. Im, Y P. Jeon, C.W. Lee, A review of recent advances in catalytic hydrocracking of heavy residues, J. Ind. Eng. Chem. 27 (2015) 12–24
[3] R. Prajapati, K. Kohli, S.K. Maity, Slurry phase hydrocracking of heavy oil and residue to produce lighter fuels: An experimental review, Fuel 288 (2021) 119686
[4] C.S. Laxminarasimhan, R.P. Verma, P.A. Ramachandran, Continuous lumping model for simulation of hydrocracking, AIChE J. 42 (1996) 2645–2653
[5] T.C. Ho, Kinetic modeling of large-scale reaction systems, Catal. Rev.: Sci. Eng. 50 (2008) 287–378
[6] J. Ancheyta, Deactivation of Heavy Oil Hydroprocessing Catalysts, John Wiley & Sons: Hoboken, New Jersey, 2016
[7] M.T. Klein, G. Hou, R. Bertolacini, L.J. Broadbelt, A. Kumar, Molecular Modeling in Heavy Hydrocarbon Conversions, CRC Press: Boca Raton, Florida, 2005
[8] R. Van de Vijver, B.R. Devocht, K.M. Van Geem, J.W. Thybaut, G.B. Marin, Challenges and opportunities for molecule-based management of chemical processes, Curr. Opin. Chem. Eng. 13 (2016) 142–149
[9] L.P. de Oliveira, D. Hudebine, D. Guillaume, J.J. Verstraete, A review of kinetic modeling methodologies for complex processes, Oil Gas Sci. Technol. 71 (2016) 45
[10] Y. Ren, Z. Liao, J. Sun, B. Jiang, J. Wang, Y. Yang, Q. Wu, Molecular reconstruction: recent progress toward composition modeling of petroleum fractions, Chem. Eng. J. 357 (2019) 761–775
[11] M. Neurock, C. Libanati, A. Nigam, M.T. Klein, Monte Carlo simulation of complex reaction systems: molecular structure and reactivity in modelling heavy oils, Chem. Eng. Sci. 45 (1990) 2083–2088
[12] R.J. Quann, S.B. Jaffe, Structure-oriented lumping: describing the chemistry of complex hydrocarbon mixtures, Ind. Eng. Chem. Res. 31 (1992) 2483–2497
[13] B. Peng, Molecular modelling of petroleum processes, Ph. D. Thesis, University of Manchester, 1999
[14] D. Hudebine, C. Vera, F. Wahl, J. Verstraete, Molecular representation of hydrocarbon mixtures from overall petroleum analyses, AIChE Spring Meeting, New Orleans, 2002
[15] M. Neurock, A. Nigam, D. Trauth, M.T. Klein, Molecular representation of complex hydrocarbon feedstocks through efficient characterization and stochastic algorithms, Chem. Eng. Sci. 49 (1994) 4153–4177
[16] J.M. Sheremata, M.R. Gray, H.D. Dettman, W.C. McCaffrey, Quantitative molecular representation and sequential optimization of athabasca asphaltenes, Energy Fuels 18 (2004) 1377–1384
[17] C.U. Deniz, M. Yasar, M.T. Klein, A new extended structural parameter set for stochastic molecular reconstruction: application to asphaltenes, Energy Fuels 31 (2017) 7919–7931
[18] D.M. Trauth, S.M. Stark, T.F. Petti, M. Neurock, M.T. Klein, Representation of the molecular structure of petroleum resid through characterization and Monte Carlo modeling, Energy Fuels 8 (1994) 576–580
[19] D.M. Campbell, M.T. Klein, Construction of a molecular representation of a complex feedstock by Monte Carlo and quadrature methods, Appl. Catal. A: Gen. 160 (1997) 41–54
[20] D M. Campbell, C. Bennett, Z. Hou, M.T. Klein, Attribute-based modeling of resid structure and reaction, Ind. Eng. Chem. Res. 48 (2009) 1683–1693
[21] K. Wang, S. Li, Modified molecular matrix model for predicting molecular composition of naphtha, Chin. J. Chem. Eng. 25 (2017) 1856–1862
[22] K.M. Van Geem, D. Hudebine, M.F. Reyniers, F. Wahl, J.J. Verstraete, G.B. Marin, Molecular reconstruction of naphtha steam cracking feedstocks based on commercial indices, Comput. Chem. Eng. 31 (2007) 1020–1034
[23] S.B. Jaffe, H. Freund, W.N. Olmstead, Extension of structure-oriented lumping to vacuum residua, Ind. Eng. Chem. Res. 44 (2005) 9840–9852
[24] L. Tian, B. Shen, J. Liu, Building and application of delayed coking structure-oriented lumping model, Ind. Eng. Chem. Res. 51 (2012) 3923–3931
[25] D. Hudebine, J.J. Verstraete, Molecular reconstruction of LCO gasoils from overall petroleum analyses, Chem. Eng. Sci. 59 (2004) 4755–4763
[26] A. Alvarez-Majmutov, J. Chen, R. Gieleciak, D. Hager, N. Heshka, S. Salmon, Deriving the molecular composition of middle distillates by integrating statistical modeling with advanced hydrocarbon characterization, Energy Fuels 28 (2014) 7385–7393
[27] M.L. Abelairas, L.P. de Oliveira, J.J. Verstraete, Application of Monte Carlo techniques to LCO gas oil hydrotreating: molecular reconstruction and kinetic modelling, Catal. Today 271 (2016) 188–198
[28] J.J. Verstraete, N. Revellin, H. Dulot, D. Hudebine, Molecular reconstruction of vacuum gasoils, Prepr. Pap. Am. Chem. Soc., Div. Fuel Chem. 49 (2004) 20–21
[29] A. Alvarez-Majmutov, J. Chen, R. Gieleciak, Molecular-level modeling and simulation of vacuum gas oil hydrocracking, Energy Fuels 30 (2016) 138–148
[30] A. Alvarez-Majmutov, J. Chen, Stochastic modeling and simulation approach for industrial fixed-bed hydrocrackers, Ind. Eng. Chem. Res. 56 (2017) 6926–6938
[31] J.J. Verstraete, P. Schnongs, H. Dulot, D. Hudebine, Molecular reconstruction of heavy petroleum residue fractions, Chem. Eng. Sci. 65 (2010) 304–312
[32] L.P. de Oliveira, A.T. Vazquez, J.J. Verstraete, M. Kolb, Molecular reconstruction of petroleum fractions: application to vacuum residues from different origins, Energy Fuels 27 (2013) 3622–3641
[33] A. Alvarez-Majmutov, R. Gieleciak, J. Chen, Modeling the molecular composition of vacuum residue from oil sand bitumen, Fuel 241 (2019) 744–752
[34] A. Alvarez-Majmutov, R. Gieleciak, J. Chen, Deriving the molecular composition of vacuum distillates by integrating statistical modeling and detailed hydrocarbon characterization, Energy Fuels 29 (2015) 7931–7940
[35] T. F. Petti, D. M. Trauth, S. M. Stark, M. Neurock, M. Yasar, M.T. Klein, CPU issues in the representation of the molecular structure of petroleum resid through characterization, reaction, and Monte Carlo modeling, Energy Fuels 8 (1994) 570–575
[36] L.P. de Oliveira, J.J. Verstraete, M. Kolb, Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion, Sci. China-Chem. 56 (2013) 1608–1622
[37] C.U. Deniz, M. Yasar, M.T. Klein, Stochastic reconstruction of complex heavy oil molecules using an artificial neural network, Energy Fuels 31 (2017) 11932–11938
[38] C.U. Deniz, S.H.O. Yasar, M. Yasar, M.T. Klein, Effect of boiling point and density prediction methods on stochastic reconstruction, Energy Fuels 32 (2018) 3344–3355
[39] C.E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J. 27 (1948) 623–656
[40] S. Guiasu, A. Shenitzer, The principle of maximum entropy, The Mathemat. Intelli. 7(1985) 42–48
[41] S.P. Pyl, K.M. Van Geem, M.F. Reyniers, G.B. Marin, Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis, AIChE J. 56 (2010) 3174–3188
[42] D. Hudebine, J.J. Verstraete, Reconstruction of petroleum feedstocks by entropy maximization. Application to FCC gasolines, Oil Gas Sci. Technol. 66 (2011) 437–460
[43] Y. Pan, B. Yang, X. Zhou, Feedstock molecular reconstruction for secondary reactions of fluid catalytic cracking gasoline by maximum information entropy method, Chem. Eng. J. 281 (2015) 945–952
[44] S. Feng, C. Cui, K. Li, L. Zhang, Q. Shi, S. Zhao, C. Xu, Molecular composition modelling of petroleum fractions based on a hybrid structural unit and bond-electron matrix (SU-BEM) framework, Chem. Eng. Sci. 201 (2019) 145–156