[1] D. Stratiev, I. Shishkova, I. Tankov, A. Pavlova, Challenges in characterization of residual oils. A review, J. Petroleum Sci. Eng. 178 (2019) 227-250.https://doi.org/10.1016/j.petrol.2019.03.026 [2] F. Qian, W.M. Zhong, W.L. Du, Fundamental theories and key technologies for smart and optimal manufacturing in the process industry, Engineering 3 (2) (2017) 154-160.https://doi.org/10.1016/j.eng.2017.02.011 [3] F.F. Shen, M.H. Wang, L.X. Huang, F. Qian, Exergy analysis and multi-objective optimisation for energy system:a case study of a separation process in ethylene manufacturing, J. Ind. Eng. Chem. 93 (2021) 394-406.https://doi.org/10.1016/j.jiec.2020.10.018 [4] K.X. Bi, B. Beykal, S. Avraamidou, I. Pappas, E.N. Pistikopoulos, T. Qiu, Integrated modeling of transfer learning and intelligent heuristic optimization for steam cracking process, Ind. Eng. Chem. Res. 59 (37) (2020) 16357-16367.https://pubmed.ncbi.nlm.nih.gov/33041499 [5] K.X. Bi, T. Qiu, Novel naphtha molecular reconstruction process using a self-adaptive cloud model and hybrid genetic algorithm-particle swarm optimization algorithm, Ind. Eng. Chem. Res. 58 (36) (2019) 16753-16760.https://doi.org/10.1021/acs.iecr.9b02605 [6] Y.L. Wang, D.D. Shang, X.F. Yuan, Y.F. Xue, J.Z. Sun, Modeling and simulation of reaction and fractionation systems for the industrial residue hydrotreating process, Processes 8 (1) (2019) 32.https://doi.org/10.3390/pr8010032 [7] F. Dai, M.M. Gong, C.S. Li, Z.X. Li, S.J. Zhang, New kinetic model of coal tar hydrogenation process via carbon number component approach, Appl. Energy 137 (2015) 265-272.https://doi.org/10.1016/j.apenergy.2014.10.009 [8] K. He, M. Zhong, J. Fang, Y. Li, Biased minimax probability machine- based adaptive regression for online analysis of gasoline property, IEEE Trans. Industr. Inform. 16 (4) (2020) 2799-2808 [9] Y. Ren, Z.W. Liao, J.Y. Sun, B.B. Jiang, J.D. Wang, Y.R. Yang, Q. Wu, Molecular reconstruction of naphtha via limited bulk properties:methods and comparisons, Ind. Eng. Chem. Res. 58 (40) (2019) 18742-18755.https://doi.org/10.1021/acs.iecr.9b03290 [10] R.J. Quann, S.B. Jaffe, Structure-oriented lumping:describing the chemistry of complex hydrocarbon mixtures, Ind. Eng. Chem. Res. 31 (11) (1992) 2483-2497.https://doi.org/10.1021/ie00011a013 [11] B. Peng, Molecular modelling of petroleum processes, Ph.D. thesis, University of Manchester, UK (1999) [12] L.Y. Liu, S.D. Hou, N. Zhang, Incorporating numerical molecular characterization into pseudo-component representation of light to middle petroleum distillates, Chem. Eng. Sci. X 3 (2019) 100029.https://doi.org/10.1016/j.cesx.2019.100029 [13] 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 (8) (1990) 2083-2088.https://doi.org/10.1016/0009-2509(90)80080-x [14] 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 (24) (1994) 4153-4177.https://doi.org/10.1016/s0009-2509(05)80013-2 [15] D. Hudebine, J.J. Verstraete, Molecular reconstruction of LCO gasoils from overall petroleum analyses, Chem. Eng. Sci. 59 (22-23) (2004) 4755-4763.https://doi.org/10.1016/j.ces.2004.09.019 [16] Bojkovic, T. Dijkmans, H. Dao Thi, M. Djokic, K. M. van Geem, Molecular reconstruction of hydrocarbons and sulfur-containing compounds in atmospheric and vacuum gas oils, Energy Fuels 35 (7) (2021) 5777-5788 [17] D.K. Liguras, D.T. Allen, Structural models for catalytic cracking. 2. Reactions of simulated oil mixtures, Ind. Eng. Chem. Res. 28 (6) (1989) 674-683.https://doi.org/10.1021/ie00090a005 [18] S.B. Jaffe, H. Freund, W.N. Olmstead, Extension of structure-oriented lumping to vacuum residua, Ind. Eng. Chem. Res. 44 (26) (2005) 9840-9852.https://doi.org/10.1021/ie058048e [19] 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 [20] L.D. Tian, J.M. Wang, B.X. Shen, J.C. Liu, Building a kinetic model for steam cracking by the method of structure-oriented lumping, Energy Fuels 24 (8) (2010) 4380-4386.https://doi.org/10.1021/ef100534e [21] L.D. Tian, B.X. Shen, J.C. Liu, Building and application of delayed coking structure-oriented lumping model, Ind. Eng. Chem. Res. 51 (10) (2012) 3923-3931.https://doi.org/10.1021/ie2025272 [22] R. Zhu, B. Shen, J. Liu, X. Chen, A kinetic model for catalytic cracking of vacuum gas oil using a structure-oriented lumping method, Energy Sources A Recovery Util. Environ. Eff. 34 (22) (2012) 2066-2072.https://doi.org/10.1080/15567036.2012.673052 [23] P.F. He, C. Zhu, T.C. Ho, A two-zone model for fluid catalytic cracking riser with multiple feed injectors, AIChE J. 61 (2) (2015) 610-619.https://doi.org/10.1002/aic.14665 [24] S. Haitao, Z. Yuxia, D. Zhijian, L. Zelong, H. Zhiqing, Z. Xinyi, L. Yingrong, T. Songbai, Studies on catalytic cracking performances of saturates and aromatics separated from vacuum gas oil-i. separation and characterization, Prepr. Pap. Am. Chem. Soc., Div. Fuel. Chem. 58 (1) (2013) 976-977 [25] J.C. Chen, Z. Fang, T. Qiu, Molecular reconstruction model based on structure oriented lumping and group contribution methods, Chin. J. Chem. Eng. 26 (8) (2018) 1677-1683.https://doi.org/10.1016/j.cjche.2017.09.013 [26] N. Charon-Revellin, H. Dulot, C. López-García, J. Jose, Kinetic modeling of vacuum gas oil hydrotreatment using a molecular reconstruction approach, Oil Gas Sci. Technol.-Rev. IFP Energies Nouvelles 66 (3) (2011) 479-490.https://doi.org/10.2516/ogst/2010005 [27] C.G. Pernalete, J. van Baten, J.C. Urbina, J.F. Arévalo, A molecular reconstruction feed characterization and CAPE OPEN implementation strategy to develop a tool for modeling HDT reactors for light petroleum cuts, in:12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering. Elsevier, Amsterdam (2015) 359-364 [28] G. Guo, Y. Li, S. Hou, Y. Luo, Model estimation and simulation of hydrocarbon composition of molecular reconstruction model of diesel, Comput. Appl. Chem. 31 (12) (2014) 1452-1456 [29] Y. Ren, Z.W. Liao, J.Y. Sun, B.B. Jiang, J.D. Wang, Y.R. Yang, Q. Wu, Molecular reconstruction:recent progress toward composition modeling of petroleum fractions, Chem. Eng. J. 357 (2019) 761-775.https://doi.org/10.1016/j.cej.2018.09.083 [30] 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 (9) (2007) 1020-1034.https://doi.org/10.1016/j.compchemeng.2006.09.001 [31] M.S.A. Mi, ^. Nan, A novel methodology in transforming bulk properties of refining streams into molecular information, Chem. Eng. Sci. 60 (23) (2005) 6702-6717.https://doi.org/10.1016/j.ces.2005.05.033 [32] Y.W. Wu, N. Zhang, Molecular characterization of gasoline and diesel streams, Ind. Eng. Chem. Res. 49 (24) (2010) 12773-12782.https://doi.org/10.1021/ie101647d [33] Y. Wu, N. Zhang, Molecular management for refining operations, Ph.D. thesis, University of Manchester, UK (2010) [34] L. Liu, Molecular characterisation and modelling for refining processes, Ph.D. thesis, University of Manchester Manchester, UK (2015) [35] K. Wang, S.Y. Li, Modified molecular matrix model for predicting molecular composition of naphtha, Chin. J. Chem. Eng. 25 (12) (2017) 1856-1862.https://doi.org/10.1016/j.cjche.2017.01.008 [36] C. Cui, T. Billa, L.Z. Zhang, Q. Shi, S.Q. Zhao, M.T. Klein, C.M. Xu, Molecular representation of the petroleum gasoline fraction, Energy Fuels 32 (2) (2018) 1525-1533.https://doi.org/10.1021/acs.energyfuels.7b03588 [37] Y. Ren, Z. Liao, J. Sun, B. Jiang, J. Wang, Y. Yang, Q. Wu, Novel parameter estimation method for molecular reconstruction of naphtha by gamma distribution, Chem. Eng. Trans. 76 (2019) 793-798 [38] N. Glazov, P. Dik, A. Zagoruiko, Effect of experimental data accuracy on stochastic reconstruction of complex hydrocarbon mixture, Catal. Today 378 (2021) 202-210.http://dx.doi.org/10.1016/j.cattod.2020.12.022 [39] M. Lopez 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.http://dx.doi.org/10.1016/j.cattod.2016.02.041 [40] Alvarez-Majmutov, J. Chen, R. Gieleciak, Molecular-level modeling and simulation of vacuum gas oil hydrocracking, Energy Fuels 30 (1) (2016) 138-148 [41] L. Yan, X.P. Zhang, S.J. Zhang, The study of molecular modeling for heavy oil thermal cracking, Chem. Eng. Technol. 30 (9) (2007) 1166-1175.https://doi.org/10.1002/ceat.200700178 [42] S.R. Horton, L.Z. Zhang, Z. Hou, C.A. Bennett, M.T. Klein, S.Q. Zhao, Molecular-level kinetic modeling of resid pyrolysis, Ind. Eng. Chem. Res. 54 (16) (2015) 4226-4235.https://doi.org/10.1021/ie5041572 [43] S.R. Horton, R.J. Mohr, Y. Zhang, F.P. Petrocelli, M.T. Klein, Molecular-level kinetic modeling of biomass gasification, Energy Fuels 30 (3) (2016) 1647-1661.http://dx.doi.org/10.1021/acs.energyfuels.5b01988 [44] Hou, L.Z. Zhang, S.R. Horton, Q. Shi, S.Q. Zhao, C.M. Xu, M.T. Klein, Molecular-level composition and reaction modeling for heavy petroleum complex system, Structure and Modeling of Complex Petroleum Mixtures, Springer International Publishing, Cham, 2015, pp. 93-119 [45] C.U. Deniz, M. Yasar, M.T. Klein, Stochastic reconstruction of complex heavy oil molecules using an artificial neural network, Energy Fuels 31 (11) (2017) 11932-11938.http://dx.doi.org/10.1021/acs.energyfuels.7b02311 [46] X. Zhou, Z. Hou, J.G. Wang, W. Fang, A.Z. Ma, J.B. Guo, M.T. Klein, Molecular-level kinetic model for C12 continuous catalytic reforming, Energy Fuels 32 (6) (2018) 7078-7085.https://doi.org/10.1021/acs.energyfuels.8b00950 [47] 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 (1) (1997) 41-54.http://dx.doi.org/10.1016/S0926-860X(97)00123-3 [48] 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 (3) (1994) 570-575.https://doi.org/10.1021/ef00045a009 [49] L.Z. Zhang, Z. Hou, S.R. Horton, M.T. Klein, Q. Shi, S.Q. Zhao, C.M. Xu, Molecular representation of petroleum vacuum resid, Energy Fuels 28 (3) (2014) 1736-1749.https://doi.org/10.1021/ef402081x [50] C.U. Deniz, M. Yasar, M.T. Klein, A new extended structural parameter set for stochastic molecular reconstruction:application to asphaltenes, Energy Fuels 31 (8) (2017) 7919-7931.http://dx.doi.org/10.1021/acs.energyfuels.7b01006 [51] C. Cui, L.Z. Zhang, Y.J. Ma, T. Billa, Z. Hou, Q. Shi, S.Q. Zhao, C.M. Xu, M.T. Klein, Computer-aided gasoline compositional model development based on GC-FID analysis, Energy Fuels 32 (8) (2018) 8366-8373.https://doi.org/10.1021/acs.energyfuels.8b01953 [52] J. J. Verstraete, N. Revellin, H. Dulot, D. Hudebine, Molecular reconstruction of vacuum gasoils, Prepr. Symp. Am. Chem. Soc., Div. Fuel Chem. 49 (1) (2004) 20-21 [53] Hudebine, D., Verstraete, J., Chapus, T., Statistical reconstruction of gas oil cuts, Oil Gas Sci. Technol.-Rev. IFP Energies Nouvelles 66 (3) (2011) 461-477 [54] L.P. Oliveira, J.J. Verstraete, M. Kolb, Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion, Sci. China Chem. 56 (11) (2013) 1608-1622.http://dx.doi.org/10.1007/s11426-013-4989-3 [55] J.J. Verstraete, P. Schnongs, H. Dulot, D. Hudebine, Molecular reconstruction of heavy petroleum residue fractions, Chem. Eng. Sci. 65 (1) (2010) 304-312.http://dx.doi.org/10.1016/j.ces.2009.08.033 [56] C. Pernalete, F. Ruette, A. Peraza, An application of molecular reconstruction for light petroleum cuts via entropy maximization, J. Comput. Methods Sci. Eng. 17 (1) (2017) 177-186.https://doi.org/10.3233/jcm-160671 [57] 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 (7) (2013) 3622-3641.https://doi.org/10.1021/ef300768u [58] A. Alvarez-Majmutov, J.W. 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 (12) (2014) 7385-7393.http://dx.doi.org/10.1021/ef5018169 [59] A.S. Hukkerikar, B. Sarup, A. Ten Kate, J. Abildskov, G. Sin, R. Gani, Group-contribution+ (GC+) based estimation of properties of pure components:improved property estimation and uncertainty analysis, Fluid Phase Equilibria 321 (2012) 25-43.http://dx.doi.org/10.1016/j.fluid.2012.02.010 [60] R. Gani, B. Nielsen, A. Fredenslund, A group contribution approach to computer-aided molecular design, AIChE J. 37 (9) (1991) 1318-1332.https://doi.org/10.1002/aic.690370905 [61] J. Marrero, R. Gani, Group-contribution based estimation of pure component properties, Fluid Phase Equilibria 183-184 (2001) 183-208.http://dx.doi.org/10.1016/S0378-3812(01)00431-9 [62] 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 (3) (2018) 3344-3355.https://doi.org/10.1021/acs.energyfuels.8b00018 [63] K. V. Price, Differential Evolution, Springer Berlin Heidelberg, Berlin, Heidelberg, (2013)187-214 [64] M. Riazi, Characterization and properties of petroleum fractions, ASTM Int.,West Conshohocken, PA, USA, 2005. [65] R.D. American Petroleum Institute, Technical Data Book:Petroleum Refining, American Petroleum Institute, Washington, DC, 1997. |