1 |
Marafi A, Albazzaz H, Rana M S. Hydroprocessing of heavy residual oil: Opportunities and challenges[J]. Catalysis Today, 2019, 329: 125⁃134.
|
2 |
Sahu R, Song B J, Im J S, et al. A review of recent advances in catalytic hydrocracking of heavy residues[J]. Journal of Industrial and Engineering Chemistry, 2015, 27: 12⁃24.
|
3 |
吴青. 炼化企业数字化工厂建设及其关键技术研究[J]. 无机盐工业, 2018, 50(2): 1⁃7.
|
|
Wu Q. Research on construction of digital factory of refinery enterprise and its key technologies[J]. Inorganic Chemicals Industry, 2018, 50(2): 1⁃7.
|
4 |
Basak K, Sau M, Manna U, et al. Industrial hydrocracker model based on novel continuum lumping approach for optimization in petroleum refinery[J]. Catalysis Today, 2004, 98(1⁃2): 253⁃264.
|
5 |
Ancheyta J, Sánchez S, Rodríguez M A. Kinetic modeling of hydrocracking of heavy oil fractions: A review[J]. Catalysis Today, 2006, 109(1⁃4): 76⁃92.
|
6 |
Balasubramanian P, Pushpavanam S. Model discrimination in hydrocracking of vacuum gas oil using discrete lumped kinetics[J]. Fuel, 2008, 87(8⁃9): 1660⁃1672.
|
7 |
Iplik E, Aslanidou I, Kyprianidis K. Hydrocracking: A perspective towards digitalization[J].Sustainability,2020,12(17): 7058.
|
8 |
Stangeland B E. A kinetic model for the prediction of hydrocracker yields[J]. Industrial & Engineering Chemistry Process Design and Development, 1974,13(1): 71⁃76.
|
9 |
Mohanty S, Saraf D N, Kunzru D. Modeling of a hydrocracking reactor[J]. Fuel Processing Technology, 1991, 29(1⁃2): 1⁃17.
|
10 |
Pacheco M A, Dassori C G. Hydrocracking: An improved kinetic model and reactor modeling[J]. Chemical Engineering Communications, 2002, 189(12): 1684⁃1704.
|
11 |
Palos R, Gutiérrez A, Hita I, et al. Kinetic modeling of hydrotreating for enhanced upgrading of light cycle oil[J]. Industrial & Engineering Chemistry Research, 2019, 58(29): 13064⁃13075.
|
12 |
Olmos⁃Cerda E H, Laredo G C, Pérez⁃Romo P, et al. Selective hydrogenation of light cycle oil for BTX and gasoline production purposes[J]. International Journal of Chemical Reactor Engineering, 2022, 20(1): 69⁃82.
|
13 |
Morales⁃Blancas M, Mederos⁃Nieto F S, Elizalde I, et al. Discrete lumping kinetic models for hydrodesulfuration and hydrocracking of a mixture of FCC feedstock and light gasoil[J]. Chemical Papers, 2022, 76(8): 4885⁃4891.
|
14 |
Li M L, Ren T X, Sun Y D. Analysis of reaction path and different lumped kinetic models for asphaltene hydrocracking[J]. Fuel, 2022, 325: 124840.
|
15 |
Soto⁃Azuara L A, Ramírez⁃López R, del Carmen Monterrubio⁃Badillo M, et al. Mathematical modeling of the hydrocracking kinetics of a heavy oil fraction using the discrete lumping approach: The effect of the variation of the lump number[J]. Reaction Kinetics, Mechanisms and Catalysis, 2022, 135(2): 655⁃667.
|
16 |
Shi S, Tan W, Sun J S. Progress in kinetic predictions for complex reaction of hydrocarbons: From mechanism studies to industrial applications[J]. Reviews in Chemical Engineering, 2016, 32(3): 363⁃378.
|
17 |
Laxminarasimhan C S, Verma R P, Ramachandran P A. Continuous lumping model for simulation of hydrocracking[J]. AIChE Journal, 1996, 42(9): 2645⁃2653.
|
18 |
Chou M Y, Ho T C. Continuum theory for lumping nonlinear reactions[J]. AIChE Journal, 1988, 34(9): 1519⁃1527.
|
19 |
Elizalde I, Ancheyta J. Modeling the simultaneous hydrodesulfurization and hydrocracking of heavy residue oil by using the continuous kinetic lumping approach[J]. Energy & Fuels, 2012, 26(4): 1999⁃2004.
|
20 |
Becker P J, Serrand N, Celse B, et al. Comparing hydrocracking models: Continuous lumping vs. single events[J]. Fuel, 2016, 165: 306⁃315.
|
21 |
Naderi H, Shokri S, Ahmadpanah S J. Optimization of kinetic lumping model parameters to improve products quality in the hydrocracking process[J]. Brazilian Journal of Chemical Engineering, 2018, 35: 757⁃768.
|
22 |
Faraji D, Zabihi S, Ghadiri M, et al. Computational fluid dynamic modeling and simulation of hydrocracking of vacuum gas oil in a fixed⁃bed reactor[J]. ACS Omega, 2020, 5(27): 16595⁃16601.
|
23 |
Aris R, Gavalas G R. On the theory of reactions in continuous mixtures[J]. Philosophical Transactions of the Royal Society A: Mathematical,Physical and Engineering Sciences, 1966, 260(1112): 351⁃393.
|
24 |
Lababidi H M S, AlHumaidan F S. Modeling the hydrocracking kinetics of atmospheric residue in hydrotreating processes by the continuous lumping approach[J]. Energy & Fuels, 2011, 25(5): 1939⁃1949.
|
25 |
Chen Z Y, Feng S, Zhang L Z, et al. Molecular⁃level kinetic modelling of fluid catalytic cracking slurry oil hydrotreating[J]. Chemical Engineering Science, 2019, 195: 619⁃630.
|
26 |
Djokic M R, Muller H, Ristic N D, et al. Combined characterization using HT⁃GC× GC⁃FID and FT⁃ICR MS: A pyrolysis fuel oil case study[J]. Fuel Processing Technology, 2018, 182: 15⁃25.
|
27 |
He G, Zhou C L, Luo T, et al. Online optimization of fluid catalytic cracking process via a hybrid model based on simplified structure⁃oriented lumping and case⁃based reasoning[J]. Industrial & Engineering Chemistry Research, 2021, 60(1): 412⁃424.
|
28 |
Hudebine D, Verstraete J J. Molecular reconstruction of LCO gasoils from overall petroleum analyses[J]. Chemical Engineering Science, 2004, 59(22⁃23): 4755⁃4763.
|
29 |
Liguras D K, Allen D T. Structural models for catalytic cracking.1.Model compound reactions[J]. Industrial & Engineering Chemistry Research, 1989, 28(6): 665⁃673.
|
30 |
Hudebine D, Verstraete J J. Reconstruction of petroleum feedstocks by entropy maximization. Application to FCC gasolines[J]. Oil & Gas Science and Technology⁃Revue d’IFP Energies Nouvelles, 2011, 66(3): 437⁃460.
|
31 |
Mei H, Cheng H, Wang Z L, et al. Molecular characterization of petroleum fractions using state space representation and its application for predicting naphtha pyrolysis product distributions[J]. Chemical Engineering Science, 2017, 164: 81⁃89.
|
32 |
Cui C, Billa T, Zhang L Z, et al. Molecular representation of the petroleum gasoline fraction[J]. Energy & Fuels, 2018, 32(2): 1525⁃1533.
|
33 |
Bojkovic A, Dijkmans T, Dao Thi H, et al. Molecular reconstruction of hydrocarbons and sulfur⁃containing compounds in atmospheric and vacuum gas oils[J]. Energy & Fuels, 2021, 35(7): 5777⁃5788.
|
34 |
Chen Z Y, Guan D, Zhang X J, et al. A mass⁃temperature decoupled discretization strategy for large⁃scale molecular⁃level kinetic model[J]. Chemical Engineering Science, 2022, 249: 117348.
|
35 |
Pernalete C G, Ibanez J, Mendes P S F, et al. Hydrocracking of complex mixtures: From bulk properties, over fundamental kinetics to detailed product composition[J]. Catalysis Today, 2021, 378: 189⁃201.
|
36 |
Guan D, Chen Z Y, Chen X, et al. Molecular⁃level heavy petroleum hydrotreating modeling and comparison with high⁃resolution mass spectrometry[J]. Fuel, 2021, 297: 120792.
|
37 |
de Oliveira L P, Verstraete J J, Kolb M. Molecule⁃based kinetic modeling by Monte Carlo methods for heavy petroleum conversion[J]. Science China Chemistry, 2013, 56: 1608⁃1622.
|
38 |
Neurock M, Libanati C, Nigam A, et al. Monte Carlo simulation of complex reaction systems: Molecular structure and reactivity in modelling heavy oils[J]. Chemical Engineering Science, 1990, 45(8): 2083⁃2088.
|
39 |
Hudebine D, Verstraete J, Chapus T. Statistical reconstruction of gas oil cuts[J]. Oil & Gas Science and Technology⁃Revue d’IFP Energies Nouvelles, 2011, 66(3): 461⁃477.
|
40 |
Ren Y,Liao Z W, Sun J Y, et al. Molecular reconstruction:Recent progress toward composition modeling of petroleum fractions[J].Chemical Engineering Journal, 2019, 357: 761⁃775.
|
41 |
Guan D, Zhang L Z. Initial guess estimation and fast solving of petroleum complex molecular reconstruction model[J]. AIChE Journal, 2022, 68(10): 17782.
|
42 |
Zhao G Y, Yang M L, Du W Y, et al. A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha[J]. Chinese Journal of Chemical Engineering, 2022, 51: 153⁃167.
|
43 |
Quann R J, Jaffe S B. Structure⁃oriented lumping: Describing the chemistry of complex hydrocarbon mixtures[J]. Industrial & Engineering Chemistry Research, 1992, 31(11): 2483⁃2497.
|
44 |
Jaffe S B, Freund H, Olmstead W N. Extension of structure⁃oriented lumping to vacuum residua[J].Industrial & Engineering Chemistry Research, 2005, 44(26): 9840⁃9852.
|
45 |
Chen J C, Fang Z, Qiu T. Molecular reconstruction model based on structure oriented lumping and group contribution methods[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1677⁃1683.
|
46 |
Guan Y M, Guan D, Zhang C, et al. Diesel molecular composition and blending modeling based on SU⁃BEM framework[J]. Petroleum Science, 2022, 19(2): 839⁃847.
|
47 |
Song W J, Mahalec V, Long J, et al. Modeling the hydrocracking process with deep neural networks[J]. Industrial & Engineering Chemistry Research, 2020, 59(7): 3077⁃3090.
|
48 |
Yu W X, Ye L, Qin X L, et al. Reaction behaviors of polycyclic aromatic hydrocarbon molecules in a diesel hydro⁃upgrading process based on the molecular⁃level reaction kinetic model[J]. Industrial & Engineering Chemistry Research, 2022, 61(17): 5723⁃5733.
|