Knowledge expression, numerical modeling and optimization application of ethylene thermal cracking: From the perspective of intelligent manufacturing

doi:10.1016/j.cjche.2021.03.033

摘要/Abstract

摘要： Applications of process systems engineering (PSE) in plants and enterprises are boosting industrial reform from automation to digitization and intelligence. For ethylene thermal cracking, knowledge expression, numerical modeling and intelligent optimization are key steps for intelligent manufacturing. This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking; introduces the frameworks, methods and algorithms that have been proposed over the past 10 years and discusses the advantages, limitations and applications in industrial practice. An entire set of molecular-level modeling approaches from feedstocks to products, including feedstock molecular reconstruction, reaction-network auto-generation and cracking unit simulation are described. Multi-level control and optimization methods are exhibited, including at the operational, cycle, plant and enterprise level. Relevant software packages are introduced. Finally, an outlook in terms of future directions is presented.

关键词: Ethylene thermal cracking, PSE, Intelligent manufacturing, Molecularization and digitization, Modeling and optimization

Abstract: Applications of process systems engineering (PSE) in plants and enterprises are boosting industrial reform from automation to digitization and intelligence. For ethylene thermal cracking, knowledge expression, numerical modeling and intelligent optimization are key steps for intelligent manufacturing. This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking; introduces the frameworks, methods and algorithms that have been proposed over the past 10 years and discusses the advantages, limitations and applications in industrial practice. An entire set of molecular-level modeling approaches from feedstocks to products, including feedstock molecular reconstruction, reaction-network auto-generation and cracking unit simulation are described. Multi-level control and optimization methods are exhibited, including at the operational, cycle, plant and enterprise level. Relevant software packages are introduced. Finally, an outlook in terms of future directions is presented.

Key words: Ethylene thermal cracking, PSE, Intelligent manufacturing, Molecularization and digitization, Modeling and optimization

Kexin Bi, Shuyuan Zhang, Chen Zhang, Haoran Li, Xinye Huang, Haoyu Liu, Tong Qiu. Knowledge expression, numerical modeling and optimization application of ethylene thermal cracking: From the perspective of intelligent manufacturing[J]. 中国化学工程学报, 2021, 38(10): 1-17.

参考文献

[1] I. Amghizar, L.A. Vandewalle, K.M. Van Geem, G.B. Marin, New trends in olefin production, Engineering 3 (2017) 171-178
[2] S.M. Sadrameli, Thermal/catalytic cracking of hydrocarbons for the production of olefins: a state-of-the-art review I: Thermal cracking review, Fuel 140 (2015) 102-115
[3] J. Ge, F. Wang, H.X. Sun, L.L. Fu, M.W. Sun, Research on the maturity of big data management capability of intelligent manufacturing enterprise, Syst. Res. Behav. Sci. 37 (4) (2020) 646-662
[4] H. Zhang, X. Zhao, The review of lightolefins production technology, Chem. Ind. 32 (2014) 17-21
[5] R. Al, C.R. Behera, K.V. Gernaey, G. Sin, Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty, Comput. Chem. Eng. 143 (2020) 107118
[6] O.J. Fisher, N.J. Watson, J.E. Escrig, R. Witt, L. Porcu, D. Bacon, M. Rigley, R.L. Gomes, Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems, Comput. Chem. Eng. 140 (2020) 106881
[7] Y. Chen, Z.H. Yuan, B.Z. Chen, Process optimization with consideration of uncertainties—An overview, Chin. J. Chem. Eng. 26 (8) (2018) 1700-1706
[8] S. Chen, Analysis of current status of steam cracking feed production and measures for maximization of steam cracking feed, China Pet. Process. Petrochem. Technol. 8 (2005) 41-46
[9] A. Hinkle, E.J. Shin, M.W. Liberatore, A.M. Herring, M. Batzle, Correlating the chemical and physical properties of a set of heavy oils from around the world, Fuel 87 (13-14) (2008) 3065-3070
[10] K.J. Li, B.A. Akeredolu, A.M. Renehan, Y. Yang, M. Batzle, R.J. Evans, J.R. Dorgan, M.W. Liberatore, A.M. Herring, Correlation of chemical and physical properties of an Alaska heavy oil from the Ugnu formation, Fuel 103 (2013) 843-849
[11] D. Molina V, U.N. Uribe, J. Murgich, Correlations between SARA fractions and physicochemical properties with ¹H NMR spectra of vacuum residues from Colombian crude oils, Fuel 89 (1) (2010) 185-192
[12] M.R. Riazi, T.A. Al-Sahhaf, Physical properties of heavy petroleum fractions and crude oils, Fluid Phase Equilib. 117 (1-2) (1996) 217-224
[13] O. Alomair, M. Jumaa, A. Alkoriem, M. Hamed, Heavy oil viscosity and density prediction at normal and elevated temperatures, J. Petroleum Explor. Prod. Technol. 6 (2) (2016) 253-263
[14] K.X. Bi, T. Qiu, Elsevier, Amsterdam (2018) 2005-2010
[15] K.X. Bi, T. Qiu, An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method, Chin. J. Chem. Eng. 27 (8) (2019) 1888-1894
[16] D.S. Stratiev, S. Sotirov, I. Shishkova, A. Nedelchev, I. Sharafutdinov, A. Vely, M. Mitkova, D. Yordanov, E. Sotirova, V. Atanassova, K. Atanassov, D.D. Stratiev, N. Rudnev, S. Ribagin, Investigation of relationships between bulk properties and fraction properties of crude oils by application of the intercriteria analysis, Petroleum Sci. Technol. 34 (13) (2016) 1113-1120
[17] Z.D. Wang, M. Fingas, K. Li, Fractionation of a light crude oil and identification and quantitation of aliphatic, aromatic, and biomarker compounds by GC-FID and GC-MS, Part I, J. Chromatogr. Sci. 32 (1994) 361-366
[18] Z.D. Wang, M. Fingas, K. Li, Fractionation of a light crude oil and identification and quantitation of aliphatic, aromatic, and biomarker compounds by GC-FID and GC-MS, part II, J. Chromatogr. Sci. 32 (9) (1994) 367-382
[19] A. Giri, M. Coutriade, A. Racaud, K. Okuda, J. Dane, R.B. Cody, J.F. Focant, Molecular characterization of volatiles and petrochemical base oils by photo-ionization GC×GC-TOF-MS, Anal. Chem. 89 (10) (2017) 5395-5403
[20] L. Zhang, B. Chen, Applications of shannon's entropy theory to naphtha pyrolysis simulation, Chem. Eng. Technol. 35 (2) (2012) 281-286
[21] D.S. Stratiev, I.K. Shishkova, E. Nikolaychuk, I.M. Sharafutdinov, A. Vely, M. Mitkova, D. Yordanov, N. Rudnev, Relationship of the aromatic structural types in vacuum gas oil to empirical correlations based on bulk properties, Petroleum Sci. Technol. 34 (9) (2016) 860-865
[22] D. Stratiev, I. Shishkova, E. Nikolaychuk, W. Ijlstra, M. Caillot, Feed properties effect on the performance of vacuum residue ebullated bed H-Oil hydrocracking, Refinery Technology (4) (2019) 112-117
[23] D. Stratiev, I. Shishkova, R. Dinkov, R. Nikolova, M. Mitkova, K. Stanulov, R. Sharpe, C.A. Russell, A. Obryvalina, R. Telyashev, Reactivity and stability of vacuum residual oils in their thermal conversion, Fuel 123 (2014) 133-142
[24] D. Stratiev, I. Shishkova, T. Tsaneva, M. Mitkova, D. Yordanov, Investigation of relations between properties of vacuum residual oils from different origin, and of their deasphalted and asphaltene fractions, Fuel 170 (2016) 115-129
[25] D.M. Jewell, J.H. Weber, J.W. Bunger, H. Plancher, D.R. Latham, Ion-exchange, coordination, and adsorption chromatographic separation of heavy-end petroleum distillates, Anal. Chem. 44 (8) (1972) 1391-1395
[26] 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 (3) (1994) 576-580
[27] 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
[28] J.J. Verstraete, P. Schnongs, H. Dulot, D. Hudebine, Molecular reconstruction of heavy petroleum residue fractions, Chem. Eng. Sci. 65 (1) (2010) 304-312
[29] L.P. de Oliveira, A. Trujillo 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
[30] B. Peng, Molecular modelling of petroleum processes, University of Manchester, Ph.D. Thesis, 1999.
[31] M.I. Ahmad, N. Zhang, M. Jobson, Molecular components-based representation of petroleum fractions, Chem. Eng. Res. Des. 89 (4) (2011) 410-420
[32] 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 (12) (2010) 3174-3188
[33] S.P. Pyl, Z. Hou, K.M. van Geem, M.F. Reyniers, G.B. Marin, M.T. Klein, Modeling the composition of crude oil fractions using constrained homologous series, Ind. Eng. Chem. Res. 50 (18) (2011) 10850-10858
[34] 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
[35] L. Pereira de Oliveira, J.J. Verstraete, M. Kolb, A Monte Carlo modeling methodology for the simulation of hydrotreating processes, Chem. Eng. J. 207-208 (2012) 94-102
[36] 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
[37] H. Mei, H. Cheng, Z.L. Wang, J.L. Li, Molecular characterization of petroleum fractions using state space representation and its application for predicting naphtha pyrolysis product distributions, Chem. Eng. Sci. 164 (2017) 81-89
[38] D. Stratiev, I. Shishkova, I. Tankov, A. Pavlova, Challenges in characterization of residual oils. A review, J. Petroleum Sci. Eng. 178 (2019) 227-250
[39] 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
[40] S.B. Jaffe, H. Freund, W.N. Olmstead, Extension of structure-oriented lumping to vacuum residua, Ind. Eng. Chem. Res. 44 (2005) 9840-9852
[41] L. Tian, J. Wang, B. Shen, J. Liu, Building a kinetic model for steam cracking by the method of structure-oriented lumping, Energy Fuels. 24 (2010) 4380-4386
[42] 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
[43] Y.B. Pan, B.L. Yang, X.W. 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] 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
[45] S.M. Sadrameli, Thermal/catalytic cracking of liquid hydrocarbons for the production of olefins: a state-of-the-art review II: Catalytic cracking review, Fuel 173 (2016) 285-297
[46] M. Fakhroleslam, S.M. Sadrameli, Thermal/catalytic cracking of hydrocarbons for the production of olefins; A state-of-the-art review III: Process modeling and simulation, Fuel 252 (2019) 553-566
[47] Z. Belohlav, P. Zamostny, T. Herink, The kinetic model of thermal cracking for olefins production, Chem. Eng. Process.: Process. Intensif. 42 (6) (2003) 461-473
[48] F.O. Rice, K.F. Herzfeld, The thermal decomposition of organic compounds from the standpoint of free radicals. vi. The mechanism of some chain reactions. Selected Readings in Chemical Kinetics, Elsevier, Amsterdam, (1967) 154-170.
[49] M.L. Poutsma, Fundamental reactions of free radicals relevant to pyrolysis reactions, J. Anal. Appl. Pyrolysis 54 (1-2) (2000) 5-35
[50] K.X. Bi, C. Zhang, T. Qiu, An ingenious characterization of reaction network using sub-network reconstruction, Comput. Chem. Eng. 134 (2020) 106695
[51] Z. Fang, T. Qiu, B.Z. Chen, Improvement of ethylene cracking reaction network with network flow analysis algorithm, Comput. Chem. Eng. 91 (2016) 182-194
[52] F. Hua, Z. Fang, T. Qiu, Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling, Chin. J. Chem. Eng. 26 (12) (2018) 2562-2572
[53] P.E. Savage, Mechanisms and kinetics models for hydrocarbon pyrolysis, J. Anal. Appl. Pyrolysis 54 (1-2) (2000) 109-126
[54] K.M. Sundaram, G.F. Froment, Modeling of thermal cracking kinetics—I: Thermal cracking of ethane, propane and their mixtures, Chem. Eng. Sci. 32 (6) (1977) 601-608
[55] K.M. Sundaram, G.F. Froment, Modeling of thermal cracking kinetics—II: Cracking of iso-butane, of n-butane and of mixtures ethane—propane—n-butane, Chem. Eng. Sci. 32 (1977) 609-617
[56] P.S. van Damme, G.F. Froment, W.B. Balthasar, Scaling up of naphtha cracking coils, Ind. Eng. Chem. Proc. Des. Dev. 20 (2) (1981) 366-376
[57] P. Kumar, D. Kunzru, Modeling of naphtha pyrolysis, Ind. Eng. Chem. Proc. Des. Dev. 24 (3) (1985) 774-782
[58] X. Gao, B. Chen, X. He, Estimation of the selectivities of the first-order reaction in naphtha pyrolysis, Comput. 22 (2005) 1119-1122
[59] A. Kossiakoff, F.O. Rice, Thermal decomposition of hydrocarbons, resonance stabilization and isomerization of free Radicals1, J. Am. Chem. Soc. 65 (4) (1943) 590-595
[60] K.M. Sundaram, G.F. Froment, Modeling of thermal cracking kinetics. 3. radical mechanisms for the pyrolysis of simple paraffins, olefins, and their mixtures, Ind. Eng. Chem. Fund. 17 (3) (1978) 174-182
[61] D.S. Aribike, A. Susu, Kinetics and mechanism of the thermal cracking of n-heptane, Thermochim Acta 127 (1988) 247-258
[62] E. Joo, S. Park, M. Lee, Pyrolysis reaction mechanism for industrial naphtha cracking furnaces, Ind. Eng. Chem. Res. 40 (11) (2001) 2409-2415
[63] K.M. van Geem, M.F. Reyniers, G.B. Marin, J. Song, W.H. Green, D.M. Matheu, Automatic reaction network generation using RMG for steam cracking of n-hexane, AIChE J. 52 (2) (2006) 718-730
[64] P. Zhao, S.M. Nackman, C.K. Law, On the application of betweenness centrality in chemical network analysis: Computational diagnostics and model reduction, Combust. Flame 162 (8) (2015) 2991-2998
[65] D.L. Chapman, L.K. Underhill, LV.—The interaction of chlorine and hydrogen. The influence of mass, J. Chem. Soc. Trans. 103 (1913) 496-508
[66] T. Turanyi, A.S. Tomlin, M.J. Pilling, On the error of the quasi-steady-state approximation, J. Phys. Chem. 97 (1) (1993) 163-172
[67] E. Ranzi, M. Dente, A. Goldaniga, G. Bozzano, T. Faravelli, Lumping procedures in detailed kinetic modeling of gasification, pyrolysis, partial oxidation and combustion of hydrocarbon mixtures, Prog. Energy Combust. Sci. 27 (1) (2001) 99-139
[68] T. Turányi, T. Bérces, S. Vajda, Reaction rate analysis of complex kinetic systems, Int. J. Chem. Kinetics 21 (2) (1989) 83-99
[69] G. Simm, A.C. Vaucher, M. Reiher, Exploration of reaction pathways and chemical transformation networks, J. Phys. Chem. A 123 (2) (2019) 385-399
[70] S. Vajda, P. Valko, T. Turányi, Principal component analysis of kinetic models, Int. J. Chem. Kinetics 17 (1) (1985) 55-81
[71] S.H. Lam, D.A. Goussis, Understanding complex chemical kinetics with computational singular perturbation, Symp. Int. Combust. 22 (1) (1989) 931-941
[72] S.H. Lam, D.A. Goussis, The CSP method for simplifying kinetics, Int. J. Chem. Kinetics 26 (4) (1994) 461-486
[73] Z. Till, T. Varga, J. Sója, N. Miskolczi, T. Chován, Reduction of lumped reaction networks based on global sensitivity analysis, Chem. Eng. J. 375 (2019) 121920
[74] T.F. Lu, C.K. Law, A directed relation graph method for mechanism reduction, Proc. Combust. Inst. 30 (1) (2005) 1333-1341
[75] J.A.M. Kuipers, W.P.M. van Swaaij, Application of computational fluid dynamics to chemical reaction engineering, Rev. Chem. Eng. 13 (3) (1997) 1-118
[76] G.F. Froment, Thermal cracking for olefins production. Fundamentals and their application to industrial problems, Chem. Eng. Sci. 36 (8) (1981) 1271-1282
[77] G.F. Froment, Kinetics and reactor design in the thermal cracking for olefins production, Chem. Eng. Sci. 48 (1) (1993) 199.
[78] K.M. van Geem, R. Žajdlík, M.F. Reyniers, G.B. Marin, Dimensional analysis for scaling up and down steam cracking coils, Chem. Eng. J. 134 (1-3) (2007) 3-10
[79] M. Dente, E. Ranzi, A.G. Goossens, Detailed prediction of olefin yields from hydrocarbon pyrolysis through a fundamental simulation model (SPYRO), Comput. Chem. Eng. 3 (1-4) (1979) 61-75
[80] P.J. Clymans, G.F. Froment, Computer-generation of reaction paths and rate equations in the thermal cracking of normal and branched paraffins, Comput. Chem. Eng. 8 (2) (1984) 137-142
[81] L. Zhang, H.Z. Wang, T. Qiu, B.Z. Chen, Simulation and optimization of multi-period steam cracking process, in: Advances in Petrochemicals, InTech, Rijeka, Croatia, 2015.
[82] C.W. Gear, L.R. Petzold, ODE methods for the solution of differential/algebraic systems, SIAM J. Numer. Anal. 21 (4) (1984) 716-728
[83] X.P. Wang, L.X. Tang, Multiobjective operation optimization of naphtha pyrolysis process using parallel differential evolution, Ind. Eng. Chem. Res. 52 (40) (2013) 14415-14428
[84] M. Sedighi, K. Keyvanloo, J. Towfighi, Modeling of thermal cracking of heavy liquid hydrocarbon: Application of kinetic modeling, artificial neural network, and neuro-fuzzy models, Ind. Eng. Chem. Res. 50 (3) (2011) 1536-1547
[85] B.R. Hough, D.A.C. Beck, D.T. Schwartz, J. Pfaendtner, Application of machine learning to pyrolysis reaction networks: Reducing model solution time to enable process optimization, Comput. Chem. Eng. 104 (2017) 56-63
[86] A. Habibi, B. Merci, G.J. Heynderickx, Impact of radiation models in CFD simulations of steam cracking furnaces, Comput. Chem. Eng. 31 (11) (2007) 1389-1406
[87] P.A. Reyniers, C.M. Schietekat, B. Kong, A. Passalacqua, K.M. van Geem, G.B. Marin, CFD simulations of industrial steam cracking reactors: turbulence-chemistry interaction and dynamic zoning, Ind. Eng. Chem. Res. 56 (51) (2017) 14959-14971
[88] D.J. van Cauwenberge, C.M. Schietekat, J. Floré, K.M. van Geem, G.B. Marin, CFD-based design of 3D pyrolysis reactors: RANS vs. LES, Chem. Eng. J. 282 (2015) 66-76
[89] D.J. van Cauwenberge, L.A. Vandewalle, P.A. Reyniers, K.M. van Geem, G.B. Marin, J. Floré, Periodic reactive flow simulation: Proof of concept for steam cracking coils, AIChE J. 63 (5) (2017) 1715-1726
[90] H.D. Zhu, L. Zhang, Simulation of dichloroethane pyrolysis process, Chem. React. Eng. Technol. 11 (1995) 251-258
[91] W.E. Lobo, J.E. Evans, Heat transfer in the radiant section of petroleum heaters, Trans. Am. Inst. Chem. Eng. 35 (1939) 748-788.
[92] H.C. Hottel, A.F. Sarofim, Radiative transfer, McGraw-Hill, New York, USA, 1967.
[93] W.G. Zhou, T. Qiu, Zone modeling of radiative heat transfer in industrial furnaces using adjusted Monte-Carlo integral method for direct exchange area calculation, Appl. Therm. Eng. 81 (2015) 161-167
[94] F. Hua, Z. Fang, T. Qiu, Recirculation and reaction hybrid intelligent modeling and simulation for industrial ethylene cracking furnace, CIESC J. 69 (2018) 923-930
[95] G.H. Hu, H.G. Wang, F. Qian, K.M. van Geem, C.M. Schietekat, G.B. Marin, Coupled simulation of an industrial naphtha cracking furnace equipped with long-flame and radiation burners, Comput. Chem. Eng. 38 (2012) 24-34
[96] G. Hu, C.M. Schietekat, Y. Zhang, F. Qian, G. Heynderickx, K.M. Van Geem, G.B. Marin, Impact of radiation models in coupled simulations of steam cracking furnaces and reactors, Ind. Eng. Chem. Res. 54 (2015) 2453-2465
[97] J. Towfighi, M. Sadrameli, A. Niaei, Coke formation mechanisms and coke inhibiting methods in pyrolysis furnaces, J. Chem. Eng. Japan 35 (10) (2002) 923-937
[98] H.Y. Cai, A. Krzywicki, M.C. Oballa, Coke formation in steam crackers for ethylene production, Chem. Eng. Process.: Process. Intensif. 41 (3) (2002) 199-214
[99] J. Lahaye, P. Badie, J. Ducret, Mechanism of carbon formation during steamcracking of hydrocarbons, Carbon 15 (2) (1977) 87-93
[100] F.D. Kopinke, G. Zimmermann, G.C. Reyniers, G.F. Froment, Relative rates of coke formation from hydrocarbons in steam cracking of naphtha. 3. Aromatic hydrocarbons, Ind. Eng. Chem. Res. 32 (11) (1993) 2620-2625
[101] F.D. Kopinke, G. Zimmermann, S. Nowak, On the mechanism of coke formation in steam cracking—conclusions from results obtained by tracer experiments, Carbon 26 (2) (1988) 117-124
[102] L.F. Albright, J.C. Marek, Mechanistic model for formation of coke in pyrolysis units producing ethylene, Ind. Eng. Chem. Res. 27 (5) (1988) 755-759
[103] K.M.V. Geem, I. Dhuyvetter, S. Prokopiev, M.F. Reyniers, D. Viennet, G.B. Marin, Coke formation in the transfer line exchanger during steam cracking of hydrocarbons, Ind. Eng. Chem. Res. 48 (23) (2009) 10343-10358
[104] G.J. Heynderickx, E.M. Schools, G.B. Marin, Coke combustion and gasification kinetics in ethane steam crackers, AIChE J. 51 (5) (2005) 1415-1428
[105] S.H. Symoens, N. Olahova, A.E. Muñoz Gandarillas, H. Karimi, M.R. Djokic, M.F. Reyniers, G.B. Marin, K.M. van Geem, State-of-the-art of coke formation during steam cracking: anti-coking surface technologies, Ind. Eng. Chem. Res. 57 (48) (2018) 16117-16136
[106] R. Karimzadeh, H.R. Godini, M. Ghashghaee, Flowsheeting of steam cracking furnaces, Chem. Eng. Res. Des. 87 (1) (2009) 36-46
[107] G.H. Hu, Y. Zhang, W.L. Du, J. Long, F. Qian, Zone method based coupled simulation of industrial steam cracking furnaces, Energy 172 (2019) 1098-1116
[108] Z. Fang, T. Qiu, W.G. Zhou, Coupled simulation of recirculation zonal firebox model and detailed kinetic reactor model in an industrial ethylene cracking furnace, Chin. J. Chem. Eng. 25 (8) (2017) 1091-1100
[109] Y.K. Jin, J.L. Li, W.L. Du, F. Qian, Multi-objective optimization of pseudo-dynamic operation of naphtha pyrolysis by a surrogate model, Chem. Eng. Technol. 38 (5) (2015) 900-906
[110] Y. Zhao, X. He, T. Qiu, B. Chen, J. Li, L. Tian, Y. Wang, Development and application of ethylene cracking simulation platform, Comput. Appl. Chem. 23 (2006) 1065-1068
[111] L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank Citation Ranking: Bringing Order to the Web, Stanford InfoLab, 1999.
[112] I. Koch, Petri nets - A mathematical formalism to analyze chemical reaction networks, Mol Inform 29 (12) (2010) 838-843
[113] A. Niaei, J. Towfighi, A.R. Khataee, K. Rostamizadeh, The use of ANN and the mathematical model for prediction of the main product yields in the thermal cracking of naphtha, Petroleum Sci. Technol. 25 (8) (2007) 967-982
[114] P.P. Plehiers, S.H. Symoens, I. Amghizar, G.B. Marin, C.V. Stevens, K.M. van Geem, Artificial intelligence in steam cracking modeling: a deep learning algorithm for detailed effluent prediction, Engineering 5 (6) (2019) 1027-1040
[115] 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
[116] Y.M. Han, H. Wu, Z.Q. Geng, Q.X. Zhu, X.B. Gu, B. Yu, Review: Energy efficiency evaluation of complex petrochemical industries, Energy 203 (2020) 117893
[117] Z.Q. Geng, Y.H. Zhang, C.F. Li, Y.M. Han, Y.F. Cui, B. Yu, Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature, Energy 194 (2020) 116851
[118] Y.M. Han, R.D. Zhou, Z.Q. Geng, J. Bai, B. Ma, J.Z. Fan, A novel data envelopment analysis cross-model integrating interpretative structural model and analytic hierarchy process for energy efficiency evaluation and optimization modeling: Application to ethylene industries, J. Clean. Prod. 246 (2020) 118965
[119] Z. Wang, Y.M. Han, C.F. Li, Z.Q. Geng, J.Z. Fan, Input-output networks considering graphlet-based analysis for production optimization: Application in ethylene plants, J. Clean. Prod. 278 (2021) 123955
[120] R.W.J. Robertson, D. Hanesian, An optimization study of the pyrolysis of ethane in a tubular reactor, Ind. Eng. Chem. Proc. Des. Dev. 14 (3) (1975) 216-221
[121] X.Y. Nian, Z.L. Wang, F. Qian, A hybrid algorithm based on differential evolution and group search optimization and its application on ethylene cracking furnace, Chin. J. Chem. Eng. 21 (5) (2013) 537-543
[122] S.R. Nabavi, G.P. Rangaiah, A. Niaei, D. Salari, Multiobjective optimization of an industrial LPG thermal cracker using a first principles model, Ind. Eng. Chem. Res. 48 (21) (2009) 9523-9533
[123] S.R. Nabavi, G.P. Rangaiah, A. Niaei, D. Salari, Design optimization of an LPG thermal cracker for multiple objectives, Int. J. Chem. React. Eng. 9 (2011) A80
[124] C. Li, Q. Zhu, Z. Geng, Geng, Multi-objective particle swarm optimization hybrid algorithm: an application on industrial cracking furnace, Ind. Eng. Chem. Res. 46 (11) (2007) 3602-3609
[125] L.R. Xia, J.Z. Chu, Z.Q. Geng, A multiswarm competitive particle swarm algorithm for optimization control of an ethylene cracking furnace, Appl. Artif. Intell. 28 (1) (2014) 30-46
[126] Z.Q. Geng, Z. Wang, Q.X. Zhu, Y.M. Han, Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm, Chem. Eng. Sci. 153 (2016) 21-33
[127] C.M. Ying, B. Joseph, Performance and stability analysis of LP-MPC and QP-MPC cascade control systems, AIChE J. 45 (7) (1999) 1521-1534
[128] G. Emoto, Y. Ota, H. Matsuo, M. Ogawa, D.B. Raven, R.F. Preston, J.S. Ayala, Integrated advanced control and closed-loop real-time optimization of an olefins plant. Advanced Control of Chemical Processes 1994, Elsevier, Amsterdam, (1994) 95-100.
[129] R.M. Ansari, H. Imran, A.S. Hunaidy, Integration of Real-Time Optimization and Model-Predictive Control: Application to Refinery Processes, Int. J. Control Sci. Eng. 10 (2020) 1-10
[130] R. Nath, Z. Alzein, On-line dynamic optimization of olefins plants, Comput. Chem. Eng. 24 (2-7) (2000) 533-538
[131] F. Manenti, G. Buzzi-Ferraris, S. Pierucci, M. Rovaglio, H. Gulati, Elsevier, Amsterdam (2011) 452-456
[132] M.L. Darby, M. Nikolaou, J. Jones, D. Nicholson, RTO: an overview and assessment of current practice, J. Process. Control. 21 (6) (2011) 874-884
[133] A. Marchetti, B. Chachuat, D. Bonvin, Modifier-adaptation methodology for real-time optimization, Ind. Eng. Chem. Res. 48 (13) (2009) 6022-6033
[134] A. Marchetti, G. François, T. Faulwasser, D. Bonvin, Modifier adaptation for real-time optimization—methods and applications, Processes 4 (4) (2016) 55
[135] N. Abdullah, M.A. Mohd Razali, M.H.O. Ahmed, M.Z. Nuawi, M.M. Mustafa, Z. Mohd Nopiah, A. Mohamed, A.B. Mohamad, Neural network adaptive real-time optimizing control of industrial processes, Chem. Eng. Res. Bull. 19 (2017) 129
[136] J. Matias, J. Jäschke, Using a neural network for estimating plant gradients in real-time optimization with modifier adaptation, IFAC-PapersOnLine 52 (1) (2019) 808-813
[137] B. Beykal, F. Boukouvala, C.A. Floudas, N. Sorek, H. Zalavadia, E. Gildin, Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operations, Comput. Chem. Eng. 114 (2018) 99-110
[138] B. Beykal, F. Boukouvala, C.A. Floudas, E.N. Pistikopoulos, Optimal design of energy systems using constrained grey-box multi-objective optimization, Comput Chem Eng 116 (2018) 488-502
[139] V.J. And, I.E. Grossmann, Cyclic scheduling of continuous parallel-process units with decaying performance, AIChE J. 44 (7) (1998) 1623-1636
[140] I. Harjunkoski, C.T. Maravelias, P. Bongers, P.M. Castro, S. Engell, I.E. Grossmann, J. Hooker, C. Méndez, G. Sand, J. Wassick, Scope for industrial applications of production scheduling models and solution methods, Comput. Chem. Eng. 62 (2014) 161-193
[141] C.A. Méndez, J. Cerdá, I.E. Grossmann, I. Harjunkoski, M. Fahl, State-of-the-art review of optimization methods for short-term scheduling of batch processes, Comput. Chem. Eng. 30 (6-7) (2006) 913-946
[142] E.P. Schulz, J.A. Bandoni, M.S. Diaz, Optimal shutdown policy for maintenance of cracking furnaces in ethylene plants, Ind. Eng. Chem. Res. 45 (8) (2006) 2748-2757
[143] X.D. Gao, B.Z. Chen, X.R. He, T. Qiu, J.C. Li, C.M. Wang, L.J. Zhang, Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP, Comput. Chem. Eng. 32 (11) (2008) 2801-2811
[144] Y.K. Jin, J.L. Li, W.L. Du, F. Qian, Integrated operation and cyclic scheduling optimization for an ethylene cracking furnaces system, Ind. Eng. Chem. Res. 54 (15) (2015) 3844-3854
[145] L.J. Su, L.X. Tang, I.E. Grossmann, Scheduling of cracking production process with feedstocks and energy constraints, Comput. Chem. Eng. 94 (2016) 92-103
[146] C.W. Liu, J. Zhang, Q. Xu, K. Li, Cyclic scheduling for best profitability of industrial cracking furnace system, Comput. Chem. Eng. 34 (4) (2010) 544-554
[147] C. Zhao, C.W. Liu, Q. Xu, Dynamic scheduling for ethylene cracking furnace system, Ind. Eng. Chem. Res. 50 (21) (2011) 12026-12040
[148] C. Zhao, C.W. Liu, Q. Xu, Cyclic scheduling for ethylene cracking furnace system with consideration of secondary ethane cracking, Ind. Eng. Chem. Res. 49 (12) (2010) 5765-5774
[149] S.J. Zhang, S.J. Wang, Q. Xu, Emission constrained dynamic scheduling for ethylene cracking furnace system, Ind. Eng. Chem. Res. 56 (5) (2017) 1327-1340
[150] M. Chen, Q. Xu, Optimal scheduling for olefin plant furnace system with consideration of inherent process upset reduction, Comput. Chem. Eng. 126 (2019) 157-167
[151] K.J. Yu, L. While, M. Reynolds, X. Wang, Z.L. Wang, Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization, Comput. Chem. Eng. 99 (2017) 314-324
[152] K.J. Yu, X. Wang, Z.L. Wang, Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization, Knowl. - Based Syst. 96 (2016) 156-170
[153] K.J. Yu, X. Wang, Z.L. Wang, Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization, Chemom. Intell. Lab. Syst. 146 (2015) 198-210
[154] K.J. Yu, L. While, M. Reynolds, X. Wang, J.J. Liang, L. Zhao, Z.L. Wang, Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization, Energy 148 (2018) 469-481
[155] Z.H. Wang, Y.P. Feng, G. Rong, Synchronized scheduling approach of ethylene plant production and naphtha oil inventory management, Ind. Eng. Chem. Res. 53 (15) (2014) 6477-6499
[156] Y. Zhang, Y. Feng, G. Rong, New robust optimization approach induced by flexible uncertainty set: Optimization under continuous uncertainty, Ind. Eng. Chem. Res. 56 (2017) 270-287
[157] I. Tjoa, Ethylene plant scheduling system based on a MINLP formulation, Comput. Chem. Eng. 21 (1-2) (1997) S1073-S1077
[158] Z.H. Wang, Z.K. Li, Y.P. Feng, G. Rong, Integrated short-term scheduling and production planning in an ethylene plant based on Lagrangian decomposition, Can. J. Chem. Eng. 94 (9) (2016) 1723-1739
[159] H. Zhao, M.G. Ierapetritou, G. Rong, Production planning optimization of an ethylene plant considering process operation and energy utilization, Comput. Chem. Eng. 87 (2016) 1-12
[160] H. Zhao, M.G. Ierapetritou, N.K. Shah, G. Rong, Integrated model of refining and petrochemical plant for enterprise-wide optimization, Comput. Chem. Eng. 97 (2017) 194-207
[161] H. Kwon, K. Tak, J.H. Cho, J. Kim, I. Moon, Integrated decision support model for hedge trading and production planning in the petrochemical industry, Ind. Eng. Chem. Res. 56 (5) (2017) 1267-1277
[162] X. Yang, Q. Xu, K. Li, Flare minimization strategy for ethylene plants, Chem. Eng. Technol. 33 (7) (2010) 1059-1065
[163] Q. Xu, X.T. Yang, C.W. Liu, K. Li, H.H. Lou, J.L. Gossage, Chemical plant flare minimization via plantwide dynamic simulation, Ind. Eng. Chem. Res. 48 (7) (2009) 3505-3512
[164] Y.C. Zhao, J. Zhang, T. Qiu, J.S. Zhao, Q. Xu, Flare minimization during start-ups of an integrated cryogenic separation system via dynamic simulation, Ind. Eng. Chem. Res. 53 (4) (2014) 1553-1562
[165] G. Song, T. Qiu, B.Z. Chen, in: Flare minimization of ethylene plant start-up via resource-task network approach, Elsevier, Amsterdam, 2015, pp. 1871–1876.
[166] H. Dinh, S.J. Zhang, Y.L. Xu, Q. Xu, F. Eljack, M. El-Halwagi, Generic approach of using dynamic simulation for industrial emission reduction under abnormal operations: Scenario study of an ethylene plant start-up, Ind. Eng. Chem. Res. 53 (39) (2014) 15089-15100
[167] X.T. Yang, Q. Xu, C. Zhao, K. Li, H.H. Lou, Pressure-driven dynamic simulation for improving the performance of a multistage compression system during plant startup, Ind. Eng. Chem. Res. 48 (20) (2009) 9195-9203
[168] S. Guang, Z. Yongchen, Z. Shengfu, Q. Tong, Z. Jinsong, Start-up process of crack gas compressorsvia dynamic simulation, CIESC J. 65 (2014) 4839-4843
[169] S. Zhang, Y. Zhao, T. Qiu, G. Song, J. Zhao, A method to refine the start-up scheme of compression and refrigeration systems in ethylene plants via dynamic simulation, Chin. J. Chem. Eng. 24 (2016) 1007-1012
[170] X. Yang, Q. Xu, Product loss minimization of an integrated cryogenic separation system, Chem. Eng. Technol. 35 (4) (2012) 635-645
[171] Y.L. Xu, H. Dinh, Q. Xu, F.T. Eljack, M.M. El-Halwagi, Flare minimization for an olefin plant shutdown via plant-wide dynamic simulation, J. Clean. Prod. 254 (2020) 120129
[172] T. Billa, S.R. Horton, M. Sahasrabudhe, C. Saravanan, Z. Hou, P. Agarwal, J. Lucio-Vega, M.T. Klein, Enhancing the value of detailed kinetic models through the development of interrogative software applications, Comput. Chem. Eng. 106 (2017) 512-528
[173] M.W.M. van Goethem, F.I. Kleinendorst, C. van Leeuwen, N. van Velzen, Equation-based SPYRO^® model and solver for the simulation of the steam cracking process, Comput. Chem. Eng. 25 (4-6) (2001) 905-911
[174] P.A. Willems, G.F. Froment, Kinetic modeling of the thermal cracking of hydrocarbons. 1. Calculation of frequency factors, Ind. Eng. Chem. Res. 27 (11) (1988) 1959-1966
[175] L.P. Hillewaert, J.L. Dierickx, G.F. Froment, Computer generation of reaction schemes and rate equations for thermal cracking, AIChE J. 34 (1) (1988) 17-24
[176] E. Joo, K. Lee, M. Lee, S. Park, CRACKER—a PC based simulator for industrial cracking furnaces, Comput. Chem. Eng. 24 (2-7) (2000) 1523-1528
[177] S. Vasudevan, G.P. Rangaiah, John Wiley & Sons, Ltd, Chichester, UK (2012) 179-201
[178] F.J.O. Borralho, Detailed modelling and optimisation of an ethylene plant, Master Thesis, Técnico Lisboa, 2013. [179] M.N. Rosli, N. Aziz, Simulation of ethane steam cracking with severity evaluation, IOP Conf. Ser.: Mater. Sci. Eng. 162 (1) (2016) 012017.