Ethylene yield in a large-scale olefin plant utilizing regression analysis

Document Type : Original research

Authors

1 School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia

2 Steam Cracker Complex, Manufacturing Division, Pengerang Integrated Refining Complex, 81600, Johor, Malaysia

3 Group Technical Solutions, Petroliam Nasional Berhad (PETRONAS), The Intermark Tower, 55000 Kuala Lumpur, Malaysia

Abstract

The research was carried out in a large-scale olefin process to see how different variables affect ethylene yield in an actual fluctuating plant condition. Regression analysis was adopted using Minitab Software Version 18 to create a reliable ethylene yield model. Regression analysis is a robust, practical, and advanced tool that is used in various applications as an alternative to the complex, expensive, and restricted simulation software that is specifically designed for the olefin process. The 1688 data taken from the studied plant underwent outliers and residuals removal utilizing normality and stability tools in Minitab for the analysis to be conducted as normal data. The Regression was conducted a few times until all variables satisfactorily met the multicollinearity criteria with Variance Inflation Factor (VIF) < 10 and 95% confidence level criteria with P-Value < 0.05. The final Regression model established 4 significant variables which were Hearth Burner Flow, Integral Burner Flow, Super High- Pressure Steam (SHP) Temperature, and Naphtha Feed Flow by factors of -0.001266, 0.04515, -0.0795, and 0.2105, respectively. The maximum ethylene yield was calculated at 31.75% using Response Optimizer with the recommended operating conditions at 9908.50 kg/h Hearth Burner Flow, 600.39 kg/h Integral Burner Flow, 494.65°C SHP Temperature, and 63.50 t/h Naphtha Feed Flow.

Keywords

Main Subjects

References

  1. Zakria MH, Omar AA, Bustam MA (2016) Mercury removal of fluctuating athane feedstock in a large scale production by sulphur impregnated activated carbon. Procedia Eng 148: 561-567
  2. Feli Z, Darvishi A, Bakhtyari A, Rahimpour MR, Raeissi S (2017) Investigation of propane addition to the feed stream of a commercial ethane thermal cracker as supplementary feedstock. J Taiwan Inst Chem Eng 81: 1-13
  3. Zakria MH, Mohd Ghazali MN, Abdul Rahman MR (2021) Ethylene yield from a large scale naphtha pyrolysis cracking utilizing response surface methodology. Pertanika J Sci Technol 29: 791-808
  4. Shi H, Su C, Cao J, Li P, Liang J, Zhong G (2015) Nonlinear adaptive predictive functional control based on the Takagi–Sugeno model for average cracking outlet temperature of the ethylene cracking furnace. Ind Eng Chem Res 54: 1849- 1860
  5. Song H, Su C-l, Shi H, Li P, Cao J-t (2019) Improved predictive functional control for ethylene cracking furnace. Meas Control 52: 526-539
  6. Van de Vijver R, Vandewiele N, Bhoorasingh P, Slakman B, Seyedzadeh Khanshan F, Carstensen HH, Reyniers MF, Marin B, West R, Van Geem K (2015) Automatic mechanism and kinetic model generation for gas- and solution-phase processes: A perspective on best practices, recent advances, and future challenges. Int J Chem Kinet 47: 199- 231
  7. Vangaever S, Reyniers PA, Symoens SH, Ristic ND, Djokic MR, Marin GB, Van Geem KM (2020) Pyrometer-based control of a steam cracking furnace. Chem Eng Res Design 153: 380-390
  8. Fan T-J, Luo R, Xia H, Li X (2015) Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries. Nat Hazards 75: 319-332
  9. Nikolaidis IK, Franco LFM, Vechot LN, Economou IG (2018) Modeling of physical properties and vapor – liquid equilibrium of ethylene and ethylene mixtures with equations of state. Fluid Ph Equilibria 470: 149-163
  10. Gong S, Shao C, Zhu L (2017) Energy efficiency evaluation based on DEA integrated factor analysis with respect to operation classification in ethylene production. Chin J Chem Eng 25: 793-799
  11. Brayden M, Hines D, Graham J, Pickett T (2008) Top 5 contaminants in ethylene production unit feedstock. In: 2012 AIChE Annual Meeting. AIChE, New Orleans, Lousiana
  12. Sadrameli SM (2015) Thermal/catalytic cracking of hydrocarbons for the production of olefins: A state-of-the-art review I: Thermal cracking review. Fuel 140: 102-115
  13. Wang Z, Han Y, Li C, Geng Z, Fan J (2021) Input-output networks considering graphlet-based analysis for production optimization: Application in ethylene plants. J Clean Prod 278: 123955
  14. Pu X, Shi L (2013) Commercial test of the catalyst for removal of trace olefins from aromatics and its mechanism. Catal Today 212: 115-119
  15. Dente M, Ranzi E, Goossens AG (1979) Detailed prediction of olefin yields from hydrocarbon pyrolysis through a fundamental simulation model (SPYRO). Comput Chem Eng 3: 61-75
  16. Van Goethem MWM, Kleinendorst FI, van Leeuwen C, van Velzen N (2001) Equation-based SPYRO® model and solver for the simulation of the steam cracking process. Comput Chem Eng 25: 905-911
  17. Toufighi J, Karimzadeh R, Saedi G, Hosseini S, Morafahi M, Mokhtarani B, Niaee A, Sadr AM (2004) SHAHAB-A PC-based software for simulation of steam cracking furnaces (ethane and naphtha). Iran J Chem Eng 1: 55-70
  18. Joo E, Lee K, Lee M, Park S (2000) CRACKER - a PC based simulator for industrial cracking furnaces. Comput Chem Eng 24: 1523-1528
  19. Joo E, Park S (2001) Pyrolysis reaction mechanism for industrial naphtha cracking furnaces. Ind Eng Chem Res 40: 2409-2415
  20. Kee RJ, Rupley FM, Miller JA, Coltrin ME, Grcar JF, Meeks E, Moffat HK, Lutz AE, Dixon-Lewis G, Smooke MD (2006) CHEMKIN Release 4.1. Reaction Design
  21. Reyniers PA, Schietekat CM, Kong B, Passalacqua A, Van Geem KM, Marin GB (2017) CFD simulations of industrial steam cracking reactors: Turbulence–chemistry interaction and dynamic zoning. Ind Eng Chem Res 56: 14959- 14971
  22. Van Cauwenberge DJ, Vandewalle LA, Reyniers PA, Van Geem KM, Marin GB, Floré J (2017) Periodic reactive flow simulation: Proof of concept for steam cracking coils. AIChE J 63: 1715-1726
  23. Hillewaert LP, Dierickx JL, Froment GF (1988) Computer generation of reaction schemes and rate equations for thermal cracking. AIChE J 34: 17-24
  24. Van Geem K, Reyniers M, Marin G (2008) Challenges of modeling steam cracking of heavy feedstocks. Oil Gas Sci Technol 63: 79-94
  25. Willems PA, Froment GF (1988) Kinetic modeling of the thermal cracking of hydrocarbons. 1. Calculation of frequency factors. Ind Eng Chem Res 27: 1959-1966
  26. Caballero DY, Biegler LT, Guirardello R (2015) Simulation and optimization of the ethane cracking process to produce ethylene. Comput Aided Chem Eng 37: 917-922
  27. Kuritsyn V, Arapov D, Ekimova A, Yakupov A (2008) Modeling of pyrolysis of straight-run naphtha in a large-capacity type SRT-VI furnace. Chem Technol Fuels Oils 44: 180-189
  28. Karimi H, Cowperthwaite E, Olayiwola B, Farag H, McAuley K (2017) Modelling of heat transfer and pyrolysis reactions in an industrial ethylene cracking furnace. Can J Chem Eng 96: 33-48
  29. Masoumi M, Sadrameli SM, Towfighi J, Niaei A (2006) Simulation, optimization and control of a thermal cracking furnace. Energy 31: 516-527
  30. Cai Y, Yang S, Fu S, Zhang D, Zhang Q (2017) Investigation of Portevin–Le Chatelier band strain and elastic shrinkage in Al-based alloys associated with Mg contents. J Mater Sci Technol 33: 580-586
  31. Epstein LG (1978) The Le Chatelier principle in optimal control problems. J Econ Theory 19: 103-122
  32. Junfeng Z, Zhiping P, Delong C, Qirui L, Jieguang H, Jinbo Q (2019) A method for measuring tube metal temperature of ethylene cracking furnace tubes based on machinelearning and neural network. IEEE Access 7: 158643-158654
  33. Wang Z, Li Z, Feng Y, Rong G (2016) Integrated short-term scheduling and production planning in an ethylene plant based on Lagrangian decomposition. Can J Chem Eng 94: 1723-1739
  34. Kucora I, Paunjoric P, Tolmac J, Vulovic M, Speight J, Radovanovic L (2017) Coke formation in pyrolysis furnaces in the petrochemical industry. Pet Sci Technol 35: 213-221
  35. Sun X, Shen L (2017) Research progress of coking mechanism and prevention measures for ethylene cracking furnace tubes. Corros Sci Prot Technol 29: 575-580
  36. Peng Z, Zhao J, Yin Z, Gu Y, Qiu J, Cui D (2019) ABC-ANFIS-CTF: A method for diagnosis and prediction of coking degree of ethylene cracking furnace tube. Processes 7: 909
  37. Shen L, Gong J, Liu H (2015) Effect of coking size on the thermal diffusion and stress distribution of Cr25Ni35Nb and Cr35Ni45Nb austenitic steels. Appl Mech Mater 750: 192-197
  38. Hair JF, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis with readings. Prentice-Hall
  39. Ringle C, Wende S, Becker J-M (2015) SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com
  40. Haaland PD (1989) Experimental design in biotechnology. Marcel Dekker, New York
  41. Wan Omar WNN, Nordin N, Mohamed M, Saidina Amin NA (2009) A two-step biodiesel production from waste cooking oil: Optimization of pre-treatment step. J Appl Sci 9: 3098-3103

Files

Cited by

History

  • Receive Date: 25 October 2020
  • Revise Date: 02 May 2021
  • Accept Date: 19 May 2021
  • First Publish Date: 20 May 2021

Share

How to cite

Statistics