ارائه مدل نیمه‌تجربی برای دانسیته مایع هیدروکربن‌های سنگین در محدوده فشار و دما‌های مشخص

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، گروه مهندسی شیمی، دانشکده مهندسی، دانشگاه کاشان، کاشان، ایران
2 استادیار گروه مهندسی شیمی، دانشکده مهندسی شیمی و نفت، دانشگاه هرمزگان، بندرعباس، ایران
چکیده
در صنعت نفت شناخت خواص فیزیکی و شیمیایی به‌عنوان عنصر کلیدی در توسعه و کنترل این صنعت است. دانسیته از جمله خواص مهم برش‌های نفتی می‌باشد که پژوهش حاضر باهدف دستیابی به یک مدل نیمه‌تجربی ساده برای دانسیته مایع هیدروکربن‌های سنگین در محدوده فشار‌ و دما‌های مشخص انجام شد. دانسیته هشت هیدروکربن سنگین بعد از گرد‌آوری اطلاعات تجربی و خواص بحرانی با استفاده از معادلات حالت (EOS) سوآو - ردلیچ - وانگ (SRK) و پنگ رابینسون (PR) محاسبه و تابع هدف بر اساس الگوریتم بهینه‌سازی ازدحام ذرات (PSO) کمینه شد. نتایج حاصل از مقادیر مربوط به ضرایب ثابت مدل نیمه تجربی به ترتیب گویای نسبت عکس و مستقیم دانسیته با دما و فشار است. از طرفی مقدار کم‌تر میانگین نسبی خطا برای مدل نیمه تجربی به دست آمده در مقایسه با معادلات حالت SRK و PR برای هیدروکربن‌های با تعداد کربن کم‌تر حاصل شد. همچنین در محدوده دمایی پایین‌تر این مدل عملکرد و تطابق بهتری با داده‌های تجربی دارد و به‌طور کلی رابطه حاصل در عین سادگی دارای دقت و انعطاف بالایی است. نتایج نشان داد که میانگین درصد خطای نسبی حاصل از محاسبه دانسیته مایع ۸ هیدروکربن سنگین طبق مدل نیمه‌تجربی ارائه شده ۱/۱۸ درصد می‌باشد که ۱۷/۳۳ درصد از معادله حالت  SRK کمتر و ۷/۶۷ درصد از معادله حالت PR بهتر می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

A Semi-empirical Correlation for the Liquid Density of Heavy Hydrocarbons in Certain Pressure and Temperature Ranges

نویسندگان English

Amirhossein Oudi 1
Salehe Allami 1
Yegane Davoodbeygi 2
1 Ph.D. Student, Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran
2 Assistant Professor, Department of Chemical Engineering, Faculty of Chemical and Petroleum Engineering, University of Hormozgan, Bandar Abbas, Iran
چکیده English

In the oil industry, knowing the physical and chemical properties is a key element in the development and control of this industry. The present research was conducted with the aim of obtaining a simple semi-empirical correlation for the liquid density of heavy hydrocarbons in certain pressure and temperature range. Density of eight heavy hydrocarbons after collecting experimental data and critical properties using equations of state SRK and PR Calculation and objective function based on optimization algorithm PSO It was minimized. The results obtained from the values related to the constant coefficients of the semi-empirical correlation are indicative of the inverse and direct relationship of density with temperature and pressure, respectively. On the other hand, a lower value of relative average error for the obtained semi-empirical correlation compared to SRK and PR equations of state was obtained for hydrocarbons with less carbon number. Also, in the lower temperature range, this correlation has a better performance and agreement with the experimental data, and in general, the resulting relationship is simple and has high accuracy and flexibility. The results showed that the average percentage of relative error resulting from calculating the liquid density of 8 heavy hydrocarbons according to the presented semi-empirical model is 1.18%, which is 17.33% less than the SRK equation of state and 7.67% better than the PR equation of state.

کلیدواژه‌ها English

Heavy hydrocarbons
PSO algorithm
physical and chemical properties
Liquid density
Equation of state
  1. Poling, B. E., Prausnitz, J. M., & O’connell, J. P. (2001). The properties of gases and liquids (Vol. 5). Mcgraw-hill New York.
  2. Nji, G. N., Svrcek, W. Y., Yarranton, H. W., & Satyro, M. A. (2008). Characterization of heavy oils and bitumens. 1. Vapor pressure and critical constant prediction method for heavy hydrocarbons. Energy & Fuels, 22(1), 455–462.
  3. Riazi, M. R. (2005). Characterization and properties of petroleum fractions (Vol. 50). ASTM international.
  4. Carbognani, L., Carbognani-Arambarri, L., Lopez-Linares, F., & Pereira-Almao, P. (2011). Suitable density determination for heavy hydrocarbons by solution pycnometry: Virgin and thermal cracked Athabasca vacuum residue fractions. Energy & Fuels, 25(8), 3663–3670.
  5. Saryazdi, F., Motahhari, H., Schoeggl, F. F., Taylor, S. D., & Yarranton, H. W. (2013). Density of hydrocarbon mixtures and bitumen diluted with solvents and dissolved gases. Energy & Fuels, 27(7), 3666–3678.
  6. Fissa, M. R., Lahiouel, Y., Khaouane, L., & Hanini, S. (2019). QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods. Journal of Molecular Graphics and Modelling, 87, 109–120.
  7. Abdi, A., Ignatowicz, M., Gunasekara, S. N., Chiu, J. N. W., & Martin, V. (2020). Experimental investigation of thermo-physical properties of n-octadecane and n-eicosane. International Journal of Heat and Mass Transfer, 161, 120285.
  8. Leung, D. Y. C., Caramanna, G., & Maroto-Valer, M. M. (2014). An overview of current status of carbon dioxide capture and storage technologies. Renewable and Sustainable Energy Reviews, 39, 426–443.
  9. Murshid, G., Butt, W. A., & Garg, S. (2019). Investigation of thermophysical properties for aqueous blends of sarcosine with 1-(2-aminoethyl) piperazine and diethylenetriamine as solvents for CO2 Journal of Molecular Liquids, 278, 584–591.
  10. Wei, B., Gao, H., Pu, W., Zhao, F., Li, Y., Jin, F., Sun, L., & Li, K. (2017). Interactions and phase behaviors between oleic phase and CO2 from swelling to miscibility in CO2-based enhanced oil recovery (EOR) process: a comprehensive visualization study. Journal of Molecular Liquids, 232, 277–284.
  11. Chen, Y., Sari, A., Xie, Q., & Saeedi, A. (2019). Insights into the wettability alteration of CO2-assisted EOR in carbonate reservoirs. Journal of Molecular Liquids, 279, 420–426.
  12. Philippe, R., Lacroix, M., Dreibine, L., Pham-Huu, C., Edouard, D., Savin, S., Luck, F., & Schweich, D. (2009). Effect of structure and thermal properties of a Fischer–Tropsch catalyst in a fixed bed. Catalysis Today, 147, S305–S312.
  13. Mahmoudi, H., Mahmoudi, M., Doustdar, O., Jahangiri, H., Tsolakis, A., Gu, S., & LechWyszynski, M. (2017). A review of Fischer Tropsch synthesis process, mechanism, surface chemistry and catalyst formulation. Biofuels Engineering, 2(1), 11–31.
  14. Cui, J., Wu, J., & Bi, S. (2021). Liquid viscosity, interfacial tension, thermal diffusivity and mutual diffusivity of n-Tetradecane with dissolved carbon dioxide. Fluid Phase Equilibria, 534, 112951.
  15. Yang, T., Sun, Y., Meng, X., Wu, J., & Siepmann, J. I. (2021). Simultaneous measurement of the density and viscosity for n-Decane+ CO2 binary mixtures at temperature between (303.15 to 373.15) K and pressures up to 80 MPa. Journal of Molecular Liquids, 338, 116646.
  16. Cui, J., Yang, T., Bi, S., & Wu, J. (2024). Liquid viscosity, interfacial tension, thermal and mutual diffusivities of mixtures of n-eicosane with dissolved carbon dioxide. The Journal of Chemical Thermodynamics, 190, 107212.
  17. Banipal, T. S., Garg, S. K., & Ahluwalia, J. C. (1991). Heat capacities and densities of liquid n-octane, n-nonane, n-decane, and n-hexadecane at temperatures from 318.15 K to 373.15 K and at pressures up to 10 MPa. The Journal of Chemical Thermodynamics, 23(10), 923–931.
  18. Doolittle, A. K. (1964). Specific Volumes of n-Alkanes. Journal of Chemical & Engineering Data, 9(2), 275–279.
  19. Baled, H. O. (2012). Density and viscosity of hydrocarbons at extreme conditions associated with ultra-deep reservoirs-measurements and modeling. University of Pittsburgh.
  20. Van Der Waals, J. D., & Rowlinson, J. S. (2004). On the continuity of the gaseous and liquid states. Courier Corporation.
  21. Redlich, O., & Kwong, J. N. S. (1949). On the thermodynamics of solutions. V. An equation of state. Fugacities of gaseous solutions. Chemical Reviews, 44(1), 233–244.
  22. Soave, G. (1972). Equilibrium constants from a modified Redlich-Kwong equation of state. Chemical Engineering Science, 27(6), 1197–1203.
  23. Peng, D.-Y., & Robinson, D. B. (1976). A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals, 15(1), 59–64.
  24. Oudi, A., Hosseini, M., Azimi, S., & Davoodbeygi, Y. (2022). Modeling of Diffusion Coefficients for Binary Gas at P= 101.325 kPa using Particle Swarm Optimization Algorithm. Journal of Chemical and Petroleum Engineering, 56(2), 317–329.
  25. Oudi, A., Faramarzi, S., Yarmohammadian, S., & Davoodbeygi, Y. (2022). Developing a model for calculating pure gas thermal conductivity at P= 1bar using particle swarm optimization algorithm. Gas Processing Journal, 10(1), 113–124.
  26. Oudi, A., Yarmohammadian, S., Hosseini, M., & Nemati Lay, E. (2023). Optimizing the coefficients of the particle swarm optimization algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants. Journal of Modeling in Engineering, 21(75), 297–307.
  27. Mohsen-Nia, M., Modarress, H., & Mansoori, G. A. (2003). A cubic hard-core equation of state. Fluid phase equilibria206(1-2), 27-39.
  28. Yang, J., Meng, X., & Wu, J. (2018). Liquid Density of n-Pentene, n-Hexene, and n-Heptene at Temperatures from 283.15 to 363.15 K and Pressures up to 100 MPa. Journal of Chemical & Engineering Data, 63(6), 2280–2289.

  • تاریخ دریافت 06 بهمن 1403
  • تاریخ بازنگری 12 اردیبهشت 1404
  • تاریخ پذیرش 30 اردیبهشت 1404