Modeling of Diffusion Coefficients for Binary Gas at P=101.325 kPa using Particle Swarm Optimization Algorithm

Document Type : Research Paper

Authors

1 Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

2 Department of Chemical Engineering, University of Hormozgan, Iran.

10.22059/jchpe.2022.340678.1386

Abstract

The diffusion coefficient of gases in a wide range of chemical processes is of great importance. Semi-empirical models for diffusion coefficient prediction are useful due to their relatively lower cost compared to laboratory methods. In this study, to facilitate the equations and accelerate the calculations, appropriate models have been presented using existing parameters such as molecular mass and critical properties to determine the binary diffusion coefficient of gases. The calculations have been performed using a particle swarm optimization (PSO) algorithm. This model has been used to obtain the diffusion coefficient of 84 gas dual systems at P=101.325 kPa and variable temperature (373.15-673.15 K). Also, during the validation phase, the suggested model attained the most accurate prediction with R^2=0.9989. This model is capable to predict the diffusion coefficient of gases with a mean relative error percentage of 2.57% and mean square error percentage of 0.15% compared to actual data. These results are significantly better than those obtained from other models.

Keywords


  1. Graham T. XVII. On the molecular mobility of gases. Philosophical Transactions of the Royal Society of London. 1863 Dec 31(153):385-405.
  2. Wilke CR, Lee CY. Estimation of diffusion coefficients for gases and vapors. Industrial & Engineering Chemistry. 1955 Jun;47(6):1253-7.
  3. Keumnam C, Irvine Jr TF, Karni J. Measurement of the diffusion coefficient of naphthalene into air. International journal of heat and mass transfer. 1992 Apr 1;35(4):957-66.
  4. Marrero TR, Mason EA. Gaseous diffusion coefficients. Journal of Physical and Chemical Reference Data. 1972 Jan;1(1):3-118.
  5. Camper D, Becker C, Koval C, Noble R. Diffusion and solubility measurements in room temperature ionic liquids. Industrial & engineering chemistry research. 2006 Jan 4;45(1):445-50.
  6. Pillalamarry M, Harpalani S, Liu S. Gas diffusion behavior of coal and its impact on production from coalbed methane reservoirs. International Journal of Coal Geology. 2011 Jun 1;86(4):342-8.
  7. Chen M, Kang Y, Zhang T, You L, Li X, Chen Z, Wu K, Yang B. Methane diffusion in shales with multiple pore sizes at supercritical conditions. Chemical Engineering Journal. 2018 Feb 15;334:1455-65.
  8. Zhao X, Jin H. Correlation for self-diffusion coefficients of H2, CH4, CO, O2 and CO2 in supercritical water from molecular dynamics simulation. Applied Thermal Engineering. 2020 May 5;171:114941.
  9. Athar K, Doranehgard MH, Eghbali S, Dehghanpour H. Measuring diffusion coefficients of gaseous propane in heavy oil at elevated temperatures. Journal of Thermal Analysis and Calorimetry. 2020 Feb;139(4):2633-45.
  10. Zhao X, Jin H, Chen Y, Ge Z. Numerical study of H2, CH4, CO, O2 and CO2 diffusion in water near the critical point with molecular dynamics simulation. Computers & Mathematics with Applications. 2021 Jan 1;81:759-71.
  11. Si L, Zhang H, Wei J, Li B, Han H. Modeling and experiment for effective diffusion coefficient of gas in water-saturated coal. Fuel. 2021 Jan 15;284:118887.
  12. An F, Jia H, Feng Y. Effect of stress, concentration and temperature on gas diffusion coefficient of coal measured through a direct method and its model application. Fuel. 2022 Mar 15;312:122991.
  13. Chen H, Wang Y, Zuo M, Zhang C, Jia N, Liu X, Yang S. A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network. Energy. 2022 Jan 15;239:122286.
  14. Bellaire D, Großmann O, Münnemann K, Hasse H. Diffusion coefficients at infinite dilution of carbon dioxide and methane in water, ethanol, cyclohexane, toluene, methanol, and acetone: A PFG-NMR and MD simulation study. The Journal of Chemical Thermodynamics. 2022 Mar 1;166:106691.
  15. Yu K, Wang X, Wang Z. Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization. Knowledge-Based Systems. 2016 Mar 15;96:156-70.
  16. Cai P, Nie W, Chen D, Yang S, Liu Z. Effect of air flowrate on pollutant dispersion pattern of coal dust particles at fully mechanized mining face based on numerical simulation. Fuel. 2019 Mar 1;239:623-35.
  17. Liu Q, Nie W, Hua Y, Peng H, Liu C, Wei C. Research on tunnel ventilation systems: dust diffusion and pollution behaviour by air curtains based on CFD technology and field measurement. Building and Environment. 2019 Jan 1;147:444-60.
  18. Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochimica acta. 2011 Jun 10;520(1-2):1-9.
  19. Parwekar P, Rodda S, Vani Mounika S. Comparison between genetic algorithm and PSO for wireless sensor networks. InSmart Computing and Informatics 2018 (pp. 403-411). Springer, Singapore.
  20. Kennedy J, Eberhart R. Particle swarm optimization. InProceedings of ICNN'95-international conference on neural networks 1995 Nov 27 (Vol. 4, pp. 1942-1948). IEEE.
  21. Buyukada M. Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. Bioresource Technology. 2016 Sep 1;216:280-6.
  22. Song C. Parameter estimation of the pyrolysis model for fir based on particle swarm algorithm. In2011 Second International Conference on Mechanic Automation and Control Engineering 2011 Jul 15 (pp. 2354-2357). IEEE.
  23. Safari H, Jamialahmadi M. Thermodynamics, kinetics, and hydrodynamics of mixed salt precipitation in porous media: Model development and parameter estimation. Transport in porous media. 2014 Feb;101(3):477-505.
  24. Farajnezhad A, Afshar OA, Khansary MA, Shirazian S, Ghadiri M. Correlation of interaction parameters in Wilson, NRTL and UNIQUAC models using theoretical methods. Fluid Phase Equilibria. 2016 Jun 15;417:181-6.
  25. Moeini P, Bagheri A. Adsorption kinetic modeling of toxic vapors on activated carbon in the batch reactor. Research on Chemical Intermediates. 2020 Dec;46(12):5547-66.
  26. de Paulo EH, Folli GS, Nascimento MH, Moro MK, da Cunha PH, Castro EV, Neto AC, Filgueiras PR. Particle swarm optimization and ordered predictors selection applied in NMR to predict crude oil properties. Fuel. 2020 Nov 1;279:118462.
  27. Troudi H, Ghiss M, Ben Guedria N, Ellejmi M, Tourki Z. A new Sauter mean diameter correlation suited for the gas cross flow in packed bed reactors based on PSO optimization algorithm. Separation Science and Technology. 2022 Jul 18:1-24.
  28. Farajpour E, Behbahani TJ, Ghotbi C. A new experimental and theoretical approach for viscosity Iranian heavy crude oils based on tuning friction theory and friction volume theory parameters. Inorganic Chemistry Communications. 2022 May 1;139:109319.
  29. Eberhart R, Kennedy J. A new optimizer using particle swarm theory. InMHS'95. Proceedings of the sixth international symposium on micro machine and human science 1995 Oct 4 (pp. 39-43). Ieee.
  30. Zhang Y, Wu L. A hybrid TS-PSO optimization algorithm. Journal of Convergence Information Technology. 2011 May;6(5):169-74.
  31. Darvishi R, Esfahany MN, Bagheri R. Numerical study on increasing PVC suspension polymerization productivity by using PSO optimization algorithm. International Journal of Plastics Technology. 2016 Dec;20(2):219-30.
  32. Pranava G, Prasad PV. Constriction coefficient particle swarm optimization for economic load dispatch with valve point loading effects. In2013 international conference on power, energy and control (ICPEC) 2013 Feb 6 (pp. 350-354). IEEE.
  33. Rao PS, Varma GP, Prasad CD. Identification of linear and non linear curve fitting models using particle swarm optimization algorithm. InAIP Conference Proceedings 2020 Oct 12 (Vol. 2269, No. 1, p. 030040). AIP Publishing LLC.
  34. Rajpoot V, Srivastava DK, Saurabh AK. Optimization of I-shape microstrip patch antenna using PSO and curve fitting. Journal of Computational Electronics. 2014 Dec;13(4):1010-3.
  35. Lide DR, editor. CRC handbook of chemistry and physics. CRC press; 2004 Jun 29.
  36. Zhao Z, Wang J, Sun B, Arowo M, Shao L. Mass transfer study of water deoxygenation in a rotor–stator reactor based on principal component regression method. Chemical Engineering Research and Design. 2018 Apr 1;132:677-85.
  37. Liu Y, Hong W, Cao B. Machine learning for predicting thermodynamic properties of pure fluids and their mixtures. Energy. 2019 Dec 1;188:116091.
  38. Farzaneh-Gord M, Rahbari HR, Mohseni-Gharesafa B, Toikka A, Zvereva I. Accurate determination of natural gas compressibility factor by measuring temperature, pressure and Joule-Thomson coefficient: Artificial neural network approach. Journal of Petroleum Science and Engineering. 2021 Jul 1;202:108427.
  39. Arnold JH. Studies in diffusion. Industrial & Engineering Chemistry. 1930 Oct;22(10):1091-5.
  40. Gilliland ER. Diffusion coefficients in gaseous systems. Industrial & Engineering Chemistry. 1934 Jun 1;26(6):681-5.
  41. Andrussow L. Über die Diffusion in Gasen I Berechnung der Koeffizienten der Diffusion. Beziehung zwischen den Koeffizienten der Diffusion zweier Komponente und den Koeffizienten der Selbstdiffusion. Zeitschrift für Elektrochemie und angewandte physikalische Chemie. 1950 Dec;54(7):566-71.
  42. Slattery JC, Bird RB. Calculation of the diffusion coefficient of dilute gases and of the self‐diffusion coefficient of dense gases. AIChE Journal. 1958 Jun;4(2):137-42.
  43. Fuller EN, Giddings JC. A comparison of methods for predicting gaseous diffusion coefficients. Journal of Chromatographic Science. 1965 Jul 1;3(7):222-7.
Volume 56, Issue 2
December 2022
Pages 317-329
  • Receive Date: 27 March 2022
  • Revise Date: 07 November 2022
  • Accept Date: 08 November 2022
  • First Publish Date: 18 November 2022