Skip to main content

Gas-solid phase calculations of binary mixtures using optimization of evolutionary algorithms

Abstract

A comparison between different modern populations based optimization methods applied to the gas-solid phase calculations is presented. Simulations are carried out in twelve binary mixtures containing supercritical carbon dioxide. Particle swarm optimization (PSO) and genetic algorithm (GA) are used to calculate interaction parameters, solubility, and sublimation pressure on these mixtures using the Peng-Robinson equation of state with the Wong-Sandler mixing rules. Comparing PSO with GA shows that the performance of PSO is better than that of GA and that it is a preferable method to optimize parameters of the gas-solid equilibrium.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Panduro, M.A., Brizuela, C.A., Balderas, B.I., and Acosta, D.A., A Comparison of Genetic Algorithm, Particle Swarm Optimization and the Differential Evolution Method for the Design of Scannable Circular Antenna Arrays, Progress in Electromagnetics Research B, 2009, vol. 13, pp. 171–186.

    Article  Google Scholar 

  2. 2.

    Holland, J., Adaptation in Natural and Artificial Systems, USA: Univ. ofMichigan Press, 1975.

    Google Scholar 

  3. 3.

    Eberhart, R.C. and Kennedy, J., Proc. 6th Int. Symp. on Micro Machine and Human Science, Nagoya: IEEE Publ., 1995, pp. 39–43.

    Book  Google Scholar 

  4. 4.

    Kennedy, J., Eberhart, R.C., and Shi, Y., Swarm Intelligence, USA: Academic Press, 2001.

    Google Scholar 

  5. 5.

    Panda, S. and Padhy, N.P., Comparison of Particle Swarm Optimization and Genetic Algorithm for FACTS-Based Controller Design, Appl. Soft Comput., 2008, vol. 8, pp. 1418–1427.

    Article  Google Scholar 

  6. 6.

    Kao, Y.T. and Zahara, E., A Hybrid Genetic Algorithm and Particle Swarm Optimization for Multimodal Functions, Appl. Soft Comput., 2007, vol. 8, pp. 849–857.

    Article  Google Scholar 

  7. 7.

    Lazzús, J.A., Prediction of Solid Vapor Pressures for Organic and Inorganic Compounds Using a Neural Network, Thermochim. Acta, 2009, vol. 489, pp. 53–62.

    Article  Google Scholar 

  8. 8.

    Neau, E., Garnier, S., and Avaulléee, L.A., A Consistent Estimation of Sublimation Pressures Using a Cubic Equation of State and Fusion Properties, Fluid Phase Equilib., 1999, vol. 164, pp. 173–186.

    Article  Google Scholar 

  9. 9.

    Lazzús, J.A., Estimation of Solid Vapor Pressures of Pure Compounds at Different Temperatures Using a Multilayer Network with Particle Swarm Algorithm, Fluid Phase Equilib., 2010, vol. 289, pp. 176–184.

    Article  Google Scholar 

  10. 10.

    Valderrama, J.O. and Zavaleta, J., Sublimation Pressure Calculated from High-Pressure Gas-Solid Equilibrium Data Using Genetic Algorithms, Ind. Eng. Chem. Res., 2005, vol. 44, pp. 4824–4833.

    Article  Google Scholar 

  11. 11.

    Lazzús, J.A., Estimation of Solid Vapor Pressure from CO2 + Biomolecules Systems Using a PSO Algorithm, J. Supercrit. Fluids, 2010, vol. 51, pp. 312–318.

    Article  Google Scholar 

  12. 12.

    Lazzús, J.A., Prediction of Sublimation Pressures from SCO2 + Hydrocarbon Systems Using a Particle Swarm Optimization, J. Eng. Therm., 2009, vol. 18, pp. 306–314.

    Article  Google Scholar 

  13. 13.

    Davis, L., Handbook of Genetic Algorithms, New York: Van Nostrand Reinhold, 1991.

    Google Scholar 

  14. 14.

    Schwaab, M., Chalbaud, E., Monteiro, J.L., and Pinto, J.C., Nonlinear Parameter Estimation through Particle Swarm Optimization, Chem. Eng. Sci., 2008, vol. 63, pp. 1542–1552.

    Article  Google Scholar 

  15. 15.

    Gopi, E.S., Algorithm Collections for Digital Signal Processing Applications Using Matlab, The Netherlands: Springer, 2007.

    MATH  Google Scholar 

  16. 16.

    Peng, D.Y. and Robinson, D.B., A New Two-Constant Equation of State, Ind. Eng. Chem. Fund., 1976, vol. 15, pp. 59–64.

    MATH  Article  Google Scholar 

  17. 17.

    Orbey, H. and Sandler, S.I., Modeling Vapor-Liquid Equilibria. Cubic Equations of State and Their Mixing Rules, USA: Cambridge Univ. Press, 1998.

    Google Scholar 

  18. 18.

    Walas, S.M., Phase Equilibria in Chemical Engineering, USA: Butterworth Publishing, 1985.

    Google Scholar 

  19. 19.

    Daubert, T.E., Danner, R.P., Sibul, H.M., and Stebbins, C.C., Physical and Thermodynamic Properties of Pure Chemicals. Data Compilation, London: Taylor & Francis, 1996.

    Google Scholar 

  20. 20.

    Xu, G., Scurto, A.M., Castier, M., Brennecke, J.F., and Stadther, M.A., Reliable Computation of High-Pressure Solid-Fluid Equilibrium, Ind. Eng. Chem. Res., 2000, vol. 39, pp. 1624–1636.

    Article  Google Scholar 

  21. 21.

    Coimbra, P., Blanco, M.R., Costa-Silva, H.S., Gil, M.H., and Sousa, H.C., Experimental Determination and Correlation of Artemisinin’s Solubility in Supercritical Carbon Dioxide, J. Chem. Eng. Data, 2006, vol. 51, pp. 1097–1104.

    Article  Google Scholar 

  22. 22.

    Subra, P., Castellani, S., Ksibi, H., and Gabarros, Y., Contribution to the Determination of the Solubility of β-Carotene in Supercritical Carbon Dioxide and Nitrous Oxide: Experimental Data and Modeling, Fluid Phase Equilib., 1997, vol. 131, pp. 269–286.

    Article  Google Scholar 

  23. 23.

    Schmitt, W.J. and Reid, R.C., Solubility of Monofunctional Organic Solids in Chemically Diverse Supercritical Fluids, J. Chem. Eng. Data, 1986, vol. 31, pp. 204–212.

    Article  Google Scholar 

  24. 24.

    Johannsen, M. and Bruner, G., Solubilities of Xanthines, Caffeines, Theophylline and Theobromine in Supercritical Carbon Dioxide, Fluid Phase Equilib., 1994, vol. 95, pp. 215–216.

    Article  Google Scholar 

  25. 25.

    McHugh, M. and Paulaitis, M.E., Solid Solubilities of Naphthalene and Biphenyl in Supercritical Carbon Dioxide, J. Chem. Eng. Data, 1980, vol. 25, pp. 326–329.

    Article  Google Scholar 

  26. 26.

    Kurnik, R.T., Holla, S.J., and Reid, R.C., Solubility of Solids in Supercritical Carbon Dioxide and Ethylene, J. Chem. Eng. Data, 1981, vol. 26, pp. 47–51.

    Article  Google Scholar 

  27. 27.

    Anitescu, G. and Tavlarides, L.L., Solubilities of Solids Supercritical Fluids, I. New Quasistatic Experimental Method for Polycyclic Aromatic Hydrocarbons (PAHs) + Pure Fluids, J. Supercrit. Fluids, 1997, vol. 10, pp. 175–189.

    Article  Google Scholar 

  28. 28.

    Yun, S.L.J., Liong, K.K., Gurdial, G.S., and Foster, N.R., Solubility of Cholesterol in Supercritical Carbon Dioxide, Ind. Eng. Chem. Res., 1991, vol. 30, pp. 2476–2482.

    Article  Google Scholar 

  29. 29.

    Cygnarowicz, M.L., Maxwell, R.J., and Seider, W.D., Equilibrium Solubilities of β-Carotene in Supercritical Carbon Dioxide, Fluid Phase Equilib., 1990, vol. 59, pp. 57–71.

    Article  Google Scholar 

  30. 30.

    Liu, X., Liu, H., and Duan, H., Particle Swarm Optimization Based on Dynamic Niche Technology with Applications to Conceptual Design, Adv. Eng. Soft., 2007, vol. 38, pp. 668–676.

    Article  Google Scholar 

  31. 31.

    Jiang, Y., Hu, T., Huang, C.C., and Wu, X., An Improved Particle Swarm Optimization Algorithm, Appl. Math. Comput., 2007, vol. 193, pp. 231–239.

    MATH  Article  Google Scholar 

  32. 32.

    Lazzús, J.A., Estimation of Density as a Function of Temperature and Pressure for Imidazolium-Based Ionic Liquids Using a Multilayer Net with Particle Swarm Optimization, Int. J. Therm., 2009, vol. 30, pp. 883–909.

    Article  ADS  Google Scholar 

  33. 33.

    Lazzús, J.A., Hybrid Method to Predict Melting Points of Organic Compounds Using Group Contribution + Neural Network + Particle Swarm Algorithm, Ind. Eng. Chem. Res., 2009, vol. 48, pp. 8760–8766.

    Article  Google Scholar 

  34. 34.

    Da, Y. and Xiurun, G., An Improved PSO-Based ANN with Simulated Annealing Technique, Neurocomputing, 2005, vol. 63, pp. 527–533.

    Article  Google Scholar 

  35. 35.

    Shi, Y. and Eberhart, R.C., Proc. IEEE Int. Conf. on Evolutionary Computation, Piscataway: IEEE Press, 1998, pp. 69–73.

    Google Scholar 

  36. 36.

    Coelho, L.S. and Sierakowski, C.A., A Software Tool for Teaching of Particle Swarm Optimization Fundamentals, Adv. Eng. Soft., 2008, vol. 39, pp. 877–887.

    Article  Google Scholar 

  37. 37.

    Kim, K.W., Yun, Y., Yoon, J., Gen, M., and Yamazaki, G., Hybrid Genetic Algorithm with Adaptative Abilities for Resource-Constrained Multiple Project Scheduling, Comp. Ind., 2005, vol. 56, pp. 143–160.

    Article  Google Scholar 

  38. 38.

    Singh, H., Yun, S.L., Macnaughton, S.J., Tomasko, D.L., and Foster, N.R., Solubility of Cholesterol in Supercritical Ethane and Binary Gas Mixtures Containing Ethane, Ind. Eng. Chem. Res., 1993, vol. 32, pp. 2841–2848.

    Article  Google Scholar 

  39. 39.

    Škerget, M. and Knez, Ž., Solubility of Binary Solid Mixture β-Carotene-Capsaicin in Dense CO2, J. Agric. Food Chem., 1997, vol. 45, pp. 2066–2069.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to J. A. Lazzús.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lazzús, J.A., Palma Chilla, L.O. Gas-solid phase calculations of binary mixtures using optimization of evolutionary algorithms. J. Engin. Thermophys. 20, 101–114 (2011). https://doi.org/10.1134/S1810232811010097

Download citation

Keywords

  • Genetic Algorithm
  • Particle Swarm Optimization
  • Artemisinin
  • Supercritical Carbon Dioxide
  • Engineer THERMOPHYSICS