Skip to main content
Log in

New group contribution method for the prediction of normal melting points

  • Published:
Journal of Engineering Thermophysics Aims and scope

Abstract

The melting point of organic compounds was estimated using a simple group contribution method. The optimum parameters of this new method were obtained using particle swarm optimization in a multivariate linear regression. The melting temperatures of 250 pure compounds were predicted, and the results were compared with experimental data and other models available in the literature. Compared to the currently used group contribution methods, the new method makes significant improvements in accuracy and applicability of this important property. The study shows that the proposed method presents an excellent alternative for the estimation of the melting temperature of organic compounds (AARD of 10%) from the knowledge of the molecular structure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Dearden, J.C., Quantitative Structure-Property Relationships for the Prediction of Boiling Point, Vapor Pressure, and Melting Point, Environ. Toxicol. Chem., 2003, vol. 22, pp. 1696–1709.

    Article  Google Scholar 

  2. Lazzús, J.A., Neural Network Based Quantum Chemistry for Predicting Melting Point of Organic Compounds, Chin. J. Chem. Phys., 2009, vol. 22, pp. 19–26.

    Article  Google Scholar 

  3. Zhao, L. and Yalkowsky, S.H., A Combined Group Contribution and Molecular Geometry Approach for Predicting Melting Points of Aliphatic Compounds, Ind. Eng. Chem. Res., 1999, vol. 38, pp. 3581–3584.

    Article  Google Scholar 

  4. Jain, A., Yang, G., and Yalkowsky, S.H., Estimation of Melting Points of Organic Compounds, Ind. Eng. Chem. Res., 2004, vol. 43, pp. 7618–7621.

    Article  Google Scholar 

  5. Poling, B., Prausnitz, J.M., and O’Conell, J.P., The Properties of Gases and Liquids, New York: McGraw-Hill, 2004.

    Google Scholar 

  6. Lydersen, A.L., Estimation of Critical Properties of Organic Compounds, Rep. 3, College of Engineering, University of Wisconsin, Madison, WI, 1955.

    Google Scholar 

  7. 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 

  8. Joback, K. and Reid, R., Estimation of Pure Component Properties from Group Contribution, Chem. Eng. Comm., 1987, vol. 57, pp. 233–243.

    Article  Google Scholar 

  9. Constantinou, L. and Gani, R., A New Group Contribution Method for the Estimation of Properties of Pure Compounds, AIChE J., 1994, vol. 40, pp. 1697–1710.

    Article  Google Scholar 

  10. Marrero, J. and Gani, R., Group Contribution Based Estimation of Pure Component Properties, Fluid Phase Equil., 2001, vol. 183, pp. 183–208.

    Article  Google Scholar 

  11. Simamora, P. and Yalkowsky, S.H., Group Contribution Methods for Predicting the Melting Points and Boiling Points of Aromatic Compounds, Ind. Eng. Chem. Res., 1994, vol. 33, pp. 1405–1409.

    Article  Google Scholar 

  12. Krzyzaniak, J.F., Myrdal, P.B., Simamora, P., and Yalkowsky, S.H., Boiling Point and Melting Point Prediction for Aliphatic Non-Hydrogen-Bonding Compounds, Ind. Eng. Chem. Eng., 1995, vol. 34, pp. 2530–2535.

    Article  Google Scholar 

  13. Tu, C.-H. and Wu, Y.-S., Group-Contribution Estimation of Normal Freezing Points of Organic Compounds, J. Chin. Inst. Chem. Eng., 1996, vol. 27, pp. 323–328.

    Google Scholar 

  14. Skander, N. and Chitour, C.E., A New Group-Contribution Method for the Estimation of Physical Properties of Hydrocarbons, Oil Gas Sci. Technol., 2002, vol. 4, pp. 369–376.

    Article  Google Scholar 

  15. Wen, X. and Qiang, Y., A New Group Contribution Method for Estimating Melting and Boiling Points of Organic Compounds, Chem. Eng. Comm., 2004, vol. 191, pp. 75–86.

    Article  Google Scholar 

  16. Li, H., Higashi, H., and Tamura, K., Estimation of Boiling and Melting Points of Light, Heavy and Complex Hydrocarbons by Means of a Modified Group Vector Space Method, Fluid Phase Equil., 2006, vol. 239, pp. 213–222.

    Article  Google Scholar 

  17. Lazzús, J.A., Comput. Math. Appl., 2010, vol. 60, pp. 2260–2269.

    Article  Google Scholar 

  18. Eberhart, R.C. and Kennedy, J., A New Optimizer Using Particle Swarm Theory, in Proc. of 6th Int. Symp. on Micro Machine and Human Science, 1995, pp. 39–43.

    Chapter  Google Scholar 

  19. Kennedy, J., Eberhart, R.C., and Shi, Y., Swarm Intelligence, San Francisco: Morgan Kaufmann, 2001.

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  22. Shi, Y. and Eberhart, R.C., A Modified Particle Swarm Optimizer, in Proc. of IEEE Int. Conf. on Evolutionary Computation, 1998, pp. 69–73.

    Google Scholar 

  23. 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, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. A. Lazzús.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pérez Ponce, A.A., Salfate, I., Pulgar-Villarroel, G. et al. New group contribution method for the prediction of normal melting points. J. Engin. Thermophys. 22, 226–235 (2013). https://doi.org/10.1134/S1810232813030065

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1810232813030065

Keywords

Navigation