ESTIMATION METHOD OF EXPERIMENTALLY UNKNOWN CRITICAL PARAMETERS AND ACENTRIC FACTOR FOR MULTIATOMIC COMPOUNDS

Authors

  • Е. Г. Мокшина Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080, Ukraine
  • В. Е. Кузьмин Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080, Ukraine
  • В. И. Недоступ Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080, Ukraine

DOI:

https://doi.org/10.18198/j.ind.gases.2014.0714

Keywords:

Thermodynamic properties, Molecular structure, Critical parameters, Acentric factor

Abstract

Each year there are new multiatomic chemical compounds synthesized. Their implication into different technological processes are restricted by lack of the reliable experimental p,v,T-data for these compounds. To predict their thermodynamic properties, i.e. critical temperatures, pressures, volumes and acentric factor, the QSPR (Quantitative Structure-Property Relationship) method can be used. Molecular structure description was performed using Simplex Representation of Molecular Structure (SiRMS). The correlations was developed using non-linear Random Forest method. The SiRMS approach shown to be perspective investigation and estimation method for thermodynamic properties of organic compounds. According to more than sufficient statistical characteristic sofmodels as to simple des-criptors’ interpretation, simplex method could be recommended as the instrument for the critical properties investigation and prediction. 

Author Biographies

Е. Г. Мокшина, Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080

E.G. Mokshyna, PhD Student

В. Е. Кузьмин, Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080

V.E. Kuz’min,Doctor of Сhemical Sciences

В. И. Недоступ, Bogatsky Physical-Chemical Institute of NAS of Ukraine, Lustdorfskaya Road, 86, Odessa, Ukraine, 65080

V.I. Nedostup, Doctor of Technical Sciences

References

Poling B.E., Prausnitz J.M., O'Connell L.P. (2001). The Properties of Gases and Liquids. — N.Y.: McGraw-Hill. — 707 p.

Kazakov A., Chris D. Muzny, Diky V. et al. (2010). Predictive correlations based on large experimental datasets: Critical constants for pure compounds// J. Fluid Phase Equilibria. — V. 298. — No. 1. — P. 131-142.

Godavarthy S., Robert L. Robinson Jr., Khaled A.M. Gasem. (2008). Improved structure-property relationship models for prediction of critical properties// J. Fluid Phase Equilibria. — V. 264. — No. 1-2. — P. 122-136.

[Electron resource] NIST WebBook: http://webbook.nist. gov/ chemistry.

Kuz’min V. E., Artemenko A.G., Muratov E.N. et al. (2010). Virtual screening and molecular design based on hierarchical QSAR technology// Recent Advances in QSAR Studies. — Springer science+. — P. 127-176.

Kuz’min V. E., Artemenko A.G., Muratov E.N. (2008). Hierarchical QSAR technology based on the Simplex representation of molecular structure// J. Comput. Aided Mol. Des. — V. 22. — No. 6-7. — P. 403-421.

Kuz’min V.E., Muratov E.N., Artemenko A.G et al. (2008). The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study// J. Comput. Aided Mol. Des. — V. 22. — No. 10. — P. 747-759.

Kuz’min V.E., Artemenko A.G., Muratov E.N. et al. (2007). QSAR analysis of anti-coxsackievirus B3 nancy activity of 2-amino-3-nitropyrazole[1,5-a] pyrimidines by means of simplex approach// Antivir Res. — V. 74. — No. 3. — P. A49-A50.

Jolly W.L., Perry W.B. (1973). Estimation of atomic charges by an electronegativity equalization procedure calibration with core binding energies// J. Am. Chem. Soc. — V. 95. — No. 17. — P. 5442 – 5450.

Breiman L. (2001). Random Forests// Machine Learning. — V. 45. — No. 1. — P. 5-32.

Polishchuk P.G., Muratov E.N., Artemenko A.G. et al. (2009). Application of random forest approach to QSAR prediction of aquatic toxicity// J. Chem. Inf. Model. — V. 49. — No. 11. — P. 2481-2488.

Golbraikh A., Tropsha A. (2000). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection// Molecular Diversity. — V. 5. — No. 4. — P. 231-243.

Kline A.A., Zei D.A., Whitten C.R. et al. (2011). AIChE/ DIPPR® Project 911. Amer. Inst. of Chemical Engineers.

Shansheng Y., Wencong L., Nianyi C., Qiannan H. (2005). Support vector regression based QSPR for the prediction of some physicochemical properties of alkyl benzenes// J. Mol. Struct. — V. 719. — No. 1. — P. 119-127.

[Electron resource] DIPPR database: http://www.aiche.org/dippr.

[Electron resource] Сodessa descriptors:http://www.semichem.com/codessa.

Issue

Section

THERMOPHYSICAL PROPERTIES OF GASES AND THEIR MIXTURES. THERMODYNAMIC ANALYSIS OF PROCESSES IN LOW-TEMPERATURE SYSTEMS