A quantitative structure-property relationship model for predicting the critical pressures of organic compounds containing oxygen, sulfur, and nitrogen

Ji Ye Oh, Kiho Park, Yangsoo Kim, Tae Yun Park, Dae Ryook Yang

Research output: Contribution to journalArticle

Abstract

In the present research, the critical pressures of organic compounds were selected as a model case and were predicted using a quantitative structure-property relationship model. The coverage of prediction contains hydrocarbons and non-hydrocarbon organic compounds containing O, S, and N atoms. In total, 802 hydrocarbons and 1144 non-hydrocarbon organic compounds were used to develop a model with the 3D structure of each compound being optimized by quantum mechanical calculations. Furthermore, appropriate descriptors to explain critical pressure effectively were selected by forward selection regression and genetic algorithm. Multi-linear regression and neural networks were used to establish prediction models for the hydrocarbon and non-hydrocarbon organic compounds. The prediction models achieved sufficiently high performances, with R2>0.96. This research also analyzes implications of selected descriptors, and the relationship between the descriptor and critical pressure.

Original languageEnglish
Pages (from-to)397-407
Number of pages11
JournalJournal of Chemical Engineering of Japan
Volume50
Issue number6Special Issue
DOIs
Publication statusPublished - 2017

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Organic compounds
Sulfur
Nitrogen
Oxygen
Hydrocarbons
Linear regression
Genetic algorithms
Neural networks
Atoms

Keywords

  • Critical pressure
  • Implications of descriptors
  • Organic compounds
  • Prediction model
  • QSPR

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

Cite this

A quantitative structure-property relationship model for predicting the critical pressures of organic compounds containing oxygen, sulfur, and nitrogen. / Oh, Ji Ye; Park, Kiho; Kim, Yangsoo; Park, Tae Yun; Yang, Dae Ryook.

In: Journal of Chemical Engineering of Japan, Vol. 50, No. 6Special Issue, 2017, p. 397-407.

Research output: Contribution to journalArticle

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