Determination of the normal boiling point of chemical compounds using a quantitative structure-property relationship strategy: Application to a very large dataset

Farhad Gharagheizi, Seyyed Alireza Mirkhani, Poorandokht Ilani-Kashkouli, Amir H. Mohammadi*, Deresh Ramjugernath, Dominique Richon

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

In this work, the quantitative structure property relationship (QSPR) strategy is applied to predict the normal boiling point (NBP) of pure chemical compounds. In order to propose a comprehensive, reliable, and predictive model, a large dataset of 17,768 pure chemical compounds was exploited. The sequential search mathematical method has been observed to be the only viable search method capable for selection of appropriate model parameters (molecular descriptors) with regard to a data set as large as is used in this study. To develop the model, a three-layer feed forward artificial neural network has been optimized using the Levenberg-Marquardt (LM) optimization strategy. Using this dedicated strategy, satisfactory results were obtained and are quantified by the following statistical parameters: average absolute relative deviations of the predicted properties from existing literature values: 3.2%, and squared correlation coefficient: 0.94. (C) 2013 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)250-258
Number of pages9
JournalFluid Phase Equilibria
Volume354
DOIs
Publication statusPublished - 25 Sep 2013
MoE publication typeA1 Journal article-refereed

Keywords

  • Normal boiling points
  • QSPR
  • Sequential forward search
  • ANN
  • Very large database
  • NONELECTROLYTE ORGANIC-COMPOUNDS
  • ACYCLIC CARBONYL-COMPOUNDS
  • MOLECULAR-STRUCTURE
  • VAPOR-PRESSURE
  • MELTING-POINT
  • PREDICTION
  • TEMPERATURE
  • MODEL
  • HYDROCARBONS
  • SET

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