Estimation of the properties of hydrofluorocarbons by computer neural networks

Andrei A. Gakh, Elena G. Gakh, Bobby G. Sumpter, Donald W. Noid, Lee D. Trowbridge, David A. Harkins

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

A simple computational scheme which utilizes computational neural networks was developed and used estimating physical properties of hydrofluorocarbons. Testing of the computational method has demonstrated that thermodynamic and physical characteristics (boiling point, density, critical temperature, heat of evaporation) could be predicted with an average error of 3-5%.

Original languageEnglish
Pages (from-to)107-111
Number of pages5
JournalJournal of Fluorine Chemistry
Volume73
Issue number1
DOIs
StatePublished - Jul 1995

Funding

Research sponsored by the Division of Materials Sciences, Office of Basic Energy Sciences, US Department of Energy, under contract DE-AC05-84OR21400, with Martin Marietta Energy Systems, Inc, and in part by an appointment to the ORNL Postdoctoral Research Associates Program administered jointly by the ORNL and the Oak Ridge Institute for Science and Education.

Keywords

  • Computer neural networks
  • Hydrofluorocarbons

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