A new method for the prediction of flash points for ternary miscible mixtures

Jie Cheng, Yong Pan, Xiaoya Song, Juncheng Jiang, Gaoyan Li, Li Ding, Hehe Chang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The flash point is one of the most important physicochemical parameters used to characterize the fire and explosion hazard for flammable liquids. The flash points of ternary miscible mixtures with different components and compositions were measured in this study. Four model input parameters, being normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms and the stoichiometric concentration of the gas phase for mixtures, were employed and calculated based on the theory of vapor-liquid equilibrium. Both multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were applied to develop prediction models for the flash points of ternary miscible mixtures. The developed predictive models were validated using data measured experimentally as well as taking data on flash points of ternairy mixtures from the literature. Results showed that the obtained average absolute error of both the MLR and the MNR model for all the datasets were within the range of experimental error of flash point measurements. It is shown that the presented models can be effectively used to predict the flash points of ternary mixtures with only some common physicochemical parameters.

Original languageEnglish
Pages (from-to)102-113
Number of pages12
JournalProcess Safety and Environmental Protection
Volume95
DOIs
StatePublished - 1 May 2015

Keywords

  • Flash point
  • Multiple linear regression
  • Multiple nonlinear regressions
  • Physicochemical parameters
  • Prediction
  • Ternary miscible mixtures

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