Login / Signup

Comparing Antoine parameter sources for accurate vapor pressure prediction across a range of temperatures.

Puleng MosheleMark R StenzelDaniel DroletSusan F Arnold
Published in: Annals of work exposures and health (2024)
Determining the vapor pressure of a substance at the relevant process temperature is a key component in conducting an exposure assessment to ascertain worker exposure. However, vapor pressure data at various temperatures relevant to the work environment is not readily available for many chemicals. The Antoine equation is a mathematical expression that relates temperature and vapor pressure. The objective of this analysis was to compare Antoine parameter data from 3 independent data sources; Hansen, Yaws, and Custom data and identify the source that generates the most accurate vapor pressure values with the least bias, relative to the referent data set from the CRC Handbook of Chemistry and Physics. Temperatures predicted from 3 different Antoine sources across a range of vapor pressures for 59 chemicals are compared to the reference source. The results show that temperatures predicted using Antoine parameters from the 3 sources are not statistically significantly different, indicating that all 3 sources could be useful. However, the Yaws dataset will be used in the SDM 2.0 because the data is readily available and robust.
Keyphrases
  • electronic health record
  • big data
  • drinking water
  • machine learning
  • high resolution
  • long non coding rna
  • deep learning
  • binding protein