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<p>Thermophysical properties data are crucial for education, research, process modeling, and operational activities in chemical engineering. Supported by the Korean government, Korea University has been providing this data continuously since 1997 (<a href="http://www.cheric.org">www.cheric.org</a>). Recently, a new version of the Korean Thermophysical Properties Data Bank (KDB) (<a href="http://www.mdlkdb.com">www.mdlkdb.com</a>) has been developed and released. This updated version features an expanded database and enhanced calculation capabilities. It includes critically evaluated data for 1970 compounds and offers 5567 binary vapor–liquid equilibrium (VLE) data sets. The standard references are assessed based on criteria such as uncertainty, reproducibility, predictability, and consistency, ensuring the reliability and quality of the data. Additionally, machine learning methods for property estimation have been developed using the NIST/TRC database, and these calculation modules have been integrated into the new web interface. The property calculation page allows users to perform calculations for pure properties and binary vapor–liquid equilibrium using methods such as UNIFAC, COSMO-SAC, and a machine learning version of COSMO-SAC for certain simpler cases. This contribution outlines the functionalities and evaluation procedures of the KDB, which is an ongoing project aimed at enhancing the accessibility and reliability of thermophysical data while also improving precision in chemical process modeling and design.</p>

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Korea Thermophysical Properties Databank (KDB): Web Service for Critically Evaluated Thermophysical Data and Prediction Methods

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • Sun Yoo Hwang,
  • Beom Chan Ryu,
  • Sung Shin Kang,
  • Hyunwoong Bang,
  • Jeong Won Kang

摘要

Thermophysical properties data are crucial for education, research, process modeling, and operational activities in chemical engineering. Supported by the Korean government, Korea University has been providing this data continuously since 1997 (www.cheric.org). Recently, a new version of the Korean Thermophysical Properties Data Bank (KDB) (www.mdlkdb.com) has been developed and released. This updated version features an expanded database and enhanced calculation capabilities. It includes critically evaluated data for 1970 compounds and offers 5567 binary vapor–liquid equilibrium (VLE) data sets. The standard references are assessed based on criteria such as uncertainty, reproducibility, predictability, and consistency, ensuring the reliability and quality of the data. Additionally, machine learning methods for property estimation have been developed using the NIST/TRC database, and these calculation modules have been integrated into the new web interface. The property calculation page allows users to perform calculations for pure properties and binary vapor–liquid equilibrium using methods such as UNIFAC, COSMO-SAC, and a machine learning version of COSMO-SAC for certain simpler cases. This contribution outlines the functionalities and evaluation procedures of the KDB, which is an ongoing project aimed at enhancing the accessibility and reliability of thermophysical data while also improving precision in chemical process modeling and design.