(34b) NIST Thermodata Engine: Tool for Chemical Product Design

ThermoData Engine (TDE, NIST Standard Reference Database 103) is the first product that implements the concept of Dynamic Data Evaluation in the fields of thermophysics and thermochemistry. That includes maintaining of the comprehensive and up-to-date database of experimentally measured property data and expert system for data analysis and generation of recommended property values along with uncertainties on demand. Different areas of chemical engineering, including product design, benefit from the services provided by TDE.

 Chemical product design can be based on experimental data or property prediction methods based on chemical structure. TDE provides unprecedented collection of evaluated experimental data and uncertainty estimation that distinguishes it from any other product design tool. TDE product design functions include pure-compound design and solvent selection. Pure-compound design is search of a pure compound possessing required properties at specified conditions. A dynamically grown collection, now about 30,000 compounds, is available for search. User can restrict selection by experimental data or allow property prediction for the compounds with no experimental data. Properties supported are normal melting and boiling temperature, heat capacity, density viscosity, vapor pressure, etc.

 Solvent Design function serves three tasks: (1) selection of best solvent for a solid solute, (2) search for a selective solvent for separation of a solid binary mixture, and (3) selection of best solvent for extraction. Solvents are selected from a pool containing more than 27,000 compounds. Selection is made by best efficiency (solubility or selectivity, depending on the task) and matching other requirements requested by the user. Efficiency criteria are evaluated based on experimental data for binary mixtures or predictive models (original and NIST-KT UNIFAC). Underlying experimental data can be accessed and inspected in order to prove the reliability of the models used in the selection.

 Examples will be shown and benefits of ThermoData Engine for product design demonstrated.