Liquid viscosity remains one of the most challenging thermophysical properties to predict theoretically, making reliable empirical rules and extrapolation methods essential for process design. This webinar presents practical advances in viscosity estimation, drawing on the DIPPR 801 database, targeted experimental measurements, and new predictive approaches.
Key contributions include:
• Targeted experimental measurements of liquid viscosity and density for selected phenyl propanol isomers, which demonstrate how molecular structure influences these properties through hydrogen bonding and packing effects.
• A simple, broadly applicable empirical rule placing most organic liquid viscosities between 0.15 and 0.55 cP at the normal boiling point, with family-specific refinements validated across >70 chemical classes.
• A novel cotangent-function extrapolation method that uses only 3–4 parameters to span from near the melting point to near the critical region.
• Ongoing work toward a fully predictive QSPR framework that combines the cotangent model with LightGBM descriptor selection and PySR symbolic regression to estimate model parameters from molecular structure alone.
These tools offer chemical engineers robust, data-efficient options for viscosity prediction, interpolation, and high-temperature extrapolation when experimental values are limited or unavailable, supporting DIPPR’s mission of delivering high-quality thermophysical property resources.
Webinar content is available with the kind permission of the author(s) solely for the purpose of furthering AIChE’s mission to educate, inform and improve the practice of professional chemical engineering. The content reflects the views, opinions, and recommendations of the presenters. AIChE does not warrant or represent, expressly or by implication, the correctness or accuracy of the content of the information presented. All other uses are forbidden without the express consent of the author(s). For permission to re-use, please contact chemepermissions@aiche.org. Attendee contact information, including email addresses, will be shared with AIChE, with the option to unsubscribe from future communications.
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