Extending the UNIFAC-VISCO Model and Introducing the UNIFAC-THERMO Model for Improved Viscosity Prediction of Binary Liquid Mixtures

The accurate prediction of liquid mixture viscosity is essential for the design and optimization of chemical processes. This study extends the UNIFAC-VISCO (UVM) model and introduces a new group-contribution framework, the UNIFAC-THERMO (UTM) model, which eliminates the need for experimental density data of mixtures, particularly beneficial for ambient applications and cases lacking such data. Eighteen new group interaction parameters (α nm ) involving aromatic alcohols, carboxylic acids, and cyclic ethers were determined to broaden the applicability of UVM. Both models were validated using 335 binary systems across 21 chemical categories. The Grand Average Relative Deviation improved from 3.21% (UVM) to 2.75% (UTM) for dynamic viscosity and from 3.21% (UVM) to 2.72% (UTM) for kinematic viscosity. A user-friendly Excel tool implementing both models is provided to facilitate application. Overall, the UTM establishes a more versatile and transferable framework for viscosity prediction, reinforcing the role of group-contribution methods in thermophysical property estimation.