Extending the UNIFAC-VISCO Model and Introducing the UNIFAC-THERMO Model for Improved Viscosity Prediction of Binary Liquid Mixtures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".