A new proposed approach to estimate the thermodiffusion coefficients for linear chain hydrocarbon binary mixtures
Bibliographic record
Abstract
Thermodiffusion behaviors in nonassociating mixtures have an important role in separation processes of the oil industry. The variations of composition and temperature may either lessen or enhance the separation in mixtures. A new model regarding the prediction of thermodiffusion coefficients for linear chain hydrocarbon binary mixtures using the thermodynamics of irreversible process is proposed. The model predicts the net amount of heat transported based on available volume for each molecule. This newly proposed model combined with the perturbed chain statistical associating fluid theory equation of state has been applied to predict thermodiffusion coefficients for binary hydrocarbon mixtures of C(10)-nC(i) (i=5,6,7,15,16,17,18), C(12)-nC(i) (i=5,6,7,8,9), and C(18)-nC(i) (i=5,6,7,8,9,12). Comparisons of the calculated theoretical results with the experimental data show good performance of the proposed model. In particular, this model which is based on the kinetic approaches has been found to be the most reliable and represents a significant improvement over the earlier models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".