Solids Deposition from Wax–Solvent–Water “Waxy” Mixtures Using a Cold Finger Apparatus
Bibliographic record
Abstract
The solids deposition from one-phase and two-phase waxy mixtures (comprising a multicomponent paraffinic wax dissolved in a multicomponent solvent, and water) was studied using a cold finger experimental apparatus. The deposition experiments were performed with a 10 mass % wax solution, containing 0, 10, 20, and 30 vol % water, with two different rates of agitation, and for nine different deposition times ranging from 30 s to 24 h. The water content of the deposit was found to be not related to the water content of the waxy mixture. The short-duration experiments showed the deposition process to be very fast, with more than half of the deposition process completed in 30 s. Following a rapid rate of deposition initially, the deposit mass was observed to increase slowly to reach steady state at about 12 h. The deposit mass decreased with an increase in the agitation speed. The deposition data were modeled satisfactorily with a steady-state heat-transfer model and an unsteady-state model based on the moving boundary problem formulation. The results confirmed the liquid–deposit interface temperature to be equal to the wax appearance temperature (WAT) of the wax solution throughout the deposition process, i.e., for all deposition times. The results of this study provide further confirmation that the solids deposition process can be described adequately with an approach based solely on heat-transfer considerations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".