Étude numérique et expérimentale d’une conversion de fluide frigorigène sur un sécheur d’air comprimé industriel Conditionair
Bibliographic record
Abstract
The refrigerant HCFC R-22 is used in variety of refrigerating and heating equipments. Nevertheless, this refrigerant has been scheduled to be phased out by the Montreal Protocol. This ruling will result in a considerable reduction of ozone layer depletion and global warming associated with its use. However, many refrigeration and heating equipments are still working with the refrigerant R-22. This document is about a refrigerant retrofit implementation on an industrial compressed air dryer created by the company Conditionair. The HCFC R-22 have been here replaced by an HFC refrigerant. First, the performances of this industrial device has been simulated with five substitute refrigerants. The characteristics of heat transfer during the vaporization of azeotropic and zeotropic refrigerants have been studied and the average heat transfer coefficient have been calculated with a VBA macro command. Simulated performances of the air compressed dryer working with each of the five substitute refrigerants are shown and discussed. Two tests are then carried out with the use of two different refrigerants. The performances of the device are measured on the site and compared with the performances first simulated. Finally, a result analysis is performed and the refrigerant providing the best operation of the compressed air dryer is selected.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".