Valorisation des fluides de coupe usés: Partie II : cas de l'évaporation à compression mécanique de vapeur
Bibliographic record
Abstract
http://www.pro-environnement.com/environnement/DST/valorisation-des-fluides-de-coupe-uses-partie-ii-cas-de-l-evaporation-a-compression-mecanique-de-vapeur-c41dbc0ff882fbi1423.htm The cutting fluids produced by mechanical industrial processes are generally eliminated by evapo-incineration. The cost of treatment can be greatly reduced by simply extracting the water contained in the cutting fluids. Mechanical compressor evaporation has been studied with industrial microemulsion. The concentration factor has a value contained between 10 and 15 and the COD of distillate is near to 3 g/l. The COD is due to evaporation of fraction of basic oil. The economical analysis presents the financial viability of this process for evaporation of a volume of cutting fluids superior at 200 m3/year in actual conditions. Des essais d'évaporation de fluides de coupe usés (microémulsion) ont été effectués à l'aide d'un évaporateur à compression mécanique de vapeur. Le facteur de concentration peut atteindre une valeur comprise entre 10 et 15. La DCO du distillat est voisine de 3 g/l, elle est due à l'évaporation d'une coupe d'huile provenant de l'huile de base de la microémulsion. Un calcul économique de ce procédé appliqué aux fluides de coupe montre que le temps de retour est d'environ 2,5 années pour un volume traité supérieur à 200 m3/an dans les conditions d'élimination actuelles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".