Regeneration of used lubricating oil by solvent extraction and phase diagram analysis
Bibliographic record
Abstract
The methodology presented in this work is developed for the removal of sludge and thus regenerate the properties of the engine lubricating synthetic oil for its reuse. This was achieved removing contaminants from waste lubricant (WL) by solvent extraction. The technique used analyze ternary phase diagrams of solvent mixtures with WL and dehydrated WL determine regions that allow maximum wet sludge removal. The pair of solvents chosen to create ternary phase diagrams with WL consists of a polar solvent and a non-polar solvent. The pairs of solvents selected were methyl isobutyl ketone (MIBK) and methanol with toluene according to their miscibility with WL and their Hildebrand solubility parameter values. The liquid systems solvent mixture with WL corresponding to the points selected in the ternary phase diagrams were centrifuged to quantify the percentage of wet sludge removal (PWSR) to evaluate the efficiency of the process. The properties of the recovered lubricants were evaluated carrying out tests of viscosity and density at different temperatures as well as flash point. The results were compared to those of the WLO and the new lubricant (NL).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".