Assessing the efficacy of three bio‐based flocculants in the reclamation of spent lubricating oil
Bibliographic record
Abstract
Abstract The current study encompasses a comprehensive assessment of three biopolymeric flocculants on the overall performance of recycling waste lubricating oil to achieve a higher percentage recovery, flocculation efficacy, and better quality of recovered base oil. The findings reveal that with experimental conditions such as (i) mixing time of 80 min; (ii) agitation speed of 400 rpm; (iii) reaction temperature 50°C; (iv) solvent to waste oil ratio 3:1 g/g; and (v) flocculant dosage 1 g/ kg of solvents, 1‐butanol and sodium alginate gives highest percentage yield of 91% followed by corn starch of 89.10% and xanthan gum of 87.18% as bio‐polymer flocculant. The effects of various process parameters of bio‐flocculants on flocculation efficiency are expounded. With the process parameters of (i) initial pH of 5.9, 6.0, and 6.2; (ii) mixing time − 59, 60, and 63 min; and (iii) solution temperature of 59, 60, and 62.2°C, maximum flocculation efficacy (% sludge removal) of 16.24%, 13.01%, and 14.09% were attained for the cases of refined oil treated with sodium alginate, corn starch, and xanthan gum, respectively. Results also reveal that the physicochemical properties of refined base oil treated with 1‐butanol and sodium alginate as bio flocculant are almost close to the virgin lubricating oil. The optimum recovery of high‐quality base oil with the adoption of green technology and solvent–bio flocculant combination can mitigate the environmental impact of waste oil and create an energy‐efficient sustainable condition for the regeneration of re‐refined base oil.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".