Vermiculite Filtration for Removal of Oil from Water
Bibliographic record
Abstract
In the present study expanded vermiculite in the granular form was used in filtration studies (column studies) in treating four representative oil-in-water emulsions. The four oil-in-water emulsions were standard mineral oil (SMO), canola oil (CO), Kutwell oil (KUT45), and refinery effluent (RE) from the Co-operative Oil Refinery, Regina, Canada. The concentrations of oil in these emulsions varied from 10.6to120mg∕L. Short-term column studies were conducted in a 30mm i.d., 400mm long cast acrylic pipe with expanded vermiculite of 300mm depth. The four oil-in-water emulsions were pumped into the column at a flow rate of 3mL∕min. Breakthrough studies were conducted in a 12.5mm i.d. 300mm long cast acrylic column using 200mm depth of expanded vermiculite. The study was conducted for all the four oil-in-water emulsions with a flow rate of 12mL∕min. Results of short-term column studies showed 30% oil removal for SMO, 82% for CO, 71% for KUT45, and 54% for RE. The lower removal efficiency of oil from RE was due to the fact that RE was a stable emulsion. The results from the column breakthrough studies clearly showed that the Thomas equation provided a reasonable fit to the data. The oil sorption capacities (gram of oil sorbed/gram of vermiculite) based on the mass balance analysis was found to be 0.014, 0.013, 0.015, and 0.005g∕g for SMO, CO, KUT45, and RE, respectively. The analysis of breakthrough data using the Thomas model did not agree with these values.
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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 | 0.000 | 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) | 0.001 | 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".