Remediation of flare pit soils using supercritical fluid extraction
Bibliographic record
Abstract
Supercritical fluid extraction (SFE) is a promising remediation technology for contaminated soils. This work investigates the use of SFE to remove petroleum hydrocarbons (PHCs) from flare pit soils. Extractions were performed on two flare pit soils at pressures ranging from 11.0 to 24.1 MPa and at temperatures ranging from 40 to 80 °C in an attempt to identify the best extraction conditions and to understand the effects of pressure, temperature, supercritical fluid flow rate, soil type, and extraction time on the extraction efficiency. For the conditions studied, the efficiency of the SFE process appeared to be solvent-density dependent. Conditions of 24.1 MPa and 40 °C (highest supercritical fluid density) yielded the highest extraction efficiency for both soils (89% for the sand and 80% for the loam). An increase in temperature at a fixed pressure led to a decrease in the extraction efficiency while an increase in pressure at a fixed temperature led to an increase in the extraction efficiency. The treated soils appeared to be drier, grainy, and lighter coloured than the soil prior to extraction.Key words: supercritical fluid extraction (SFE), supercritical carbon dioxide, flare pit soils, contaminated soil, soil remediation, biorecalcitrant petroleum hydrocarbons.
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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) | 0.000 | 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".