Evaluating rhamnolipid-enhanced washing as a first step in remediation of drill cuttings and petroleum-contaminated soils
Bibliographic record
Abstract
Environmental pollution by petroleum hydrocarbons (PHCs) is a severe and widespread problem impacting human health and the environment. To combat this issue, innovative and sustainable treatment methods are required. This research study investigated rhamnolipid-enhanced washing of drill cuttings and petroleum-contaminated soil obtained from northeastern British Columbia in Canada. The efficiency of PHC reduction was analysed and quantified via a Gas Chromatography equipped with a Flame Ionization Detector. Optimum washing conditions for both drill cuttings and petroleum-contaminated soil were temperature of ∼23.5 °C (room temperature), rhamnolipid concentration of 500 mg/L, and a washing time of 30 min. The optimum stirring speed and solution-to-sample ratio for drill cuttings and petroleum-contaminated soil were 100 rpm; 1:1, and 200 rpm; 4:1 respectively. The maximum PHC reduction recorded for total petroleum hydrocarbon and PHC fractions - F2, F3 and F4 were 76.8%, 85.4%, 71.3% and 76.9% respectively for drill cuttings and 58.5%, 48.4%, 63.5% and 59.8% respectively for petroleum-contaminated soil. The results strongly suggest that soil washing is an effective step in the reduction of PHC and can be used as a first step in the treatment of drill cuttings and petroleum-contaminated soils.
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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) | 0.001 | 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".