Surfactant Enhanced Biodegradation of Petroleum Hydrocarbons in Oil Refinery Tank Bottom Sludge
Bibliographic record
Abstract
Summary Bioremediation has been recognized as an effective method to treat petroleum hydrocarbon pollutants. However, the biodegradation of crude oil-contaminated sludge could be a time-consuming and low-efficiency process. Among the reasons is that some petroleum hydrocarbons in the sludge are unavailable for micro-organisms? utilization. Surfactants have the potential to increase the bioavailability of such pollutants because of their capability to reduce the surface and interfacial tension and increase the solubility of hydrocarbons in water. In this study, the addition of two different chemical surfactants (Igepal CO-630 and Cedephos FA-600) were tested using a laboratory respirometer, and the effects of such surfactants on the biodegradation of total petroleum hydrocarbon (TPH) in the oil refinery sludge were investigated. Both surfactants have been found to be effective on improving microbial growth at low-concentration additions, while the concentration of 400 mg/kg has been found most effective for improving TPH (C10-C50) reduction after 14 days of biodegradation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".