Treatment of refinery oily sludge using ultrasound, bio-surfactant, and advanced oxidation processes.
Bibliographic record
Abstract
Oil refinery sludge can be generated throughout the oil production process. It consists of a large amount of petroleum hydrocarbons (PHCs) and other hazardous materials which should be disposed of appropriately. In order to find effective methods to treat the oily sludge, three different approaches were investigated in this research, including bioremediation, oil recovery, and advanced oxidation processes (AOPs). In terms of the bioremediation approach, the oily sludge was mixed with soil, and a screened bacterium strain was then introduced with the supplement of nutrients and the addition of bio-surfactant. The reduction rate of total petroleum hydrocarbons (TPH) in oily sludge spiked soil was up to 50.8% after 40 days of biodegradation. With regard to recovering oil from the oily sludge, three processes were investigated, including ultrasonic treatment alone, freeze/thaw treatment alone, and the combined ultrasonic and freeze/thaw treatment. The experimental results revealed that the combined process could achieve satisfactory performance of oil recovery. In terms of the advanced oxidation processes (AOPs), the ultrasonic treatment alone, the Fenton process alone, and the combination of ultrasound and Fenton's reagents, were examined for their abilities to reduce petroleum hydrocarbons (PHCs) content in oily sludge. The Taguchi experimental results indicated that the combination of ultrasound and Fenton reagents achieved the best effect, with the highest TPH reduction rate of 88.1% being observed in the experimental conditions. --Leaf ii.
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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 | 0.000 | 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 | 0.001 |
| 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 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".