Development of novel oil recovery methods for petroleum refinery oily sludge treatment
Bibliographic record
Abstract
Oily sludge is one of the most significant wastes generated in the petroleum industry. It is a complex emulsion of various petroleum hydrocarbons (PHCs), water, metals, and fine solids. Due to its hazardous nature and increased generation quantities around the world, the effective treatment of oily sludge has attracted widespread attention. The complexity of its composition and diversity of its origin sources make oily sludge management a difficult and costly undertaking. Many methods have been developed for the treatment of oily sludge through oil recovery or sludge disposal approaches, but no single specific process can be considered as a panacea since each method is associated with different advantages and limitations. Efforts should focus on the improvement of current technologies and the combination of oil recovery with sludge disposal in order to comply with both resource reuse recommendations and environmental regulations. The object of this study was to develop novel combined methods for oil recovery treatment on different refinery oily sludges. The investigation focused on the oil recovery performance of combined methods based on four individual treatment processes including ultrasonic irradiation, solvent extraction, freeze/thaw, and pyrolysis in oily sludge treatment. "~The results of this research indicate that the combined oil recovery methods have the potential to be applied for the treatment of different complex oily wastes in petroleum refining industries to meet sustainable development principles. --Leaves 2-5.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".