Bubbler System Testing in Controlled Environment for Harsh Environment Oil Spill Recovery
Bibliographic record
Abstract
Abstract Mechanical recovery techniques are used to clean up oil spills in marine environments; however, their efficiency is challenged when dealing with heavy oil, ice covered water and high sea states. Current mechanical recovery techniques are based on removing oil from the water surface, however, a significant amount of oil could remain in the water column below the surface due to turbulent ocean conditions, the density of heavy oil and oil escaping underneath the booms when the sweeping speed increases. To enhance the oil recovery effectiveness, oil particles in the water column need to be guided to the surface to be recovered by the skimmers. This paper focusses on the development of a test protocol and physical testing in C-CORE’s lab of a bubbler system for enhancing the harsh environment oil spill recovery. Air bubbles produce an upward flow in the water body, which guides the submerged particles to the surface. The air bubbles also attach to the oil particles, leading to an increase in the buoyancy and rate at which oil droplets rise to the surface. By adopting this technique for oil recovery, additional oil particles can be brought to the surface. In the study, the bubbler system was tested in both stationary and advancing conditions with medium and heavy oils. The results of the stationary and advancing tests indicate that the oil recovery ratios can be significantly enhanced by using an optimized bubbler system. Different types and configurations of bubblers were tested by varying the airflow rates and bubbler advancing speeds to determine the optimal solution. The optimal bubbler system has been observed to enhance the recovery ratio from 41.5% to 84.8% with airflow rates ranging from 0.05 to 0.20 CFM/foot. Furthermore, the effective integration of the bubbler system with a mechanical recovery system, its deployment and retrieval in a near field condition were demonstrated during tests in an outdoor tank.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".