Modelling and Experimental Validation of a Sponge Filter in a Column System
Bibliographic record
Abstract
Oil spills can damage the marine environment, local populations, and impact tourism. By adding an in-situ filtration step to the existing skimming process, it is possible to improve the oil spill response times dramatically. This thesis examines surface engineered sponges implemented in a flow through column configuration as a potential filtration technology for this application. A lab scale column system is developed to investigate the impact of temperature, flow rate, and emulsion concentration on the oil adsorption. The results of the experiments are used to develop a computational fluid dynamics model. Results show that the column system can achieve a maximum adsorption of 0.0022 kg/g and adsorption rate of 1.1 g/(kg-s) when run at a flow rate of 300 ml/min. The computational fluid dynamics model shows that the technology can be scaled for skimming vessel operation, increasing utilization by 75% over current practice when implemented in a continuous processing mode.
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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 | 0.000 |
| 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".