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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".