A Study of Process Optimization of Extraction of Oil from Fish Waste for Use as A Low‐Grade Fuel
Bibliographic record
Abstract
Abstract Waste oils are potentially advantageous over petroleum and virgin vegetable oil based fuels due to waste utilization, and an overall lowering of gases and most other emissions over the life cycle of fuel production, use, and disposal. Waste generated from fish processing plants varies from 10–50 wt% of landed fish depending on the type of fish, product and processing techniques. A portion of this waste contains fish oil and varies significantly depending on the species. The oil recovery process must maximize extraction of oil and at the same time be able to integrate into the existing infrastructure at fish plants. In this study, we have optimized the recovery process developed in our lab (based on a fishmeal processing) and tested with the waste of a variety of fish species. The oil had low impurities (<0.5 wt% moisture) and degradation products, and physical properties suitable for substitution of No. 6 fuel oils and marine distillate/residual fuels. Based on this, pilot scale experiments were performed to determine scale‐up challenges and design specifications for eventual costs analysis (e.g. size, residence time, etc.), energy required and waste emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".