Modeling of future refrigerants in a vapour compression cycle
Bibliographic record
Abstract
The refrigeration plant on a fishing vessel is one of the main contributors ofthe overall energy usage of the vessel. Any increase in efficiency of the refrigerationsystem will reduce the fuel consumption and can improve the overall efficiency of thevessel. The Montreal Protocol states that all environmental impacting refrigerants mustbe phased out. Therefore there is a need to find an environmentally friendly refrigerantsthat meets the global legislation requirements as well as high refrigeration efficiency. Thepresent study investigates different refrigerants in a vapour compression refrigerationplant for efficiency and operational cost with special consideration on environmentalimpact. With the aid of simulations, the Coefficient of Performance (COP) for differentrefrigerants were determined. The results showed that carbon dioxide (R-744) andammonia (R-717) have the highest calculated performances and therefore carbon dioxidecan be recommended as a future refrigerant. On the other side, the hydrocarbons havethe lowest COPs of 2.67, 3.01 and 2.98 for methane (R-50), ethane (R-170) and propane(R-290) respectively. Overall, the hydrocarbons have 24.72%, 10.63% and 11.74% lessperformance compared to R-134a. The safety consideration for the use of ammonia(R-717) and carbon dioxide (R-744) showed that carbon dioxide is the preferred futurerefrigerant. For new built refrigeration systems, carbon dioxide is recommended for itslow global warming potential.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".