EXPERIMENTAL INVESTIGATION OF A REFRIGERATOR BY USING ALTERNATIVE ECO FRIENDLY REFRIGERANTS( R600a & HC MIXTURE).
Bibliographic record
Abstract
In the present days refrigeration became a common human need but due to the release of green house gases from the refrigerators in to the earth’s atmosphere, causes high global warming and in turn destructing the ozone layer of atmosphere. In concern of the earth’s environment, Montreal and Kyoto protocols proposed for alternative eco-friendly refrigerants. In the current work, experimental investigation on VCRS system tested with R600a and hydrocarbon mixture (R290/R600a) as refrigerants as they have zero ODP and very low GWP related to the refrigerant R134a. Due to the higher value of latent heat of hydrocarbons, the amount of refrigerant charge will be relatively lower than R134a. By using R600a and hydrocarbon mixture refrigerants of charges 50g, 55g and 60 g are tested individually for performance characteristics and critical correlations are drawn between the refrigerants and represented by graphical representation. Refrigeration effect of R290/R600a (50/50 by wt %) mixture was 21.4% higher than R600a for 55g charge. The obtained results proved that the overall performance of 55g (R290/R600a) mixture could be considered as the best eco-friendly alternative refrigerant phase out R134a refrigerant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".