Analysis of refrigerants used in supermarket commercial equipment and the potential for increasing energy efficiency and reducing environmental impact
Bibliographic record
Abstract
Refrigerants used in the commercial equipment of supermarkets are the object of the research. The Montreal Protocol calls for a complete phase-out of hydrochlorofluorocarbons (HCFCs) by 2030, and the Kigali Amendment regulates the use of hydrofluorocarbons (HFCs) from 2019. Developed countries began phasing out HFC use in 2019, while developing countries plan to freeze HFC consumption from 2024. These global efforts are aimed at reducing the depletion of the ozone layer and combating climate change. The number of supermarkets in the world varies greatly: in Europe they number from 110 thousand to 115 thousand, and in China – from 65 thousand to 70 thousand, which reflects various needs in refrigeration equipment. Stringent environmental regulations are forcing the commercial refrigeration sector to remain globally competitive. Modernization of supermarkets using natural refrigerants is important for solving emerging challenges. The results of the study show significant improvements in energy efficiency ratio (EER) and coefficient of performance (COP) when using a mixture of hydrocarbons (R290: 85 %, R600a: 15 %) compared to traditional refrigerants R404a, R449a and R502. Specifically, at the evaporation temperature of Tevap=–10 °C, EER increased by 38–44 % and COP by 26–31 % compared to R404a and R449a, respectively. At Tevap=–25 °C, EER increased by 17–34 % and COP by 2–22 % compared to R404a and R449a. Additionally, compared to R502, the hydrocarbon blend showed a 38–44 % increase in EER and 28–31 % COP at Tevap=–10 °C, and a 17–34 % increase in EER and 5–22 % COP at Tevap=–25 °C. These results highlight the advantages of the hydrocarbon mixture at different evaporation temperatures, indicating its potential to improve energy efficiency in refrigeration applications. The obtained data suggest the possibility of a wider application of the mixture of hydrocarbons in commercial refrigeration plants, offering both improved performance and compliance with safety regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".