Refrigerants and their Environmental Impact Substitution of Hydro Chlorofluorocarbon HCFC and HFC Hydro Fluorocarbon. Search for an Adequate Refrigerant
Bibliographic record
Abstract
Globally, the production of cold housing is seen as a major energy challenge of this new century. The economic development of developing countries, submitted their majority in hot climates, will lead to a growing demand chilling requirements. Yet currently, the production of cold solutions is mainly based on refrigeration systems major consumers of electrical energy.It is then necessary to prepare socio-economically acceptable solutions tailored to meet those needs without compromising future international commitments on the protection of the environment, particularly for reducing greenhouse gas emissions and better protection of the ozone layer by use of refrigerants neutral. For some years now, because of their impact on the environment, the use of halogenated refrigerants has been progressively subject to quotas. In this context, the use of “natural” refrigerants becomes a possible solution. We introduce in this work the merit of redeploying these natural refrigerants as an alternative solution to replace halogenated refrigerants. The solution to the environmental impacts of refrigerant gases would therefore pass by a gas which contains no chlorine no fluorine and does not reject any CO2 emissions in the atmosphere, in brief a green gas! The aim of our project is to contribute to the protection of our environment. Our motive being to produce cold for freezing foodstuffs and seeds, safeguarding pharmaceuticals and cooling of premises: temperature conditions, air qualities controlling and producing. This work is also concerned by a contribution to the reduction of greenhouse gases and by the replacement of the polluting cooling fluids (HCFC). It is essentially the refrigeration at low temperatures, lower than (-20 °C), using the solar thermal energy, in order to improve the quality of life for many people especially in arid and semi arid regions in our country.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".