Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants
Bibliographic record
Abstract
There have been consequences regarding the increment of the greenhouse effect, such as the rise in the planet’s global temperature, and climate change. Refrigerants have an important contribution to the aforementioned environmental impact. In particular, hydrofluorocarbons (HFCs) contribute to the destruction of the ozone layer and the increase of the greenhouse effect. Protocols, international agreements, and legislation were developed to slow down the emission of greenhouse gases. Prohibition and definition of deadlines for the gradual elimination of various refrigerants have been proposed to replace them with others that are environmentally sustainable. Soon, the refrigeration sector will have to replace some refrigerants with others that are alternative and/or sustainable with minimal or zero environmental impact. A computational tool to support decision-making regarding the selection of alternative and/or sustainable refrigerant to replace the old one is developed to be used by refrigeration companies, manufacturers, and installers. A suggestion of refrigerants with reduced environmental impact is provided, ensuring similar thermal performance and energy efficiency, considering the safety level and renovation cost of the installation and refrigerant itself. This decision support system (DSS) uses an objective function that includes the technical specifications and properties of alternative and sustainable refrigerants. The computational tool is applied in the agri-food sector in three case studies. The results show not only the consistency of the computational tool, but also its flexibility, objectivity, and simplicity. Its use allows companies to choose refrigerants with reduced environmental impact, reduced or zero ozone depletion potential and global warming impact, thus contributing to environmental sustainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".