Desempenho termodinâmico de uma máquina de gelo com R437a: estudo experimental de substituição Drop-In ao R134A
Bibliographic record
Abstract
The replacement of refrigerant fluids with high global warming potential (GWP) has become a priority in the refrigeration industry, especially after the Kigali Amendment to the Montreal Protocol. This international guideline promotes the transition to moresustainable composts, such as R437A, which is presented as a viable alternative to R134a in refrigeration systems. This study carries out an experimental analysis of the thermodynamic performance of an automatic gel machine operating with R437A, using a drop-in procedure, without structural modifications in the equipment. Critical variables of the refrigeration cycle —temperature, pressure and electrical power —are monitored by means of pressure sensors, type K thermocouples and wattmeter coupled to a hermetic compressor. The data obtained indicates that R437A exhibits behavior compatible with R134a, maintaining evaporation and condensation temperatures within the expected limits, apart from standard energy consumption. The operational efficiency of the system was preserved, confirming the technical viability of the replacement. The results contribute to the advancement of refrigerant solutions with lower environmental impact, aligned with international regulatory requirements, and reinforce the potential of R437A as a safe and efficient alternative for applications in ice production systems.
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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) | 0.000 | 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 | 0.001 |
| 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".