Desenvolvimento de refrigeradores de líquido ambientalmente sustentáveis para a indústria de bebidas
Bibliographic record
Abstract
This work presents an experimental analysis of the elimination of the refrigerant R22 and the reduction of CO2 equivalent emissions in direct expansion beverage coolers. These chillers are widely used in bars, restaurants, shopping malls, etc. to extract from the keg and chill the beverage simultaneously. These refrigeration systems use flooded-type evaporators and consequently need a high refrigerant charge to operate. The refrigerant charge can range from 950 g for a domestic application and ~6000 g for a commercial application. It is noteworthy that the most used refrigerants for this application are R22 and, to replace it, the use of R134a has increased significantly over the years. Both refrigerants are listed as substances regulated by the Montreal Protocol due to their high global warming potential (GWP), a harmful characteristic to the environment related to the greenhouse effect. To reduce the emissions of this type of refrigerant, initially, a significant reduction in the fluid charge was proposed to reduce direct contribution, furthermore, the use of R290 (HC) was proposed as a replacement for R22 for this application. A fully instrumented and automated experimental apparatus was built to simulate the beverage extractions and operation under different external conditions. The results showed that direct emissions can be reduced by up to 40% and performance can be significantly improved by refrigerant charge and capillary tube optimization on existing equipment (flooded expansion), reflected in the slight reduction of indirect emissions. To make feasible use of R290, all potential points for charge reduction in the system were mitigated and a dry expansion evaporator was developed for this application. This retrofit brought improvements such as a refrigerant charge reduction from 4500 g of R22 to 150 g of R290 and lower energy consumption of the equipment. These improvements allowed the direct emissions to be reduced by up to 99% and indirect emissions of CO2 equivalent by up to 3%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".