Optimization of compressor work, power and heat on efficiency refrigerant r290 on split ac
Bibliographic record
Abstract
The vapor compression cycle refrigeration machine is the type of refrigeration machine that is most widely used today, generally the medium used as a working fluid that transfers heat from the product being cooled to its environment is a synthetic refrigerant. The working fluid is called refrigerant. Refrigerant is the main working fluid used in air conditioning systems to absorb heat and convert it in the refrigeration system. This then encourages countries in the world including Indonesia to then jointly carry out prevention and improvement by making agreements. Then this agreement was known as the Vienna Convention in 1985 followed by the Montreal agreement in 1987. In order to reduce the impact of ODS (Ozone Depleting Substances), in 1992 Indonesia signed the Montreal protocol. At that time, Indonesia also launched the "Indonesian Country for the phase Out of Ozone Depleting Substances (ODS) under the Montreal Protocol". This study aims to compare the work of compression, heat absorbed, and the electric power used by the split type refrigeration machine between refrigerant R-22 and refrigerant R-290. These results and analysis show that R-290 has a higher value than R-22 in compression performance. R22 power consumption is 0.30 kw and R290 is 0.26. R290 this is influenced by the light work of the compressor so that it will have an impact on electric power consumption. The power consumption of R290 is 42% less than R22. The heat absorbed by R 22 is smaller than that using hydrocarbons. The results of the optimization of compressor work, power and heat variables show an effect on efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".