Performance Assessment of CO <sub>2</sub> -Hydrocarbon Zeotropic Refrigerant Mixture for High-Temperature Heat Pumps
Bibliographic record
Abstract
The increasing need for energy-efficient and sustainable high-temperature heat pump systems (HTHPs) has led to extensive research into working fluids with optimal thermodynamic performance.Among various refrigerants, carbon dioxide is a promising candidate due to its favourable effects on the environment, good thermophysical properties, and economic feasibility.However, its low critical temperature and high operating pressure pose significant challenges on efficiency of the system.One of the methods to explore the use of CO as a refrigerant is to mix it with other hydrocarbons and form zeotropic refrigerant mixtures.Thus, the present study investigates the performance of CO-based zeotropic refrigerant mixtures with six hydrocarbons.Key performance indicators, including coefficient of performance (COP), sink outlet temperature, pressure ratio, and Lorenz efficiency were evaluated for the selected mixtures at different source inlet temperatures.The results indicated that at a source inlet temperature of 90C, a CO/butane mixture delivers a sink outlet temperature of 147.3C with a COP of 5.73, making it a strong candidate for temperature < 140C.Additionally, CO/butane exhibits the lowest pressure ratio and highest Lorenz efficiency of 77.7% at 90C source inlet temperature, exhibits reduced compressor workload and improving overall efficiency.For high-temperature applications exceeding 150C, CO/acetone emerges as the most suitable mixture.At the maximum source inlet temperature, it achieves a sink outlet temperature of 188.76C with a highest COP of 6.41 among all tested mixtures.Those findings highlight the potential of CObased zeotropic mixtures to enhance HTHPs performance by reducing exergy destruction and improving heat exchanger thermal matching without the need for complex system modifications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".