Assessment of Blends of CO2 with Hydrocarbons as a Zeotropic Refrigerant for HTHPs
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 efficiency challenges.One of the methods to explore the use of CO₂ as a refrigerant is to mix it with other hydrocarbons and form zeotropic refrigerant mixtures.Thus, the present study investigates the performance of CO₂-based zeotropic refrigerant mixtures with six hydrocarbons.Key performance indicators, including coefficient of performance (COP), sink outlet temperature, Lorenz efficiency, and condensation heat transfer coefficient, were evaluated for the selected mixtures at different source inlet temperatures.The results indicated that at a source inlet temperature of 90°C, a CO₂/butane mixture delivers a sink outlet temperature of 147.3°C with a COP of 5.73, making it a strong candidate for moderate temperature applications.Additionally, CO₂/butane exhibits the lowest pressure ratio and highest Lorenz efficiency of 77.7% at 90°C source inlet temperature, exhibits reduced compressor workload and improving overall efficiency.For high-temperature applications exceeding 150°C, CO₂/acetone emerges as the most suitable mixture.At a source inlet temperature of 130°C, it achieves a sink outlet temperature of 188.76°C with a highest COP of 6.41 among all tested mixtures.Additionally, CO₂/acetone exhibits a high condensation heat transfer coefficient (1706.9W/m²K at the same source inlet temperature) leading to lower exergy destruction and enhanced thermal efficiency.Those findings highlight the potential of CO₂-based zeotropic mixtures to enhance HTHP 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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".