Heat and Mass Transfer Characteristics of a Zeolite 13X/CaCl2 Composite Adsorbent in Adsorption Cooling Systems
Bibliographic record
Abstract
The performance of the adsorption cooling system using the zeolite 13X/CaCl2 composite adsorbent was studied using a numerical simulation. The novel zeolite 13X/CaCl2 composite adsorbent with superior adsorption properties was developed in previous studies [11]. It has high equilibrium water uptake of 0.404 g/g between 25°C and 100°C under 870Pa. The system specific cooling power (SCP) and coefficient of performance (COP) were successfully predicted for different operation parameters. The simulated COP with the composite adsorbent is 0.76, which is 81% higher than a system using pure zeolite 13X under desorption temperature of 75°C. The SCP is also increased by 34% to 18.4 W/kg. The actual COP can be up to 0.56 compared to 0.2 for zeolite 13X-water systems, an increase of 180%. It is predicted that an adsorption cooling system using the composite adsorbent could be powered by a low grade thermal energy source, like solar energy or waste heat, using the temperature range of 75°C to 100°C. The performance of the adsorber with different design parameters was also studied in the present numerical simulation. Adsorbents with smaller porosity can have higher thermal conductivity and may result in better system performance. The zeolite bed thickness should be limited to 10mm to reduce the thermal response time of the adsorber. Addition of high thermal conductivity materials, for example carbon nanotube, can also improve the performance of the adsorber. Multi-adsorber tube connected in parallel can be employed to provide large heat transfer surface and maintain a large SCP and COP. The desorption temperature also showed a large effect on the system performance.
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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 | 0.000 |
| 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 | 0.000 |
| 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".