Performance of an adsorption cooling system using MOF-303 adsorbent: Mathematical modelling using experimentally measured properties
Bibliographic record
Abstract
Adsorption cooling systems (ACS) offer several advantages over traditional vapor compression systems, primarily due to their sustainability and compatibility with renewable energy sources. As published previously, Metal-organic frameworks (MOFs), particularly MOF-303, present superior sorption properties in water-related applications compared to conventional adsorbents like silica gel. Key benefits of MOFs include higher surface area, tunable pore sizes, and enhanced adsorption capacities, which help in enhancing the efficiency and overall cooling system performance. MOF-303, in particular, has demonstrated high water vapor adsorption capacity under atmospheric conditions and rapid diffusion rates, making it a promising candidate for (ACS) applications. This study experimentally investigates the water vapor diffusion rates of MOF-303 and theoretically evaluates the performance of a two-bed adsorption cooling cycle using cyclic lumped modeling. The experimental results indicate that MOF-303 exhibits an adsorption rate approximately seven times greater than that of silica gel. A comparative analysis shows that MOF-303 has a significantly higher capacity for both the adsorption and desorption of water vapor, which directly enhances the cooling cycle performance. Operating at heating water temperatures between 75°C and 90°C, MOF-303 achieves a maximum coefficient of performance (COP) of 0.62, increasing by 13% compared to silica gel. Specific cooling power (SCP) also improves significantly, rising from 440 W/kg to 970 W/kg as the heating water temperature increases. Chilled water mass flow rate enhancements further increase COP and SCP, with values reaching 0.63 and 952 W/kg, respectively, at 2.5 kg/s.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".