An experimental comparison of thermal energy storage in directly and indirectly radiated adsorbent beds integrated with solar thermal collectors
Bibliographic record
Abstract
Abstract Adsorbents heated using solar energy can be used to achieve thermal energy storage and sorption refrigeration with low environmental impacts. This research compares two different methods of heating adsorbents with solar energy to store thermal energy: (1) by exposing the adsorbents to incident light transmitted through a solar collector window, and (2) by heating a highly absorbing solar collector cover, and then transferring the heat from this solar absorber to adsorbents located beneath it. To carry out this comparison experiments are conducted for three cases of adsorbent beds using zeolite 13X and water as the adsorbent‐adsorbate pair. In the first case, the top of the adsorbent bed is a polycarbonate sheet, and the zeolites are heated directly by solar‐simulated light transmitted through this sheet. In the second case, a blackened aluminum sheet is placed beneath the polycarbonate sheet to generate heat by absorbing incident light. For the third case, the blackened aluminum absorber is placed directly on top of the zeolite beads and the absorber is isolated from the walls of the reactor to avoid heat losses. The outcomes reveal an energy storage density (ESD) of 43.6 kWh/m3 (63.4 Wh/kg) when light is directly incident onto the zeolite 13X and an ESD of 33.3 kWh/m3 (48.4 Wh/kg) when light is incident onto a blackened absorber plate that transfers heat to Zeolite beads residing beneath it. However, ESD values were improved to 48.9 kWh/m3 (71.0 Wh/kg) when the blackened absorber plate was thermally insulated from the walls of the adsorbent bed. These results demonstrate the importance of an optimal absorber arrangement in enhancing the adsorption process for the purpose of elevating energy storage densities.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".