Enhanced energy storage density in thermal energy storage systems simultaneously heated with solar radiation and industrial waste heat
Bibliographic record
Abstract
• Adsorbents charged with solar and waste heat simultaneously. • Synergistic improvement by using two heat sources is experimentally demonstrated. • Novel adsorbent bed utilizes low-grade heat sources more efficiently. Adsorbent-based thermal energy storage (ATES) systems can provide high energy storage densities for long durations. However, abundantly available thermal energy sources, such as industrial waste heat and solar energy, often do not provide enough heat to effectively charge ATES systems. Herein experiments are performed to investigate the benefits of using simulated solar radiation and waste heat simultaneously to charge zeolite 13X for ATES applications. The energy storage density (ESD) for three cases is determined: 1) the adsorption bed is heated with simulated solar radiation alone, 2) the adsorption bed is heated using simulated waste heat alone, and 3) the adsorption bed is heated using simulated solar radiation and waste heat simultaneously. When simulated solar radiation is the sole source of thermal energy, the ESD is 5.6 kWh/m 3 . When the adsorbent bed is charged using waste heat the ESD is 7.6 kWh/m 3 . However, when both solar-simulated radiation and waste heat are used simultaneously to charge the adsorbent bed the ESD is 18.9 kWh/m 3 . The results show that using both solar and waste heat at the same time to charge the adsorbent bed is a promising strategy for improving the performance of ATES systems.
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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".