Adsorption Prediction and Modeling of Thermal Energy Storage Systems: A Parametric Study
Why this work is in the frame
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Bibliographic record
Abstract
Thermal energy storage allows for the storage of energy from intermittent sources to correct for the variable supply and demand. The current work investigates adsorption technology for thermal energy storage through the development of a theoretical model, which describes the material and energy transfers in the system. The theoretical model was used to conduct a parametric study which examines the effect of column dimension, particle diameter, adsorption activation energy, flow rate, column void fraction, and adsorbent heat of adsorption on the thermal energy storage system performance. It was found that an optimal column length to column diameter ratio of 1.4, a column diameter to particle diameter ratio of 14.7, a flow rate of 24 LPM, and a void fraction of 0.4 gave the best thermal energy performance for a column volume of 6.275 × 10 –5 m 3 . Also, a low activation energy and a high heat of adsorption represent the best adsorption parameters for optimal temperature outputs, breakthrough behavior, and energy densities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it