Effects of Physical and Sorption Properties of Desiccant Coating on Performance of Energy Wheels
Bibliographic record
Abstract
Desiccant-coated energy wheels are rotary-air-to-air energy exchangers widely used in ventilation systems to reduce the energy consumption required in industrial environments and commercial buildings. In this study, the effects of silica gel microphysical properties, i.e., pore width (Pw), specific surface area (SA), and particle size (dp), on the moisture recovery efficiency (latent effectiveness) of energy wheels are investigated. Three silica gel samples with different particle size and pore width (55 μm–77 Å, 150 μm–63 Å, and 160 μm–115 Å) are selected to coat small-scale energy exchangers. The sorption performance of the exchangers is determined from their normalized humidity response to a step increase in the inlet humidity at different flow rates. The results demonstrate that the transient humidity response is mainly specified by the desiccant pore size distribution, specific surface area, and mass of the coating. The transient analytical model is used to calculate the latent effectiveness (ɛL) of the exchangers from the transient humidity response. It was found that the exchanger coated with the smallest pore width (63 Å) has the highest available surface area and the highest latent effectiveness. With almost the same particle size (dp = 150 μm and 160 μm), the latent effectiveness increases by 5% (at wheel speed 20 rpm and Re = 174) as the pore width reduces from 150 Å to 63 Å. Increasing the particle size from 55 μm to 150 μm with almost the identical pore width (Pw = 63 Å and 77 Å) results in a slight enhancement in the latent effectiveness. ɛL is also calculated for correlated data (Yoon–Nelson model) where the results agree within experimental uncertainty bounds.
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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 | 0.000 |
| 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".