Simulation of Water Vapor Adsorption in a Fixed-bed Column withSilica Gel Material for Thermal Energy Storage Applications
Bibliographic record
Abstract
In order to reduce the environmental impact of fossil fuels and transition to a low-carbon economy, researchers around the world have been investing heavily in the development of renewable energy technologies.However, the renewable energy sources (e.g.wind power and solar energy) have the major constraint of intermittency and lack of consistency.Therefore, energy storage technologies play an important role in balancing the misalignment between the energy supply and demand, and creating a more flexible and reliable energy system [1].Water vapor adsorption in porous materials in a fixed bed column can be used in the thermal energy storage (TES) systems for space heating applications.Various adsorbent materials have been tested and evaluated for their thermal energy storage abilities by our research group [2]- [6].Silica gel has proven to be one of the better adsorbents with high energy storage density using low regeneration temperatures [5].To gain a better understanding of this exothermic adsorption process, a mathematical model has been developed in this study, to simulate the water vapor adsorption process of silica gel material from humid air.The model included the mass and energy balances with equations to take into account the adsorption isotherms, the pressure-drop in the system, the heat of adsorption released during the process and the heat loss to the surroundings.The validated model can be used to optimize the TES system design and predict the TES system's performance under operating conditions that we are not able to create in our laboratories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".