Adsorption‐Based Thermal Energy Storage Using Zeolites for Mobile Heat Transfer
Bibliographic record
Abstract
ABSTRACT The utilization of the water–zeolite pair as an adsorbate–adsorbent system has garnered significant attention in the realm of thermochemical energy storage, offering great potential for various applications. Despite promising results in laboratory settings, widespread implementation of this technology has yet to be realized. Recent advancements in mobile thermal energy storage (m‐TES) employing thermochemical materials have opened new avenues for enhancing the practicality and cost‐effectiveness of solar thermal energy harnessing and waste heat recovery. This experimental study investigates the feasibility of storing thermal energy in zeolites, charged externally to the heat recovery reactor, and discusses the potential applications of externally charged zeolites for m‐TES over short distances, shedding light on their practicality and significance in advancing the field of mobile thermal energy storage. Our findings reveal that zeolites charged at 200°C and subsequently stored outside the discharging unit exhibit an impressive energy storage density (ESD) exceeding 110 kWhth/m3 under conditions of 0.45 m/s air velocity and 60% relative humidity during zeolite discharging. These ESD values are comparable to previously reported figures in the literature. Moreover, ESD values of 30.6 kWhth/m3 were achieved by charging zeolite beads contained within packed transportable tubes constructed from stainless‐steel mesh.
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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".