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Record W2914915390 · doi:10.22215/etd/2018-13325

An Experimental Evaluation of Fixed and Fluidized Beds of Zeolite 13X for the Application of Compact Thermal Energy Storage

2018· dissertation· en· W2914915390 on OpenAlexafffund
Dylan Bardy

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaLeidos
KeywordsAdsorptionZeoliteWater vaporMaterials scienceFluidizationFluidized bedMolecular sieveScalingRelative humidityHumidityChemical engineeringVolumetric flow rateThermodynamicsWaste managementChemistryOrganic chemistryEngineeringMathematicsCatalysis

Abstract

fetched live from OpenAlex

For thermal energy storage technologies based on physical adsorption to become a commercially viable option in the future, particular advancements in the research and development of the system's components are required to complement existing research in advanced materials. To investigate the application of fluidization as a solid-gas contacting method for low-temperature thermochemical energy storage, a bench-scale adsorption-based TES system was designed, constructed, instrumented, and commissioned. In demonstrating this technology, the objective of this research was to obtain thermodynamic data for the adsorption of water vapour onto zeolite 13X under fluidization to evaluate fluidized beds as potential reactor or adsorber designs. Multiple adsorption experiments were performed on samples of an 8x12 and 60x65 mesh zeolite 13X molecular sieve, comparing the effects of air flow rate and concentration of water vapour on the breakthrough and temperature lift on the energy density of fixed and fluidized adsorbent beds. Variation of the air flow rate from 10 to 30 L/min had little effect "If the fool would persist in his folly he would become wise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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