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Record W2097983792 · doi:10.1002/aic.11904

Preliminary evaluation of the performance of an adsorption‐based hydrogen storage system

2009· article· en· W2097983792 on OpenAlexaff
Marc‐André Richard, Daniel Cossement, Patrick‐Adam Chandonia, Richard Chahine, Daigoro Mori, Katsuhiko Hirose

Bibliographic record

VenueAIChE Journal · 2009
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHydrogen storageAdsorptionLiquid hydrogenStorage tankCompressed hydrogenNuclear engineeringHydrogenMaterials scienceThermal energy storageResidualEnvironmental scienceWaste managementProcess engineeringChemistryThermodynamicsEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Using modeling and thermal simulations, the feasibility of an adsorption‐based hydrogen storage system for vehicles is evaluated. The storage capacity of a 150 L tank filled with a high surface‐area activated carbon is mapped for temperatures from 60 to 298 K and pressures up to 35 MPa. The thermal simulations are verified using experiments. For a storage capacity target of 5 kg, the adsorption‐based storage system will offer a storage advantage over the cryogenic gas storage if the residual mass of hydrogen in the tank is retrieved by heating. For a discharge rate of 1.8 g/s, the required heat is of the order of 500 W. The net energy requirements for the refueling has contributions from compression, precooling and tank cooling and can approach that for liquid hydrogen storage. With a good insulation and a maximum tank pressure of 35 MPa, the dormancy period can be extended to several weeks. © 2009 American Institute of Chemical Engineers AIChE J, 2009

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2009
Admission routes1
Has abstractyes

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