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Record W2173932025 · doi:10.1504/ijpse.2015.071413

Analytical and experimental investigation of thermal efficiency improvement of thermochemical water splitting for hydrogen production

2015· article· en· W2173932025 on OpenAlexaff
Samane Ghandehariun, Marc A. Rosen, G.F. Naterer, Zhaolin Wang

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsMemorial University of NewfoundlandOntario Tech UniversityUniversity of Alberta
Fundersnot available
KeywordsMolten saltHydrogen productionHydrogenThermalMaterials scienceThermochemical cycleHeat recovery ventilationThermodynamicsThermal efficiencyNuclear engineeringChemistryMetallurgyHeat exchangerEngineering

Abstract

fetched live from OpenAlex

This paper examines heat recovery in a thermochemical Cu-Cl cycle for efficient hydrogen production. It is essential to recover heat within the Cu-Cl cycle to improve the overall thermal efficiency of the cycle. A major portion of heat recovery can be achieved by cooling and solidifying the molten salt exiting an oxygen reactor. Heat recovery from the molten salt is achieved by dispersing the molten stream into droplets. In this paper, an analytical study and experimental investigation of the thermal phenomena of a falling droplet quenched into water is presented, involving the droplet surface temperature during descent and resulting composition change in the quench process. The results show it is feasible to quench the molten salt droplets for an efficient heat recovery process without introducing any material imbalance for the overall cycle integration.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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