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Record W1989411951 · doi:10.1115/icone18-30201

Thermal Integration Options for Hydrogen Cogeneration With Molten Salt Nuclear Reactors

2010· article· en· W1989411951 on OpenAlexafffund
Z. Wang, G.F. Naterer, Kamiel Gabriel

Bibliographic record

Venue18th International Conference on Nuclear Engineering: Volume 3 · 2010
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCogenerationHeat exchangerHydrogen productionMolten saltHydrogenNuclear engineeringCoolantHeat transferChemistryWaste managementMaterials scienceElectricity generationThermodynamicsPower (physics)Inorganic chemistryEngineering

Abstract

fetched live from OpenAlex

Thermochemical hydrogen cogeneration using heat of molten salt nuclear reactors (MSRs) is discussed in this paper. Sulfur-iodine and copper-chlorine cycles are taken as typical examples for analysis and discussion. It is found that the heat exchanger design is predominately determined by the maximum and range of temperatures of themochemical hydrogen production cycles with MSRs. Copper-chlorine (Cu-Cl) thermochemical cycles can link with most MSRs, but sulfur-iodine (S-I) cycles can only link with very high temperature MSRs. The location of extracted heat from MSRs to S-I and Cu-Cl cycles is investigated, and its influence on the layout of nuclear reactor coolant loop is discussed. Some conceptual designs of heat exchangers are proposed to transfer heat from MSRs to Cu-Cl and S-I cycles. The available heat quantity at different hours of a day and corresponding hydrogen production scales are determined. It is found that the available heat at most hours of power demand in a day is equivalent to the hydrogen cogeneration capacity of an industrial scale steam methane reforming plant, if an MSR power station is operating at an invariable maximum power, independent of an electrical load throughout a day or year.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.835

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)0.0010.000

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 head0.215
Teacher spread0.202 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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