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Record W2012076916 · doi:10.1115/power2011-55097

Economy Analysis of Electricity Production From Hydrogen in Combination With Nuclear Power Plant

2011· article· en· W2012076916 on OpenAlexaboutno aff
Jurij Avsec, Peter Virtič, Tomazˇ Zˇagar, Luka Sˇtrubelj

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicRenewable energy and sustainable power systems
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogen technologiesRenewable energyHydrogen productionNuclear powerHydrogen economySteam reformingElectricityEnergy carrierEnvironmental scienceElectricity generationHydrogen fuelWaste managementProduction (economics)Energy technologyPower to gasNuclear power plantProcess engineeringHydrogenEnvironmental economicsEngineeringPower (physics)ChemistryEconomicsElectrical engineering

Abstract

fetched live from OpenAlex

Efficient and sustainable methods of clean fuel and energy production are needed in all countries of the world in the face of depleting oil reserves and the need to reduce carbon dioxide emissions. Some countries are developing technologies that could be named zero carbon technologies. The presented article will show how hydrogen technologies could be implemented with renewable technologies and nuclear technology. Nuclear technology produce very cheap electricity and could produce also cheap energy like heat and vapour. This technology should be used in nuclear power plants to develop other products like hydrogen, biofuels or district heating. One of the biggest opportunities for nuclear energy technology is to produce hydrogen. Some countries like Canada and US are in preparation to build hydrogen villages. However, a key missing element is a large-scale method of hydrogen production [1–5]. As a carbon-based technology, the predominant existing process (steam-methane reforming (SMR)) is unsuitable. This paper focuses on a production of hydrogen in connection with a nuclear power plant. We will show the technologies which allow the coupling between a nuclear power plant and hydrogen technologies.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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