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Record W2559749434 · doi:10.1115/1.4035335

Thermo-Economic Performance of a Cogeneration Medium–Small Modular Nuclear Reactor Plant in Canada

2016· article· en· W2559749434 on OpenAlexaffabout
Gaoming Ge, Tomohiko Ikegawa, Koji Nishida, Carey J. Simonson

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCogenerationElectricityModular designNatural gasWaste managementEnvironmental scienceProcess engineeringElectricity generationNuclear engineeringEngineeringComputer scienceElectrical engineeringThermodynamicsPhysicsOperating system

Abstract

fetched live from OpenAlex

Hitachi-GE developed a 300 MWel class modular simplified and medium small reactor (DMS) concept, and the DMS was originally designed for generating electricity only. In this study, the feasibility of a cogeneration DMS plant which supplies both electricity and heat is under investigation. The thermal performance of the DMS plant without or with low-, medium-, or high-temperature thermal utilization (TU) applications is evaluated by numerical simulations. The results show that the electricity generated reduces as the heating requirement of TU application becomes higher. Furthermore, the economic performance of the cogeneration DMS plant is compared with another two integrated systems: (i) DMS plus electric boilers and (ii) DMS plus natural gas boilers, for those three TU applications in Canada. The results illustrate that the DMS plus natural gas boilers system are most economic if there is no carbon tax, but with high-CO2 emissions (up to 180 kton per year). The cogeneration plant performs best as the carbon tax increases up to $40/ton. The cogeneration DMS plant is a promising scheme to supply both electricity and heat simultaneously in the economic-environmental point of view.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.535

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 head0.004
GPT teacher head0.159
Teacher spread0.155 · 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 designSimulation or modeling
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
Published2016
Admission routes2
Has abstractyes

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