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Record W4366116788 · doi:10.1615/icpws-1994.1030

DETERMINATION OF LOW-LEVEL CARBONIC ACID IN THE STEAM-CONDENSATE CYCLES OF FOSSIL AND NUCLEAR POWER PLANTS

2023· article· en· W4366116788 on OpenAlexaboutno aff
Sylvie Charbonneau, Roland Gilbert, Louis Lépine, Jan Stodola

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonic acidStress corrosion crackingCorrosionChemistryBrinePressurized water reactorNuclear power plantEnvironmental scienceMaterials scienceMetallurgyNuclear engineeringNuclear physicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Carbonic acid (H<sub>2</sub>CO<sub>3</sub>) has recently been identified as one of the contaminants contributing to stress corrosion cracking of low-pressure turbine disks in addition to being involved in low-pH erosion-corrosion of steam and return lines and corrosion in the air-cooling zone of water­tube condensers. Many attempts to establish the exact amount of H<sub>2</sub>CO<sub>3</sub> in these systems were not totally satisfactory because of a lack of a selective, accurate and sensitive technique to assess low microgram per litre levels. In this work, a new technique based on non-suppressed ion chromatography has been successfully applied for examining the H2CO3 distribution in samples collected at Hydro-Quebec's Gentilly 2 nuclear power plant and Ontario Hydro's Nanticoke fossil plant. Performed for two random conditions, the analyses revealed carbonic acid levels ranging from 5 to 74 &mu;g/L with a specific distribution for each plant and a dependence of the relative volatility on the temperature, pressure and/or chemistry. Finally, the two Canadian plants investigated in the present study have shown comparable H<sub>2</sub>CO<sub>3</sub> levels with a German PWR plant operating under an AVT control based on hydrazine addition.

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 score0.591
Threshold uncertainty score0.142

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 head0.019
GPT teacher head0.233
Teacher spread0.215 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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