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Record W2985490418 · doi:10.1002/cjce.23677

Experimental investigation of high‐temperature corrosion properties in simulated reducing‐sulphidizing atmospheres of the waterwall fireside in the boiler

2019· article· en· W2985490418 on OpenAlexvenueno aff
Ligang Xu, Yaji Huang, Jian Wang, Changqi Liu, Lingqin Liu, Lei Zou, Junfeng Yue, Kuixu Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsCorrosionHigh-temperature corrosionBoiler (water heating)MetallurgyMaterials scienceReducing atmosphereOxideCombustionNOxWaste managementChemistry

Abstract

fetched live from OpenAlex

Abstract The presence of reducing atmospheres of the waterwall fireside in the boiler due to extensive use of low‐NOx combustion mode caused severe high‐temperature corrosion problems. In this study, high‐temperature sulphur corrosion properties of two types of low alloyed waterwall steel (15CrMoG and 12Cr1MoVG) in two kinds of reducing‐sulphidizing atmospheres were investigated by lab‐scale experiments. The experimental atmospheres and temperature were simulated according to field measurements in the boiler of a thermal power plant. The experimental results showed that the reducing‐sulphidizing atmosphere with CO was more corrosive, and the CO accelerated the corrosion of H2S and worsened the corrosion. The CO inhibited the formation of oxide scales, provided some corrosion resistance, and produced the intensely corrosive COS. The corrosivity of the alkali metal chlorides was limited in the reducing‐sulphidizing atmosphere without CO, while the corrosion was strongly hindered in the reducing‐sulphidizing atmosphere with CO.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

Explore more

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