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Record W2767877215 · doi:10.1021/acs.iecr.7b03664

Corrosion Mechanisms and Materials Selection for the Construction of Flue Gas Component in Advanced Heat and Power Systems

2017· article· en· W2767877215 on OpenAlexafffund
Yimin Zeng, Kaiyang Li, Robin W. Hughes, Jing‐Li Luo

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of AlbertaNatural Resources Canada
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsFlue gasCorrosionCombustionFossil fuelHigh-temperature corrosionIncinerationFlueWaste managementFlue-gas emissions from fossil-fuel combustionProcess engineeringMaterials scienceEnvironmental scienceEngineeringMetallurgyChemistry

Abstract

fetched live from OpenAlex

With the desire for a clean living environment and the increasing demands for cost competitive energy supply, advanced fuel combustion technologies have been proposed and are being developed. The deployment of these technologies has been hindered by unanticipated corrosion damage within core components (such as boilers and flue gas components) at pilot-scale demonstration plants. To deal with such materials technology challenges, studies have been carried out, but there is still substantial R&D required to meet the emerging industrial demands. A comprehensive review of open database information regarding the corrosion of flue gas systems in existing combustion plants was therefore conducted in this review. It is anticipated that this information will provide a basis for addressing knowledge gaps in materials technologies and advancing the mechanistic understanding of how alloys corrode in flue gas operating environments. Corrosion modes, the effects of aggressive agents (including CO 2, HCl, SO x, and NO x ) in flue gas mixtures, and the performance of candidate metallic materials in typical combustion systems are systemically reviewed and discussed. From corrosion and economic points of view, F/M steels (P91 and P92), austenitic stainless steels (SS317, 254SMO, and 654SMO etc.), and duplex steels (2205 and 2507) are likely to be major candidates for the construction of future flue gas systems in the fossil fuel powered industries. Ni-based alloys, particularly Alloy C-276 and Alloy C-22, are more applicable for flue gas components in biomass and waste-to-energy energy combustion systems.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.419

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 head0.034
GPT teacher head0.279
Teacher spread0.245 · 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

Citations46
Published2017
Admission routes2
Has abstractyes

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