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Record W133727385 · doi:10.5006/c2008-08193

Some Aspects of Materials Selection for Condensing Economizers

2008· article· en· W133727385 on OpenAlexaff
J.R. Kish, Neville Stead, Douglas Singbeil, Fernando Preto, Francois R Jette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsNatural Resources CanadaDomtar (Canada)FPInnovations
Fundersnot available
KeywordsEconomizerSelection (genetic algorithm)Materials scienceMetallurgyProcess engineeringComputer scienceEngineeringMechanical engineeringArtificial intelligenceHeat exchanger

Abstract

fetched live from OpenAlex

Abstract The use of a condensing economizer within a biomass combustion system is a potentially attractive heat recovery solution for the pulp and paper industry. Selecting materials from which to construct condensing economizers for installation within utility power boiler units is, however, not trivial considering that aqueous sulphuric acid (H2SO4) condensates are typically formed. The expected corrosiveness of the flue gas condensate derived from pulp and paper biomass fuels is likely higher than that derived from natural gas (lower sulphur and chlorine contents), but lower than that derived from fossil fuels (higher sulphur and chlorine contents). To help guide materials selection, a laboratory corrosion testing program was initiated to evaluate the corrosion resistance of candidate alloys to synthetic acidic flue gas condensates expected to be derived from pulp and paper biomass fuels. The initial work, which is reported here, was focused on evaluating the resistance of carbon steel and stainless steel to dewpoint corrosion in chloride-free aqueous H2SO4 condensates and to stress corrosion cracking in a saturated aqueous ammonium nitrate (NH4NO3) solution.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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