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Record W2934109403 · doi:10.32964/tj15.3.195

Formation of blue deposits in kraft recovery boilers

2016· article· en· W2934109403 on OpenAlexfundno aff
Xiaosong Mao, Honghi Tran

Bibliographic record

VenueTAPPI Journal · 2016
Typearticle
Languageen
FieldChemistry
TopicPigment Synthesis and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsOxidizing agentManganeseSodium carbonateKraft paperCarbonateSuperheaterBoiler (water heating)ChemistryMetallurgyMineralogyKraft processReducing atmosphereSodiumGeologyWaste managementMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Fireside deposits in recovery boilers are typically white, red, pink, grey, black, or occasionally yellow, depending on where they are in the boiler, the mechanisms by which they are formed, and the environment to which they are exposed. Although rare, blue deposits have been reported, and some were “bluer” than others. This study systematically examines the cause of the blue coloration of deposits in recovery boilers. The results show that for a deposit to become blue, it must a) contain sodium carbonate, b) contain a small amount of manganese, c) be molten or partially molten, and d) have exposure to an oxidizing atmosphere. Because deposits always contain sodium carbonate and manganese, these requirements suggest that blue deposits can form only in the superheater region of the recovery boiler when oxidizing conditions prevail. Blue coloration is thus more likely to be observed in boilers operating at a reduced firing load with a high excess oxygen target.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.208
Teacher spread0.191 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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