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Record W2753575286 · doi:10.1002/cjce.23012

Effect of sorbent reactivity on flue gas desulphurization in fluidized‐bed boilers under air firing mode

2017· article· en· W2753575286 on OpenAlexvenueno aff
A. Szymanek, Margarita de Las Obras Loscertales, Anna Pajdak

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
FundersMinisterio de Ciencia e InnovaciónFederación Española de Enfermedades Raras
KeywordsSorbentSorptionFlue-gas desulfurizationFlue gasChemistryFluidized bed combustionReactivity (psychology)CombustionBoiler (water heating)Chemical engineeringWaste managementAdsorptionOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The article presents research into the reactivity and SO 2 sorption abilities of calcareous sorbents, which vary in quality. Two sorbents were used: limestone and travertine, with grain sizes between 0.125 and 0.250 mm. The sorbent reactivity tests were performed on a laboratory test stand. The reactivity index and the conversion degree were also determined. SO 2 sorption studies were conducted in an industrial boiler. The desulphurization efficiency was determined taking into the account the content of calcium and sulphur put into and taken out of the boiler. The relation of the desulphurization process parameters such as fuel sulphur content, stream sorbent, sorbent utilization degree, desulphurization efficiency, depending on the molar ratio Ca/S and the duration of the process was specified. The SO 2 sorption results were compared with the reactivity index of the sorbents. The investigation in dry combustion‐gas desulphurization methods showed the high quality of the sorbents whose reactivity index is above 3.5.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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