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Record W3158724453 · doi:10.3906/kim-2008-4

The effect of H2O on the sulfation of Havelock limestone under oxy-fuel conditions in a thermogravimetric analyser

2021· article· en· W3158724453 on OpenAlexaff
Murat Varol, Edward J. Anthony, Arturo Macchi

Bibliographic record

VenueTURKISH JOURNAL OF CHEMISTRY · 2021
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of OttawaNatural Resources Canada
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsChemistryThermogravimetric analysisSulfationCombustionAnalyserKineticsMass transferAnalytical Chemistry (journal)Organic chemistryChromatography

Abstract

fetched live from OpenAlex

A gas mixture representing oxy-fuel combustion conditions was employed in a thermogravimetric analyser to determine the effect of water vapor and SO$_{2}$ concentration on limestone sulfation kinetics over the temperature range of 800 to 920 °C. Here, experiments used small samples of particles (4 mg), with small particle sizes (d$_{p}$ < 38 μm) and large gas flow rates (120 mL/min@ NTP) in order to minimize mass transfer interferences. The gas mixture contained 5000 ppm$_{v}$ SO$_{2}$, 2% O$_{2}$, and the H$_{2}$O content was changed from 0% to 25% with the balance CO$_{2}$. When water vapor was added to the gas mixture at lower temperatures (800-870 °C), the limestone SO$_{2}$ capture efficiency increased. However, as the temperature became higher, the enhancement in total conversion values decreased. As expected, Havelock limestone at higher temperatures (890 °C, 920 °C, and 950 °C) experienced indirect sulfation and reacted at a faster rate than for lower temperatures (800-870 °C) for direct sulfation over the first five minutes of reaction time. However, the total conversion of Havelock limestone for direct sulfation was generally greater than for indirect sulfation.

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 score0.005
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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