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Record W1943799579 · doi:10.1002/cjce.22244

Hot Gas Clean‐Up with Dolomites: Effect of Gas Composition on Sulfur Removal Activity

2015· article· en· W1943799579 on OpenAlexvenueno aff
Şiringül Ay, Hüsnü Atakul, Alper Sarıoğlan, Fehmi Akgün, Işıl Işık‐Gülsaç, Yeliz Çetin, Ersin Üresin, Ömer Orçun Er, Parvana Aksoy

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsGas compositionSulfurComposition (language)Environmental scienceChemistryMaterials scienceMetallurgyArtPhysics

Abstract

fetched live from OpenAlex

In this study high temperature desulfurization performance of dolomite was investigated for coal‐derived gases. Experimental results indicated that dolomite was active and could be effectively used for gas desulfurization at temperatures greater than 1023 K. However, due to thermodynamic equilibrium restrictions, in the presence of water vapour the H 2 S concentration could not be reduced below 150–200 ppmv by dolomite. Thermodynamics predicts that water vapour can adversely affect the chemical equilibrium between H 2 S and calcium oxide. The presence of CO 2 in the gas stream did not have a suppressing effect on the sulfidation reactions, whereas it tended to depress the calcination of CaCO 3 to CaO. During the dolomite‐based sulfur removal process, both the water‐gas shift and reverse water‐gas shift reactions could also occur depending on the reacting gas compositions. The Boudouard reaction could take place and proceed at 1023 K depending on the CO/CO 2 ratio in the feed stream. This was dictated by the thermodynamic characteristics of the reaction. COS formation was observed due to the reactions taking place between H 2 S and CO/CO 2 during H 2 S removal by dolomite. Trace amounts of methyl mercaptan were also detected in the reactor effluent gases during H 2 S removal. Methyl mercaptan formation might be attributed to the reactions taking place between H 2 S, H 2 , and CO and/or CO 2 .

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)0.001
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 score0.351
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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