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Record W4304892390 · doi:10.1002/cjce.24717

Regenerative low‐cost ionic liquids: Synthesis, characterization, and application in desulphurization of flue gases

2022· article· en· W4304892390 on OpenAlexvenueno aff
Avanish Kumar, Vinod Kumar Dhakad, Susanta Kumar Jana

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
Fundersnot available
KeywordsFlue-gas desulfurizationChemistryIonic liquidFlue gasAmmoniumFourier transform infrared spectroscopyVolume (thermodynamics)Chemical engineeringMaterials scienceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Three ionic liquids (ILs) containing the same cation but different anions were synthesized and used to separate SO 2 from its mixture with air in a semi‐batch bubble column absorber. Desulphurization efficiency was determined using a state‐of‐the‐art experimental set‐up relevant to chemical process industries concerning the important process variables. The virgin and exhausted ILs were characterized using TGA, FTIR, and 1 H NMR analyses. The concentrations of SO 2 at the inlet to and exit from the absorber were measured using a ZRJFAY36, Fuji, Japan infrared SO 2 analyzer. A maximum of 92 percent removal efficiency (PRE) was achieved from a feed gas containing 250–1500 ppm SO 2 with the use of 2‐hydroxyethyl ammonium lactate [MEA][L] or tri (2‐hydroxyethyl) ammonium lactate ([TEA][L]), as the absorbents. This could be further enhanced by the interactive effect of the simultaneous increase in the superficial velocity of the gas and the volume of IL fed into the absorber. However, PRE was about 20% less with tri (2‐hydroxyethyl) ammonium salicylate ([TEA][S]). It was observed that the ILs, [MEA][L] and [TEA][L], were fairly suitable for flue gas desulphurization in industrial situations. A design criterion for continuous operation using two semi‐batch absorbers has been proposed.

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 score0.216
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
Teacher spread0.169 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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