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Record W4400232265 · doi:10.1016/j.psep.2024.07.007

CFD modeling of H2S removal from liquid sulfur in a pilot-scale gas-liquid contactor

2024· article· en· W4400232265 on OpenAlexafffund
Qianqian Nie, Mengdie Wang, Guangyuan Xie, Zhongchao Tan, Hesheng Yu

Bibliographic record

VenueProcess Safety and Environmental Protection · 2024
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNational University's Basic Research Foundation of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsContactorComputational fluid dynamicsSulfurLiquid liquidSCALE-UPScale (ratio)Process engineeringEngineeringEnvironmental scienceWaste managementPetroleum engineeringMaterials scienceChromatographyChemistryThermodynamicsPhysicsAerospace engineeringMetallurgy

Abstract

fetched live from OpenAlex

Removal of hydrogen sulfide (H 2 S) from liquid sulfur is essential to product quality and safe operation in sulfur processing recovery. This paper presents computational fluid dynamics (CFD) models of sulfur degassing performance in a pilot-scale gas-liquid contactor. Two mass transfer models with and without considering the decomposition of hydrogen polysulfide (H 2 S x ) to H 2 S are established. They are then used to explore the effects of various operating parameters, including residence time, injected liquid sulfur flow rate, impeller type, and agitation speed, on degassing efficiency. Results show that the relative errors between calculation and experimental data are within ±6.2 %. Furthermore, the impeller geometry is optimized, and the efficiencies of three degasser shapes are compared using the verified CFD tool. The optimized impeller structure can increase degassing efficiency by about 9 % compared to the original one. The CFD model can be further improved to guide the design and scale-up of diverse H 2 S degassers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.694

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 head0.014
GPT teacher head0.200
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations1
Published2024
Admission routes2
Has abstractyes

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