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Record W4385186750 · doi:10.1080/10934529.2023.2238586

Transient simulation of SO<sub>2</sub> absorption into water in a bubbling reactor

2023· article· en· W4385186750 on OpenAlexaff
Yuyang Cai, Zhen Wang, Dunyu Liu, Jun Chen, Jing Jin, Qi Qin, Ke Liu, Haixiang Hu, Sijie Li, Huancong Shi

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2023
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsScrubberEntrainment (biomusicology)BubbleAbsorption (acoustics)Water modelMechanicsChemistryVolumetric flow rateEnvironmental scienceMaterials scienceMolecular dynamics

Abstract

fetched live from OpenAlex

A bubbling reactor is an important type of gas scrubber to reduce SO2 emissions in maritime shipping. Both experiments and simulations were conducted to study the relationship between the periodic gas bubbling process and SO2 concentration at the outlet of the reactor, and the entrainment of liquid droplets on SO2 absorption. The accuracy of the model was verified by comparing the bubble size, the depth of bubbles injected into the water, and the SO2 concentration obtained in both experiments and simulations. The gas bubbling process is accompanied by bubble formation, rise, and collapse. The gas bubbling period is affected by the disturbance of the liquid level. The period of the SO2 concentration at the outlet of the gas bubbling reactor is smaller than that at the gas jar outlet which acts as the gas buffering region. The amounts of water carried out of the bubbling reactor by the gas bubbling process increase with the gas flow rates. The droplets and liquid film in the gas jar and the connecting tube play an important role in the absorption of SO2. This study encourages more research to reduce the fluctuation of SO2 concentration and consider droplet entrainment in the design of bubbling reactors.

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)0.002
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 score0.453
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.280
Teacher spread0.250 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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