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Record W2314375411 · doi:10.1021/ef200199r

<i>Ex Situ</i> Dissolution of CO<sub>2</sub>: A New Engineering Methodology Based on Mass-Transfer Perspective for Enhancement of CO<sub>2</sub> Sequestration

2011· article· en· W2314375411 on OpenAlexaff
Sohrab Zendehboudi, Asif Abdullah Khan, Stephen Carlisle, Yuri Leonenko

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDissolutionMass transferSupercritical fluidBrineCarbon sequestrationCarbon dioxideChemical engineeringConvective mixingMaterials scienceChemistryConvectionThermodynamicsPetroleum engineeringGeologyChromatography

Abstract

fetched live from OpenAlex

A new methodology is proposed for the acceleration of CO 2 dissolution to lower the risk of CO 2 leakage for carbon capture and storage (CCS) technology. It is called ex situ dissolution because CO 2 is being dissolved at a surface before it is injected underground. This new approach reduces or eliminates possible leakage of CO 2 from underground formation. To achieve full underground dissolution of injected pure supercritical CO 2 or gaseous CO 2 may take thousands of years because of the absence of strong mixing (convective-diffusion dominated processes). Dissolving CO 2 in brine before injection significantly increases the security of geological sequestration. The mass transfer from CO 2 droplets into brine during cocurrent (CO 2 –brine) horizontal pipe flow is studied mathematically to investigate the effectiveness of the proposed method. The dissolution rate of the CO 2 droplets is correlated to the variation of mean droplet diameter versus time, because the mass transfer causes shrinkage of the droplets. Empirical correlations based on Sherwood numbers were employed in the example for calculation of mass-transfer coefficients for droplets of CO 2 in the fluid flowing through a pipe.

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: none
Teacher disagreement score0.506
Threshold uncertainty score0.867

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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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

Citations72
Published2011
Admission routes1
Has abstractyes

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