MétaCan
Menu
Back to cohort
Record W1978385362 · doi:10.1021/ef301278c

Practical and Economic Aspects of the Ex-Situ Process: Implications for CO<sub>2</sub> Sequestration

2012· article· en· W1978385362 on OpenAlexafffund
Sohrab Zendehboudi, Alireza Bahadori, Ali Lohi, Ali Elkamel, Ioannis Chatzis

Bibliographic record

VenueEnergy & Fuels · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsDissolutionBrineCarbon sequestrationAquiferCarbon capture and storage (timeline)Petroleum engineeringEnvironmental scienceGroundwaterGeologyCarbon dioxideEngineeringChemistryGeotechnical engineeringChemical engineeringClimate change

Abstract

fetched live from OpenAlex

The risk of CO 2 leakage and the very slow rate of CO 2 dissolution in brine present major technical challenges for secure implementation of CO 2 sequestration at large scale in saline aquifers. To tackle these issues, a new technology based on Ex-Situ Dissolution Approach (ESDA) was developed recently aiming at dissolving CO 2 in brine phase prior to injection into the aquifer to eliminate or minimize the risk of leakage and accelerate CO 2 dissolution rate in brine. The ESDA is based on the mass transfer from CO 2 droplets into brine in cocurrent pipeline flow. This paper presents mass transfer modeling associated with the ESDA process concerning the evolution of the droplet size and the pressure change along the pipeline. In addition, a technical and economic feasibility of the ESDA in comparison with the standard carbon capture and storage (CCS) technologies is presented. Various aspects such as CO 2 displacement, geochemical reactions, CO 2 leakage, pressure build-up, well spacing, and dissolution efficiency for the ESDA are also discussed. This study enables the evaluation of the ESDA process for CO 2 sequestration through a systematic way.

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 score0.285
Threshold uncertainty score0.217

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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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

Citations43
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207