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Record W2618589120 · doi:10.1002/cjce.22901

Absorption and desorption of carbon dioxide in several water types

2017· article· en· W2618589120 on OpenAlexvenueno aff
Mahmoud Al‐Hindi, Fouad Azizi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsnot available
FundersConseil National de la Recherche Scientifique
KeywordsAlkalinityBrackish waterDesorptionSalinityCarbon dioxideSeawaterChemistryMass transferAbsorption (acoustics)Mass transfer coefficientAbsorption of waterAnalytical Chemistry (journal)Environmental chemistryAdsorptionChromatographyMaterials scienceGeology

Abstract

fetched live from OpenAlex

This study investigated the effect of water type on the rate of CO 2 transfer from/to an aqueous phase with varying degree of water salinity. The absorption and desorption experiments were conducted on reverse osmosis product, brackish well, and brackish water reverse osmosis reject waters as well as seawater in a mechanically agitated tank. Results show that the direction of mass transfer has a major impact on the value of the volumetric mass transfer coefficient, k L a , with the absorption experiments always rendering higher values. Furthermore, k L a values always decreased with salinity in both absorption and desorption experiments until a certain critical salinity value was reached, beyond which mass transfer increased again. However, k L a values were found to decrease continuously with an increase in the water alkalinity in absorption experiments, while no clear conclusion could be drawn for the alkalinity effect in the case of desorption experiments. These observations suggest that the effect of alkalinity should be further investigated to elucidate its impact along with the salinity on the volumetric mass transfer rate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.179
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2017
Admission routes1
Has abstractyes

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