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Record W2322089302 · doi:10.1021/ie400297s

Thermodynamic and Kinetic Study of CO<sub>2</sub> Capture with Calcium Based Sorbents: Experiments and Modeling

2013· article· en· W2322089302 on OpenAlexaff
Ehsan Mostafavi, Mohammad Hashem Sedghkerdar, Nader Mahinpey

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCalcium loopingCalcium oxideThermogravimetric analysisCarbonationThermodynamicsKinetic energyDolomiteCalcinationChemistryMaterials scienceMineralogyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The steam gasification of biomass in the presence of calcium oxide offers a viable route for the dual purpose of hydrogen production and carbon dioxide (CO 2 ) capture. Although previous studies have dealt with experimental and intrinsic rate constants of carbonation and calcination of calcium looping cycles, the data has not been compared with thermodynamic or kinetic simulation. In this study, the thermodynamic and kinetic simulation of the CO 2 capture process using two calcium-based sorbents (i.e., Imasco dolomite and Cadomin limestone) have been studied using Aspen Plus software. The thermodynamic simulation was able to predict the overall trend of the CO 2 adsorption on dolomite and limestone. However, a kinetic model was also applied to achieve a more accurate analysis. The results show good agreement between the modeling and the experimental data obtained using a thermogravimetric analyzer (TGA). A shift in the reaction mechanism was observed with respect to temperature. The experimental data and kinetic model illustrated that the maximum conversion occurred at 650 °C.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.059
GPT teacher head0.294
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 source (direct Gemma or distilled Codex), 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

Citations34
Published2013
Admission routes1
Has abstractyes

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