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Record W2772892783 · doi:10.1002/cjce.23104

Kinetic analysis of cyclic carbonation of carbide slag during chemical reaction‐controlled stage under fluidization conditions

2017· article· en· W2772892783 on OpenAlexvenueno aff
Rongyue Sun, Jiangming Ye, Linghai Chen, Siwen Zhang, Xiaolong Bi, Xueying Tan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonationMaterials scienceCarbideSlag (welding)CalcinationSorbentMetallurgyFluidizationSinteringFluidized bedChemical engineeringAdsorptionComposite materialChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Carbide slag, as a kind of typical industrial waste, was proposed as a CO 2 sorbent in the calcium looping process. The CO 2 capture performance of carbide slag was investigated in a bubbling fluidized bed reactor (BFBR) under fluidization conditions. A surface reaction‐controlled kinetic model was employed to describe the carbonation kinetics of carbide slag during the chemical reaction‐controlled stage. The results show that the values of k , t crcs , and X u , which respectively denote the reaction rate constant, duration time, and final carbonation conversion in the chemical reaction‐controlled stage, decrease with the cycle number, due to the sintering of the sorbents. The microstructure of calcined carbide slag leads to higher CO 2 diffusion resistance in carbide slag than that in limestone, causing lower k and longer t crcs compared with limestone. The larger BET areas and pore areas of calcined carbide slag provide additional surface for the reaction of CaO and CO 2 . Therefore, the X u values of carbide slag are higher than those of limestone after 5 cycles. Reaction conditions have a significant effect on the carbonation process of carbide slag. 850–900 °C is the optimum temperature range for the calcination of carbide slag. The gas‐solid transfer is strengthened at a higher fluidization number, enhancing the CO 2 capture of carbide slag. The pores of the larger particles are more easily blocked due to the formation of CaCO 3 layer, and the X u of the carbide slag with a larger particle size is lower.

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)0.001
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 score0.031
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

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