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Record W2591784671 · doi:10.1002/cjce.22827

Study on regenerative process of the new carbon capture technique based on antisolvent crystallization to strengthen crystallization

2017· article· en· W2591784671 on OpenAlexvenueno aff
Yu Zhang, Jianmin Gao, Dongdong Feng, Qian Du, Shaohua Wu, Yijun Zhao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
FundersNational Science and Technology Development AgencyNational Natural Science Foundation of China
KeywordsCrystallizationCarbonizationAmmoniaDesorptionChemical engineeringCarbon fibersMaterials scienceCrystal (programming language)Activation energyProcess (computing)Mother liquorProcess engineeringChemistryAdsorptionOrganic chemistryComposite materialComputer scienceScanning electron microscopeComposite number

Abstract

fetched live from OpenAlex

A new technique for reinforced ammonia with a low carbonized ratio to be crystallized by means of antisolvent crystallization using ethanol as the antisolvent was put forward in this paper in view of existing problems of carbon capture by ammonia. Through the application of antisolvent crystallization to reinforce the crystallization process, low energy consumption of regeneration could be achieved easily due to saving the energy to heat the water of carbonized ammonia in the desorption process. Additionally, if the rate of rising temperature is 5 K/min, the crystal product will be totally decomposed, which has advantages over the process of rich carbonated ammonia. The percent conversion of crystal product can be 100 % when the temperature is 350 K, which can be achieved by the waste heat from the power plant that can be used as a heat source of the regeneration process. When the regenerated temperature is between 313 K and 353 K, the active energy can be 44 kJ/mol. When the rate of rising temperature is 5 K/min, 10 K/min, 15 K/min, and 25 K/min respectively, the rate of decomposition and active energy at different experimental conditions are studied in this paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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