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Record W2515591039 · doi:10.1021/acs.iecr.5b00158

Experimental Studies of Reboiler Heat Duty for CO<sub>2</sub> Desorption from Triethylenetetramine (TETA) and Triethylenetetramine (TETA) + <i>N</i>-Methyldiethanolamine (MDEA)

2015· article· en· W2515591039 on OpenAlexaff
Xiao Luo, Kaiyun Fu, Zhen Yang, Hongxia Gao, Wichitpan Rongwong, Zhiwu Liang, Paitoon Tontiwachwuthikul

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsTriethylenetetramineDiethylenetriamineReboilerChemistryDesorptionAmine gas treatingNuclear chemistryMaterials scienceInorganic chemistryChromatographyOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Amine scrubbing is regarded as one of the most suitable technologies for postcombustion CO 2 capture. However, data on regeneration energy and performance is limited in literature. In this study, the reboiler heat duties of triethylenetetramine (TETA) and triethylenetetramine (TETA) + N -methyldiethanolamine (MDEA) were experimentally evaluated in a bench-scale stripper packed with Dixon ring random packing. The effects of various operating parameters on the desorption performance (presented in terms of reboiler heat duty, Q reboiler ) were investigated, including lean loading, rich loading, amine concentration, solvent flow rate, and amine type. The experimental results showed that the Q reboiler was very sensitive to these process parameters. In addition, a comparison of the Q reboiler of TETA, monoethanolamine (MEA), and diethylenetriamine (DETA) was conducted to evaluate the potential for TETA’s application in the CO 2 capture process. The results obtained in this work showed that the Q reboiler of TETA, as a function of amount of CO 2 released, is lower than that of MEA and DETA.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.152
GPT teacher head0.344
Teacher spread0.191 · 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.

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

Citations32
Published2015
Admission routes1
Has abstractyes

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