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Record W4250061592 · doi:10.4155/cmt.11.19

Part 1: Design, modeling and simulation of post-combustion CO<sub>2</sub>capture systems using reactive solvents

2011· article· en· W4250061592 on OpenAlexaff
Zhiwu Liang, Teerawat Sanpasertparnich, Paitoon Tontiwachwuthikul, Don Gelowitz, Raphael Idem

Bibliographic record

VenueCarbon Management · 2011
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPilot plantPower stationProcess (computing)Process engineeringProcess designConceptual designEngineering design processCombustionSystems engineeringProcess modelingComputer scienceEngineeringMechanical engineeringWork in processWaste managementProcess integrationChemistry

Abstract

fetched live from OpenAlex

The simulation and modeling of post-combustion CO2 capture systems are considered to be an important strategy to obtain process integrity and gain design confidence for the construction and commissioning of commercial post-combustion CO2 capture plants. It is therefore essential to obtain an understanding of the fundamental concepts of designing and modeling. This article reviews the concepts of designing a CO2 capture system with specific emphasis on the absorber for diameter and height. It covers several steps (i.e., empirical design method, theoretical design method, laboratory method and pilot plant techniques) used to design the absorber. A conceptual design of an overall CO2 capture process is also given in the article. Process validations of the modeling using ProMax with four existing pilot plants. (the International Test Centre of CO2 Capture pilot plant, the Esbjerg CASTOR pilot plant, the Institute of Thermodynamics and Thermal Process Engineering, Stuttgart pilot plant and the SINTEF/NTNU pilot plant) are presented. Moreover, a discussion of process integration of the CO2 capture plant into a fossil fuel-fired power plant is included 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.039
GPT teacher head0.224
Teacher spread0.185 · 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 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

Citations46
Published2011
Admission routes1
Has abstractyes

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