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Record W4402817180 · doi:10.3390/en17184626

Energy and Economic Assessment of Oxy-Fuel Combustion CO2 Capture in Coal-Fired Power Plants

2024· article· en· W4402817180 on OpenAlexaff
Yuyang Yuan, Lei Wang, Yaming Zhuang, Ying Wu, Xiaotao Bi

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of British Columbia
FundersGovernment of Jiangsu ProvinceJiangsu Agricultural Science and Technology Innovation Fund
KeywordsAir preheaterFlue gasWaste managementFlue-gas emissions from fossil-fuel combustionCombustionBoiler (water heating)CoalEnvironmental sciencePower stationWaste heat recovery unitElectricity generationWaste heatIncinerationEconomizerEngineeringChemistryHeat exchangerPower (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

Oxy-fuel combustion technology replaces air with a mixture of pure O2 and recycled flue gas for coal combustion, which leads to difficulties in the waste heat recovery of flue gas in the boiler tail of coal-fired power plants. This paper proposes a new integration scheme for waste heat recovery of flue gas in coal-fired power plants with oxy-fuel combustion CO2 capture. By introducing an oxygen preheater, a recycled flue gas preheater, and a low-pressure economizer, the waste heat of flue gas is fully recovered to preheat oxygen, recycled flue gas, and feed water, respectively. The proposed scheme simultaneously ensures the safe operation of the recycled fan and improves the thermal performance of the coal-fired power plants. Compared to the air combustion configuration, the boiler’s efficiency and gross power efficiency in the oxy-fuel combustion configuration are increased by 0.42% and 1.29%, respectively. Due to power consumption for the added equipment, the net power efficiency is reduced by 10.41%. A techno-economic analysis shows that the cost of electricity for oxy-fuel combustion in coal-fired power plants has increased from USD 46.45/MWh to USD 80.18/MWh, and the cost of the CO2 avoided reaches USD 43.24/t CO2.

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 score0.291
Threshold uncertainty score0.571

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 head0.006
GPT teacher head0.212
Teacher spread0.207 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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