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Record W2066419939 · doi:10.1016/j.egypro.2011.01.150

CO2 processing and multi-pollutant control for oxy-fuel combustion systems using an advanced CO2 capture and compression unit (CO2CCU)

2011· article· en· W2066419939 on OpenAlexaff
Kourosh Zanganeh, Ahmed Shafeen, Carlos Henrique Salvador, Ashkan Beigzadeh, Maria Abbassi

Bibliographic record

VenueEnergy Procedia · 2011
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsFlue gasGreenhouse gasFlue-gas emissions from fossil-fuel combustionCombustionFossil fuelWaste managementCarbon capture and storage (timeline)Environmental sciencePollutantProcess engineeringEnvironmental engineeringEngineeringChemistryClimate change

Abstract

fetched live from OpenAlex

Oxy-fuel combustion of fossil fuels produces a CO2-rich gas stream with some impurities, such as nitrogen, argon, oxygen, nitrogen oxides, heavy metals and sulphur oxides, whose concentrations vary based on the type of fuel, combustion conditions, plant configuration, and other process related parameters. To control the greenhouse gas (GHG) emissions from these plants and sustain their operational competitiveness in a carbon constrained world, the CO2 in the flue gas stream has to be captured, cleaned up and compressed to make it suitable for pipeline transport and permanent storage in geological formations. The flue gas CO2 capture and processing, including integrated multi-pollutant control, provides a feasible and viable technological pathway towards this goal. For oxy-fuel combustion systems, the CO2 capture is best achieved by physical gas separation through a series of compression and cooling stages to liquefy and separate CO2 from non-condensable gases in the flue gas stream. The effectiveness of the CO2 capture and compression system depends on the process design and assessed using removal efficiency, level of purification and energy demand for a unit of captured CO2. If the CO2 capture process can also simultaneously remove other pollutants in the gas stream, then the whole process becomes more efficient and cost-effective. In this case, implementing an elaborate flue gas pre-treatment system will not be necessary. Hence, CO2 capture technologies that are capable of simultaneously controlling emissions of multiple pollutants offer the potential to achieve emissions reduction at lower cost and reduced footprint, when compared to conventional emission control systems. For the new oxy-coal fired power plants, multi-pollutant control technologies can help designers of these plants select effective and less expensive compliance strategies, compared with compliance choices made when the requirements are addressed individually. CanmetENERGY has developed and successfully implemented an advanced and proprietary CO2 capture and compression unit (CO2CCU) that is capable of capturing and generating a relatively pure CO2 product stream, while simultaneously removing the pollutants such as nitrogen oxides, sulphur oxides and mercury in the process condensate streams. The pilot-scale unit is currently integrated with the existing 0.3 MWth oxy-fuel Vertical Combustor Research Facility. This advanced CO2 capture system represents an integrated approach to oxy-fuel combustion with multi-pollutant and CO2 capture and provides a unique test platform for CO2 processing. In this paper, we present and discuss the recent pilot-scale test results of CanmetENERGY’s CO2CCU. The unit was used to conduct experiments with a broad range of flue gas compositions, including different levels of CO2 concentrations and a host of impurities in the flue gas stream. The real-time measurements of CO2 product and vent stream gas compositions as well as the analysis of condensate streams provide insight into the type of chemical reactions that are taking place under different pressure, temperature, and moisture levels. The experimental results help to explain some of the chemical reactions involved in the CO2 capture and compression for better optimizing the capture process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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 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

Citations14
Published2011
Admission routes1
Has abstractyes

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