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Record W1969721680 · doi:10.1016/j.egypro.2009.02.202

CO2 capture retrofit options for a gasification-based integrated bitumen extraction and upgrading facility

2009· article· en· W1969721680 on OpenAlexaffabout
Guillermo Ordorica‐Garcia, Sam Wong, John Faltinson, Surindar Singh

Bibliographic record

VenueEnergy Procedia · 2009
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsExtraction (chemistry)AsphaltWaste managementEnvironmental scienceEngineeringMaterials scienceChemistryChromatographyComposite material

Abstract

fetched live from OpenAlex

We have modelled a gasification-based bitumen extraction and upgrading plant, based on the Long Lake project in Alberta, Canada. The process is energy self-sufficient, featuring gasification of upgrading residue (asphaltene) and subsequent H2 extraction. The H2-lean syngas is used as fuel in a co-generation unit to provide power for the complex and steam for bitumen extraction. This study involves retrofitting the proposed process to capture CO2 produced in the gasifier. Two CO2 capture retrofit cases are considered: integrating pre-combustion CO2 removal to the existing gasification process or adding a postcombustion CO2 capture plant as a tail-end process. Both options feature a CO2 capture efficiency of 90%. After CO2 retrofit, natural gas supplementation is required and the power output drops, in both cases. The CO2 reductions achieved by the retrofits are roughly 72% with respect to the original process. The net CO2 emissions of the pre- and postcombustion capture cases are essentially the same. From a performance perspective, no option offers a clear advantage; the precombustion case has lower steam and natural gas requirements and yields less compressed CO2 than the post-combustion case. However, the post-combustion retrofit is likely less disruptive to the process, it preserves a greater portion of the power production potential of the co-gen plant, and generates a purer CO2 stream than the pre-combustion option.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.660

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 head0.010
GPT teacher head0.217
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 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

Citations3
Published2009
Admission routes2
Has abstractyes

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