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Record W2980792818 · doi:10.1002/aic.16828

Ethyl acetate as a bio‐based solvent to reduce energy intensity and CO<sub>2</sub> emissions of in situ bitumen recovery

2019· article· en· W2980792818 on OpenAlexafffund
Ali Zirahi, Hassan Sadeghi Yamchi, Ali Haddadnia, Mohsen Zirrahi, Hassan Hassanzadeh, Jalal Abedi

Bibliographic record

VenueAIChE Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltSteam-assisted gravity drainageSolventEthyl acetateChemistryWaste managementIn situPulp and paper industryMaterials scienceEnvironmental scienceOil sandsAnalytical Chemistry (journal)Environmental chemistryChromatographyOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract We introduce ethyl acetate (EA), a bio‐based chemical, as a potential solvent for bitumen recovery through comprehensive phase behavior and numerical simulation studies. Phase behavior and thermophysical properties of EA/live bitumen are measured at temperatures and pressures up to 190°C and 4 MPa, respectively. Experimental studies suggested that coinjection of EA with steam can reduce the bitumen viscosity by several orders of magnitude. Our numerical simulations show that coinjection of 2–8 mol% EA with steam can significantly reduce the steam‐oil‐ratio (SOR) by almost 0.9 units while increasing the bitumen production rate. This reduction in SOR can be translated to significant energy saving of ~2.2 GJ, emission reduction of ~120 kg of CO 2 , and wastewater reduction of ~120 m 3 per ton of the produced bitumen, which are almost 20–25% lower than the steam‐assisted gravity drainage (SAGD) 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 categoriesnone
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 score0.012
Threshold uncertainty score0.674

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations26
Published2019
Admission routes2
Has abstractyes

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