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Record W2521285521 · doi:10.1002/aic.15512

Water content of light n‐alkanes: New measurements and cubic‐plus‐association equation of state modeling

2016· article· en· W2521285521 on OpenAlexafffund
Mohsen Zirrahi, Hassan Hassanzadeh, Jalal Abedi

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEquation of stateThermodynamicsState (computer science)Association (psychology)ChemistryMaterials scienceMathematicsPhysicsPsychology

Abstract

fetched live from OpenAlex

Light hydrocarbon gases such as methane, ethane, propane, and butane or other so called gaseous solvents have been suggested as steam additives to improve bitumen recovery and energy efficiency. The water content of these gases is one of the key requirements in the simulation and design of solvent‐aided thermal heavy oil recovery processes. In this work, we present new experimental data for the water content of these gases at high temperatures (up to 493.15 K) and moderate pressures (P < 6 MPa). The experimental data was regenerated using the cubic‐plus‐association equation of state. The Soave–Redlich–Kwong equation of state is used to treat the physical interactions. The association interactions are captured using Wertheim's first‐order thermodynamic perturbation theory. A set of binary interaction parameters is proposed to calculate the water content of methane, ethane, propane, and n‐butane at the operating conditions of the thermal heavy oil and bitumen recovery processes. © 2016 American Institute of Chemical Engineers AIChE J , 63: 1384–1389, 2017

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 score0.349
Threshold uncertainty score0.181

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 head0.060
GPT teacher head0.223
Teacher spread0.163 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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