MétaCan
Menu
Back to cohort

Batch Solvent Extraction of Bitumen from Oil Sand. Part 2: Experimental Development and Modeling

2017· article· en· W2605041883 on OpenAlexafffund
M. Khammar, Yuming Xu

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsOil sandsAsphaltExtraction (chemistry)Filtration (mathematics)Volume (thermodynamics)SolventPetroleum engineeringMaterials scienceVolumetric flow rateHydraulic conductivityChromatographyChemistryEnvironmental scienceComposite materialGeologyThermodynamicsSoil science

Abstract

fetched live from OpenAlex

A new experimental technique is developed for the determination of saturated and unsaturated hydraulic properties of solvent-diluted bitumen flow through oil sands cake. This technique is based on continuous measurement of the pressure and temperature during centrifugal extraction of solvent-diluted bitumen. Time evolutions of the extracted diluted bitumen volume and flow rate are obtained from pressure and temperature measurements. A criterion for the determination of the transition time from filtration to desaturation is applied. Experimental filtration data are fitted with an analytical model to obtain saturated hydraulic conductivity of the oil sand cake, and experimental desaturation data are fitted to obtain unsaturated hydraulic properties. The model is used to predict the evolution of bitumen extraction efficiency at different centrifugal forces. The feasibility of continuous commercial extraction of bitumen from oil sands by centrifugal filtration is discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.260
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207