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Record W2914780771 · doi:10.11575/prism/36104

Reactor Design for Partial Upgrading of Bitumen via Aquaprocessing

2018· dissertation· en· W2914780771 on OpenAlexaboutno aff
Sukhdeep Singh Gill

Bibliographic record

VenueOpen MIND · 2018
Typedissertation
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltWaste managementEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Alberta oil industry is facing a major challenge because of the widening price differentials between West Texas Intermediate and Western Canadian Select. The Western Canadian Select is trading at low of US$ 16.71 /bbl. because of its high viscosity, density, total acid number, and sulfur content and lack of access to market outside North America. Under these circumstances, partial upgrading processes such as Aquaprocessing are envisioned as a potential solution. The synthetic crude oil produced from Aquaprocessing of bitumen requires 50 vol.% less diluents than the Athabasca bitumen to meet the pipeline specifications. This thesis is focused on evaluating the reactor design parameters required for scale-up of this technology. The kinetic parameters for hydrocracking reaction were calculated using first-order rate law expression for the packed bed reactor. Diffusivity of steam in bitumen, dimensionless numbers and hydrodynamic properties of the fluid inside the reactor were calculated using experimental and simulated data. The experimental results indicate that the effect of catalyst particle size depends on the catalyst preparation techniques. The steam diffusivity in bitumen was found to increase linearly with increase in reaction temperature. 0.05 psi/m pressure drop and 39 vol.% liquid holdups are predicted for the two-phase flow regime.

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), Insufficient payload (model declined to judge)
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 score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.338
Teacher spread0.287 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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