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Record W1995016088 · doi:10.1021/ie201202b

Kinetic Models for Upgrading Athabasca Bitumen Using Unsupported NiWMo Catalysts at Low Severity Conditions

2011· article· en· W1995016088 on OpenAlexaff
C. Galarraga, Carlos E. Scott, Herbert Loría, Pedro Pereira‐Almao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCatalysisAsphaltChemistryFractionationKinetic energyResidue (chemistry)Product distributionFraction (chemistry)Activation energyBatch reactorThermodynamicsHydrogenChemical engineeringAnalytical Chemistry (journal)Materials scienceChromatographyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Typically, the catalytic upgrading of heavy fractions (VGO and VR) has been studied using mostly conventional supported catalysts at temperatures and pressures higher than 400 °C and 6 MPa, respectively. This work focuses on the upgrading of heavy oils at much lower severity conditions using dispersed NiWMo catalysts for processing whole oil with no fractionation. A kinetic study was developed to determine parameters from experimental data obtained at temperatures of 320–380 °C and reaction times from 3 to 70 h at a total hydrogen pressure of 3.45 MPa and a stirring speed of 500 rpm in a batch reactor. The conversion, estimated as the reduction of the residue 545 °C + fraction, was fitted for a first-order reaction with an apparent activation energy of 200 kJ mol –1 . Two kinetic models are proposed to predict the conversion of the residue fraction and its product distribution. Comparison between experimental data and predictions using the proposed models exhibited good agreement with average absolute errors lower than 5%.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.333
Teacher spread0.175 · 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

Citations56
Published2011
Admission routes1
Has abstractyes

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