MétaCan
Menu
Back to cohort
Record W1999275465 · doi:10.1021/ef901470j

Comparative Kinetics and Thermal Behavior: The Study of Crude Oils Derived from Fosterton and Neilburg Fields of Saskatchewan

2010· article· en· W1999275465 on OpenAlexafffundabout
Nader Mahinpey, Pulikesi Murugan, Thilakavathi Mani

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersPetroleum Technology Research Centre
KeywordsAsphalteneThermogravimetryActivation energyArrhenius equationChemistryPyrolysisCombustionPetroleum cokeKineticsOrder of reactionKinetic energyAnalytical Chemistry (journal)CokeOrganic chemistryInorganic chemistryReaction rate constant

Abstract

fetched live from OpenAlex

Pyrolysis and combustion characteristics of two different crude oil samples obtained from Fosterton (medium oil) and Neilburg (heavy oil) fields in Saskatchewan were studied and compared using the results of thermogravimetry (TG) and differential thermogravimetry (DTG) analyses. In addition, the properties of whole oil and asphaltene were determined by means of proximate and ultimate analyses. The analyses indicate that asphaltene from both Fosterton and Neilburg fields has a lower volatile matter and ash content as well as higher fixed carbon values when compared to the values of the respective whole oil. From the elemental analysis, it was determined that the H/C ratio is approximately the same for both reservoirs whole oil and asphaltenes. The reaction region, peak, and burnout temperatures of the samples were also determined. The Arrhenius equation provides kinetic data: activation energy, pre-exponential factor, and order of the reaction. The kinetic analysis showed similar activation energy for the combustion of coke produced from Neilburg and Fosterton oils, as 129.5 and 127 kJ/mol, respectively. The activation energy for Neilburg and Fosterton asphaltenes were 117.7 and 93.46 kJ/mol, respectively.

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 score0.013
Threshold uncertainty score0.345

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 head0.015
GPT teacher head0.252
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 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

Citations28
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207