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Oil Shale Pyrolysis: Conversion Dependence of Kinetic Parameters

2017· article· en· W2733114676 on OpenAlexafffund
Juliana Pedrilho Foltin, Antônio Carlos Luz Lisbôa, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersUniversidade Estadual de CampinasUniversity of Alberta
KeywordsKerogenOil shaleActivation energyPyrolysisKinetic energyIsothermal processAtmospheric temperature rangeChemistryThermogravimetric analysisKineticsThermodynamicsChemical engineeringMaterials scienceMineralogyOrganic chemistryGeologySource rock

Abstract

fetched live from OpenAlex

Oil can be recovered from kerogen in oil shale by pyrolysis. The devolatilization kinetics of the pyrolysis of oil shale from the Irati Formation in Brazil was studied. Kinetic parameters were determined from dynamic thermogravimetric analysis over the temperature range 323–1173 K, using different model-free methods. Evaluation and validation were performed by pyrolysis at 673 K for 3 hours. It was found that the activation energy depended on the extent of conversion. Activation energy increased over the range 215–255 kJ/mol for conversion in the range 0.15 ≤ α ≤ 0.55, where α = 1 for pyrolysis at 1173 K. When the reaction rate was high, the conversion calculated using kinetic parameters derived by the Friedman method was more accurate than those calculated from the Flynn–Wall–Ozawa and the Kissinger–Akahira–Sunose methods. The latter two methods performed better when the reaction rate was lower, i.e., at higher conversion. Isothermal kerogen pyrolysis approached an incomplete conversion limit that could be increased only by increasing the temperature; this type of behavior was predicted by the conversion dependence of activation energy. The observed activation energy is an average of the different activation energies of the individual compounds in kerogen. As conversion progresses, the compounds with lower activation energies are more readily converted, so that the average activation energy of the compounds that remain increases with increasing conversion. The work highlighted the importance of employing conversion-dependent kinetic parameters when modeling oil shale pyrolysis for process design, especially when the process is designed for high kerogen conversion.

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 score0.077
Threshold uncertainty score0.496

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 science0.0010.000
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 head0.013
GPT teacher head0.235
Teacher spread0.221 · 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
Published2017
Admission routes2
Has abstractyes

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