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Record W2167605255

A kinetic model for hydroconversion processing of vacuum residue

2011· article· en· W2167605255 on OpenAlex
Shiva Shams, William C. McCaffrey, Murray R. Gray, Amos Ben‐Zvi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Symposium on Advanced Control of Industrial Processes · 2011
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIdentifiabilityTransformation (genetics)Work (physics)Kinetic energyApplied mathematicsResidue (chemistry)Mathematical modelThermodynamicsComputer scienceMathematicsChemistryStatisticsPhysicsClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Hydroconversion is a complex process involving many chemical reactions. Mathematical models of hydroconversion processes often have more kinetic parameters than can be estimated from data. In this work the identifiability and estimability of parameters in a model describing the hydroconversion processing of vacuum residue are analyzed. The model under consideration contains five states, two outputs, and seven parameters. This lumped model was developed by grouping molecules based on their solubility characteristics. The model parameters were found to be identifiable. However, using previously published experimental data, the model parameters were found to be inestimable. It is shown that the model can be reparameterized using a linear transformation in the parameter space. This transformation allows the model outputs to be predicted based on only three pseudo-parameters. Confidence intervals for the three pseudo-parameters and the mean responses were calculated.

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.

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 score0.172
Threshold uncertainty score0.887

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 head0.033
GPT teacher head0.263
Teacher spread0.230 · 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