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Record W2062232284 · doi:10.1080/00908310600625285

Enhancing CO<sub>2</sub>Conversion to Methanol Using Dynamic Optimization, Applied on Shell Temperature and Inlet Hydrogen During Four Years Operation of Methanol Plant

2007· article· en· W2062232284 on OpenAlex

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

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsMethanolMole fractionExothermic reactionInletHydrogenHydrogen productionFraction (chemistry)Materials scienceChemistryThermodynamicsProcess engineeringMechanical engineeringEngineeringOrganic chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract The investigation of dynamic optimal policies for an industrial methanol reactor experiencing exothermic, reversible reactions is the subject of this study. Optimal values of inlet hydrogen mole fraction and shell temperature have been investigated for a heterogeneous methanol reactor. Optimization has been carried out by employing the methanol production rate (MPR) as an objective function. Optimal history profiles for shell temperature (Tshell) and hydrogen inlet mole fraction has been obtained during 4 years of operation. It was found that applying obtained optimal profiles of H2 and Tshell provides a 1.4% production benefit compared to an existing operating plant policy. This is equivalent to 1,400,000 USD during a four year operation period.

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 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 score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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