MétaCan
Menu
Back to cohort
Record W2044779468 · doi:10.1002/ceat.200800309

Reactor Technologies for Propane Partial Oxidation to Acrylic Acid

2009· article· en· W2044779468 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

VenueChemical Engineering & Technology · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPropaneAcrylic acidFluidized bedSelectivityRaw materialChemical engineeringPartial oxidationHeat transferMaterials scienceCatalysisWaste managementProcess engineeringChemistryOrganic chemistryThermodynamicsPolymerEngineeringCopolymer

Abstract

fetched live from OpenAlex

Abstract The two step process to produce acrylic acid from propylene is the predominant technology practiced commercially. The economics of this process are compared with the direct oxidation of propane to acrylic acid in a fixed bed, turbulent fluidized bed (TFB) and circulating fluidized bed (CFB). Economies of scale are difficult to realize in fixed beds due to the limited heat transfer surface, and therefore, several reactors are required in parallel. A single reactor train is possible due to the excellent heat transfer characteristics of the TFB and CFB. The economics of the TFB are superior to either the CFB or multi‐tubular fixed beds. However, both investment and operating costs are sensitive to selectivity. A CFB could become the reactor of choice if lattice oxygen can be shown to improve selectivity and conversion. In order for propane to replace propylene as the preferred feedstock, selectivity must be at least 65 %, which has yet to be demonstrated in practice.

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)0.001
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 score0.351
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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