MétaCan
Menu
Back to cohort
Record W2926802869 · doi:10.1021/acs.iecr.9b00280

A Perspective on the Impact of Process Systems Engineering on Reaction Engineering

2019· article· en· W2926802869 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemical reaction engineeringCheminformaticsComputer scienceProcess systemsProcess (computing)Work in processPerspective (graphical)Engineering design processControl (management)ChemometricsProcess controlManagement scienceWork (physics)Biochemical engineeringSystems engineeringProcess engineeringEngineeringChemistryArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Process systems engineering (PSE), as the name suggests, emphasizes an approach to understanding the behavior of systems as a whole with a view to improving decision-making for optimization and control of processes. The discipline emphasizes the application of mathematical techniques in this effort, and a plausible claim has been made that is at the very core of the discipline of chemical engineering. Being a generalized approach to process systems in general, it finds wide application to many areas in chemical engineering. This work reviews the application of PSE to the area of reaction engineering, which is also at the core of chemical engineering. We highlight the impactful applications of PSE in reaction engineering and discuss aspects of model building and analysis, reactor control, optimization, chemometrics, and chemoinformatics.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.002
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 head0.040
GPT teacher head0.324
Teacher spread0.284 · 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