MétaCan
Menu
Back to cohort
Record W2606251771 · doi:10.1002/aic.15753

Preemptive dynamic operation of cryogenic air separation units

2017· article· en· W2606251771 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

VenueAIChE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProcess engineeringProcess (computing)Separation (statistics)Agile software developmentSupply chainProduct (mathematics)Supply and demandComputer scienceIndustrial engineeringManufacturing engineeringEngineeringBusinessMathematics

Abstract

fetched live from OpenAlex

As markets become more competitive and dynamic, manufacturing plants are undergoing transitions toward flexible, agile, and low costs operations. Appropriate coordination within the supply chain is an important factor in manufacturing systems' performance. In this study, the impact of preemptive control action in advance of an upcoming demand change on the economic performance of a cryogenic air separation unit is investigated. The effects of various factors are explored through optimization formulations utilizing a high‐fidelity collocation based dynamic process model. This includes the amount of lead time, choice of manipulated inputs, direction of demand change, and liquid product market conditions. Plant performance is evaluated and analyzed through a comprehensive multipart case study. © 2017 American Institute of Chemical Engineers AIChE J , 63: 3845–3859, 2017

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score0.258

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 communication0.0000.001
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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