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Record W2407058370 · doi:10.1002/cjce.22544

Integrated short‐term scheduling and production planning in an ethylene plant based on Lagrangian decomposition

2016· article· en· W2407058370 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of China
KeywordsPetrochemicalScheduling (production processes)CrackingDecompositionEthyleneMathematical optimizationProduction planningUpstream (networking)Production (economics)Computer scienceScale (ratio)EngineeringProcess engineeringWaste managementMathematicsMaterials scienceChemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract A cracking furnace, which is the major unit involved in ethylene production, produces various petrochemical products ranging from ethylene to pitch and determines the production yield of an ethylene plant. It is essential to consider the operational performances of the cracking furnaces and to incorporate them into the whole plant's planning problem. Facing this challenge, this paper developed an optimization model, which integrates the scheduling problem of upstream cracking furnaces and the operational planning of the downstream units under a synchronized global time scale. Moreover, a modified Lagrangian decomposition algorithm is proposed for solving the large‐scale mixed integer nonlinear optimization problem. An industrial case study demonstrates the feasibility of the integrated model and the effectiveness of the solution algorithm.

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: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.262

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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