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Record W1991773227 · doi:10.1002/aic.11104

Dynamic optimization of electric arc furnace operation

2007· article· en· W1991773227 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

VenueAIChE Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectric arc furnaceProcess (computing)Mathematical optimizationProcess optimizationOptimization problemWork (physics)Mathematical modelChemical processComputer scienceWork in processEngineeringProcess engineeringMechanical engineeringMathematicsChemistry

Abstract

fetched live from OpenAlex

Abstract The main objective of this work is the development of a computational procedure for determining optimal operating strategies for an industrial electric arc furnace (EAF). These goals are achieved by incorporating a detailed mechanistic model into a mathematical optimization framework. The model used in this work includes mass and energy balances, and contains sufficient detail to describe the melting process, chemical changes, and material and energy flows. Mathematical optimization is used to determine the optimal input trajectories based on an economic criterion; process limitations are accounted for by including them within the optimization problem as constraints. This optimization procedure considers trade‐offs between all the process inputs and processing time, so as to maximize the profit. Several case studies illustrating the use of mathematical optimization in the enhancement of process performance are given. © 2007 American Institute of Chemical Engineers AIChE J, 2007

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.003
GPT teacher head0.212
Teacher spread0.209 · 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