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Record W4255177256 · doi:10.32920/ryerson.14654433

A computer simulator for steel plant electrical arc furnace regulator

2021· preprint· en· W4255177256 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

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAdaptive neuro fuzzy inference systemEngineeringControl engineeringSimulationFuzzy logicFuzzy control systemElectric arc furnaceControl systemControl theory (sociology)Computer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This research expands the use of ANFIS in building the full closed loop computer simulator for the three-phase regulator loop. THe ANFIS models inuts and outpus selected for this project were tried for the first time in this research. The simulator components were trained and verified by the use of plant recorded data in the open loop mode. The response of the closed loop simulator was tuned to follow the behavior of the plant EAF. Therefore the simulator works independent of the plant data or operation commands. The developed simulator, then, was used to measure the results of applying new controls in EAF such as fuzzy controllers, without disturbing the actual plant process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.218
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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