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Record W2171055480 · doi:10.1002/cjce.21855

Robust level control of a dry‐surge ore pile

2013· article· en· W2171055480 on OpenAlexafffundvenue
Kenneth Scott McClure, Ryozo Nagamune, Devin Marshman, T. Chmelyk, R. Bhushan Gopaluni

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsBurnaby HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsController (irrigation)Control theory (sociology)PileFlow (mathematics)Robust controlStability (learning theory)SurgeDisturbance (geology)EngineeringEnvironmental scienceComputer scienceControl (management)GeologyControl systemMathematicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract This paper presents a new method for controlling the height of a mineral processing dry‐surge ore pile between specified upper and lower levels by manipulating the outlet ore flow. The upper level should not be exceeded for safety, while the height should always be higher than the lowest allowable level to maintain a reserve for continuous operation downstream of the ore pile. However, this is a non‐trivial control problem because the disturbance of the unpredictable discontinuous ore input must be attenuated in the continuous outlet ore flow. In addition, ore is mined from multiple sites and causes the physical properties of the ore to be uncertain. To deal with these issues, a control problem is formulated to attenuate the disturbance from the discontinuous feed and guarantee stability given the inaccurate process model. To solve the formulated control problem, a robust height controller is developed using the linear matrix inequality technique. It is shown that the robust controller attenuates four times more variability in outlet ore flow than a gain‐scheduled PI controller and guarantees robust closed loop stability.

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.

How this classification was reachedexpand

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 score0.635
Threshold uncertainty score0.424

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 head0.158
Teacher spread0.147 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes3
Has abstractyes

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