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Advanced Electric Mining Dredge Designs

2022· article· en· W4385249650 on OpenAlexaff
Kent Zehr, Ken McConachie, Damon Gonzales, Eliot Castanza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsThe King's UniversityBP (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Electric mining dredges produced recently have benefitted from significant advances in the applied power systems protection and control and in the delivered safety of those systems on board. These advances also contributed to the simplification of the essential mechanical systems. Older designs are compared to newer designs and the various advantages are discussed. Normal and emergency states of the power systems on board are described and how the newer concepts have affected the transition between states is explained. The developed design concepts that have allowed these material advances are discussed so that their more general application may be understood. Some of the design drivers are of more general application than mining dredges, for example the necessity of being space conscious both from the standpoint of minimizing the structural requirements but also to allow highway transportation of significant assemblies. Another design feature influenced partly by the need for a compact design was to simplify the topology of the electrical design. By taking advantage of improved control features, the mechanical systems also benefitted, leading to higher reliability and a compounding effect on the physical size of the resulting assemblies. As a result of the continuous design development the safety of operators and maintenance personnel has been enhanced by providing better protection and control and the overall cost, reliability, and performance of the dredges have been significantly improved. In short, the innovative end product, ostensibly an ordinary medium-and low-voltage distribution system powering heavy mechanical equipment, is a fine example of what value engineering, unabridged commitment to “better”, practicality, and occasionally clever ideas can bring to otherwise low-profile aspects of plant or mining machinery design.

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 score0.906
Threshold uncertainty score0.815

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)0.0010.000

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 head0.181
Teacher spread0.172 · 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
Published2022
Admission routes1
Has abstractyes

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