MétaCan
Menu
Back to cohort

Low Voltage Modular Inverter System For Mining Applications

2025· article· W4417473098 on OpenAlexaboutno aff
Joshua Jagnanan

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsnot available
Fundersnot available
KeywordsModular designAccelerationFocus (optics)Power (physics)Low voltageBelt conveyorWork (physics)Inverter

Abstract

fetched live from OpenAlex

Underground potash mines present many challenging work areas, some of which have low heights. These conditions pose significant challenges for manufacturers of adjustable speed drives, as their cabinets tend to be taller at larger power ratings. This height issue often results in the installation of drives with long cable lengths between the drives and their motors. Over time, this can lead to damaging dV/dt voltage spikes impacting the motor windings. A potential solution to these specific low-height areas is the implementation of a modular drive system housed within a mining sled. This technical paper presents an in-depth analysis of such a system, specifically installed on a highspeed loadout conveyor belt within an underground potash mine located in Saskatchewan, Canada. The study’s primary focus is on the replacement of existing adjustable speed drives and switched reluctance motors. The new installation features a modular drive system and standard induction motors, all operating on a common drive pulley. The paper provides details of the technical specifications of the modular drive system. It includes comprehensive information on its unique design and power platform, both of which contribute to its suitability for underground mining applications. Additionally, the paper discusses technical programming aspects in detail. These include the setup of the master-follower configuration, calculations for acceleration and deceleration, motor tuning procedures, and the implementation of dynamic braking.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.209
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

Classification

machine, unvalidated

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

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicSensorless Control of Electric MotorsFrench-language works237,207