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Record W4403431059 · doi:10.1080/00084433.2024.2415235

Influence of HPGR operating parameters on the edge and centre product – an industrial trial

2024· article· en· W4403431059 on OpenAlexaff
Mohsen Izadi-Yazdan Abadi, Saeid Zare, Javad Pourshahabadi, Mohammad Reza Garmsiri

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2024
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsEnhanced Data Rates for GSM EvolutionProduct (mathematics)Materials scienceMetallurgyNuclear engineeringProcess engineeringEnvironmental scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

High pressure grinding rolls has been widely used in mineral industries as energy-efficient equipment. During the working life of an HPGR mill, the rolls’ surface wears out, and a concave wear profile (maximum wear in the middle) occurs on the rolls. As a result, a difference between HPGR discharge particle size from the middle and edges is also expected that has yet to be quantified. On the other hand, many operational variables affect the HPGR performance; however, there is an interaction between the operational variables and worn rolls. This work studied the effect of operational variables such as hydraulic pressure, rolls’ rotation speed, level of material in the hopper, and operational gap on the HPGR performance on an industrial scale. In addition, the particle size of HPGR discharge from the middle and edges of the rolls is also investigated. Results indicated that HPGR discharge from the middle of the rolls is always finer than discharge from the edges, while the difference between the discharge size from the middle and edges depended on the other variables. Furthermore, it was concluded that deeper material levels in the hopper and lower rotation speed of the rolls resulted in a finer HPGR discharge. This finding is crucial in optimising the HPGR performance and modifying the design of HPGRs to regulate the distribution of material on the rolls.

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: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.418

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Metallurgical QuarterlySame topicMineral Processing and GrindingFrench-language works237,207