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Record W4297348676 · doi:10.56952/arma-2022-0756

A Case Study on Trim Blast Fragmentation Optimization Using MBF and MSW Models at an Open Pit Mine in Canada

2022· article· en· W4297348676 on OpenAlexaboutno aff
Ruilin Yang, L. H. Pratt, Qian Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsRock blastingTrimVibrationOpen-pit miningFragmentation (computing)Structural engineeringEnvironmental scienceEngineeringMining engineeringComputer scienceAcoustics

Abstract

fetched live from OpenAlex

ABSTRACT: A project for monitoring, analysis, and modelling trim blasting at an open pit copper/molybdenum mine in Canada in 2021 was conducted using Orica’s in house developed proprietary tools, which includes the accelerometer near-field vibration monitoring system, the Multiple Seed Waveform (MSW) near- and far-field blast vibration model, and the Multiple Blasthole Fragmentation (MBF) model. Both MSW and MBF models are built on the fundamentals and empirical aspects of blasting mechanics. The blast vibration at the highwalls from trim blasts was predicted with MSW model. The effects of parameters from the blast design scenarios at the mine site were simulated and optimized designs were proposed based on the MBF and MSW modelling. The MBF model explicitly simulates all blast design parameters. The MSW blast vibration model uses multiple seed waveforms at different distances from a blasthole to a point of interest and is suitable for near-field blast vibration at the highwalls. Through the application of the MBF in conjunction with the MSW, various design scenarios can be explored, and rock fragmentation can be optimized while controlling blast vibration levels. These models have been successfully applied to many open pit mines and quarries around the world. 1. INTRODUCTION For a large open pit mine, it is of vital importance to optimize trim blasts. A trim blast is next to the presplit. To minimize the negative impact on the highwall, trim blasts are often loaded with a low powder factor that results in much coarser fragmentation than the production blasts. The coarser fragmentation increases the cost of the downstream operations. On the other hand, trim blasts with a high powder factor could impact the highwall causing the highwall instability. Over the recent years, Orica developed a unique system for blast optimization projects. The system includes near-field blast vibration monitoring using accelerometers, the multiple blasthole fragmentation (MBF) model, and the multiple seed waveform (MSW) blast vibration model. The three components of the system are used together to improve blast performance at a site.

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 score0.034
Threshold uncertainty score0.345

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 head0.063
GPT teacher head0.273
Teacher spread0.210 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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