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Grade Control and Ore movement from blasting: Integration of blast movement modelling into a Datamine Ore Control Process
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Predicting Ore Movement During Blasting: In-situ Copper, Mine plan, Drill and Blast, Dig and Dispatch, Crush and Mill, Process to product
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Blasting: commodity, geology, the scale of mining, blasting practices
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Conventional Blast Movement Modelling Methods
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Blast Movement Modelling - the key challenge
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Case study Blast from Kansanshi
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Copper Grades Ore vs Waste
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Pre and Post-Blast Survey
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Swell and Thoughts
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Blast Pattern Data - Hole Depth, Stemmed Heights, Kg of explosives, Timing Sequence
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Datamine and OMP Software Integration: Augment software, machine learning, In-situ
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Pre or Post-Blast Models
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Blast Validation using Datamine scripts and macros
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Power BI Validation Dashboard
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FQLM Grade Control System
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Step1: Select the Mark-up Stage
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Step 2: Define the Pre and Post Blast Extents
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Step 4: Move the Model
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Step 4: Ore / Waste Mark-up
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Step 5: Plotting and Data export
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Conclusions: Machine learning ML based solutions
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Conclusions: Blasting
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Conclusions: Datamine Software Datamine macro language
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Conclusions: GC System
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the integration of blast movement modeling into Datamine's grade control system in this 38-minute conference talk by Richard Sulway, Resource Systems Geologist at First Quantum Minerals. Delve into predicting ore movement during blasting, conventional modeling methods, and key challenges using a case study from Kansanshi. Learn about Datamine and OMP software integration, blast validation techniques, and the FQLM Grade Control System. Gain insights on machine learning-based solutions, blasting considerations, and the application of Datamine macro language in grade control systems. Understand the importance of accurate blast movement modeling in optimizing mineral extraction and processing in open-pit mining operations.

Integration of Blast Movement Modelling into Datamine Grade Control System

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