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
Overview
Syllabus
Grade Control and Ore movement from blasting: Integration of blast movement modelling into a Datamine Ore Control Process
Predicting Ore Movement During Blasting: In-situ Copper, Mine plan, Drill and Blast, Dig and Dispatch, Crush and Mill, Process to product
Blasting: commodity, geology, the scale of mining, blasting practices
Conventional Blast Movement Modelling Methods
Blast Movement Modelling - the key challenge
Case study Blast from Kansanshi
Copper Grades Ore vs Waste
Pre and Post-Blast Survey
Swell and Thoughts
Blast Pattern Data - Hole Depth, Stemmed Heights, Kg of explosives, Timing Sequence
Datamine and OMP Software Integration: Augment software, machine learning, In-situ
Pre or Post-Blast Models
Blast Validation using Datamine scripts and macros
Power BI Validation Dashboard
FQLM Grade Control System
Step1: Select the Mark-up Stage
Step 2: Define the Pre and Post Blast Extents
Step 4: Move the Model
Step 4: Ore / Waste Mark-up
Step 5: Plotting and Data export
Conclusions: Machine learning ML based solutions
Conclusions: Blasting
Conclusions: Datamine Software Datamine macro language
Conclusions: GC System
Taught by
Datamine Software
