Improve Fe Accuracy by 32.7%
Major Iron Ore Mine in Australia
A case study in overcoming low accuracy levels in the material grade feed to the plant using IntelliSense.io Stockpile & Inventory Optimization.
Low Accuracy in Material Grade Feed to Plant
The Weighted Average Model (WAM) of stockpiles grades was showing low accuracy levels with the material fed to the plant severely impacting downstream processes.
How We Helped
Improved Visibility with 3D Stockpile Models
IntelliSense.io 3D Stockpile model data and the crusher feed was compared to the provided head-grade assay to determine accuracy deviation.
Accuracy Improved by 32.7%
Fe, Al2O3, SiO2, and P assay results are closely matched by the IntelliSense.io Stockpile & Inventory data.
On the comparison of the weighted average crusher feed to the head grade results, the IntelliSense.io Stockpile 3D block model outperformed the weighted average model in all instances, with Fe accuracy improved by 32.7%.
Delivering optimization of short, medium, and long-term mining planning with better blending.
Discover how Stockpile & Inventory Optimization can be applied to your operation
Mine to Market: Value Chain Optimization
The Stockpile & Inventory Optimization Application is one of a suite of real-time decision-making applications that use Scientific AI to optimize each process; from mine to market.
Our Material Handling model connects these applications to drive even greater efficiency.
Our process optimization apps can be deployed on a specific process bottleneck or expanded across the entire value chain.
They are powered by our Scientific AI Decision Intelligence Platform, brains.app.