AI in Mineral Processing: Optimizing Ore Recovery Rates by 25%
The Efficiency Gap in Processing Plants
In the processing plant, even a 1% variation in recovery rates can mean millions in lost revenue. Traditional plants operate based on set points that don't account for the variable nature of incoming ore. AI is changing this by bringing real-time adaptability to the crushing and separation circuits.
How AI Optimizes the Plant
1. Computer Vision for Ore Sorting
Cameras analyze the color, texture, and shape of ore on a conveyor belt, allowing for automated sorting that removes waste rock before it even reaches the crusher.
2. Adaptive Crushing & Grinding
Neural networks analyze the particle size distribution in real-time and adjust crusher settings and mill speeds to optimize energy use and product quality.
3. Flotation & Separation Optimization
AI models predict the optimal chemical dosage and aeration levels in flotation cells based on the specific mineralogy of the feed.
The Results: Higher Yields, Lower Costs
- 25% Increase in Recovery Rates: Extracting more value from every ton of ore.
- 30% Reduction in Energy Consumption: Optimized grinding—the most energy-intensive part of the plant.
- Consistent Product Quality: Reducing variability in the final concentrate.
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