How Stockpile Variability Hurts Recovery in Mining

How Stockpile Variability Erodes Recovery - And What to Do About It

Stockpiles are often viewed as buffers - a way to smooth out fluctuations in supply and maintain consistent feed to the plant. But in practice, stockpiles are rarely uniform. Variability in ore type, grade, hardness, and oxidation state can significantly impact plant performance and metal recovery. When that variability isn’t tracked or corrected, it leads to costly underperformance across crushing, grinding, flotation, and beyond.

What Causes Stockpile Variability?

Stockpile variability typically stems from several interacting factors:

• Geological Variance: Different ore zones contribute varying mineralogy, hardness, and particle size distribution.

• Blending Gaps: Poor blending strategies - or none at all - result in unbalanced feed quality.

• Time-Based Degradation: Long-term stockpiling can oxidize sulfides, altering flotation performance (MDPI, 2023).

For example, a recent study found that extended exposure of low-grade copper sulfide ore to weathering led to measurable declines in copper recovery due to increased oxidation (MDPI, 2023).

Why Variability Wrecks Recovery

When stockpiles vary unpredictably, the entire plant becomes reactive:

• Grinding Efficiency Drops: Harder ores or inconsistent size distribution force the mill to work harder, reducing throughput (ScienceDirect, 2023).

• Flotation Selectivity Suffers: Changes in surface chemistry, especially from oxidation, shift how reagents behave and reduce recovery.

• Operational Targets Become Unreliable: Inconsistent feed makes it harder to stabilize plant KPIs and predict output.

Without real-time insights into what’s in the stockpile - or a method to actively control the blend - operators are left chasing symptoms.

Strategies to Reduce the Impact

Fortunately, stockpile variability can be managed with the right mix of technology and operational discipline:

• Improved Blending Plans: Implementing post-crusher or dynamic blending strategies can rebalance feed quality before it hits the mill (LinkedIn, 2023).

• Real-Time Monitoring: Sensors and sampling can be used to continuously track material properties as stockpiles are reclaimed.

• Predictive Models: AI-based forecasts can simulate how blending decisions will affect plant stability and recovery (MDPI, 2021).

At NTWIST, we help mining teams integrate material tracking, predictive modeling, and blending optimization into one unified platform - reducing the operational cost of variability and boosting recovery rates through smarter, faster decisions.

Case in Point: Lihir Gold Mine

At Newmont’s Lihir gold mine, a digital twin implementation helped manage feed variability through mine-to-mill optimization. Simulating material flow and response allowed teams to anticipate metallurgical performance, adjust blending decisions, and increase plant stability (Metso, 2024).

Conclusion

Stockpiles are more than buffers - they are active, complex reservoirs of value and risk. Variability across those piles is one of the most under-monitored sources of recovery loss in mining. With the right visibility and control, operations can reclaim that lost value, improve metallurgical performance, and move from reactive to truly optimized throughput.