Stop the Slip: How AI Verifies Stockpile Grades and Prevents Ore Loss

Why Manual Grade Checks Just Don’t Cut It Anymore

You can’t prevent what you can’t see - and for most mine sites, stockpiles remain the biggest blind spot. Despite precise blast models and mill sensors, material sitting on the ROM pad is often treated like a black box. That’s how ore gets misrouted, diluted, or lost entirely.

This article explains how AI-powered systems - from sensors to smart models - are helping mines catch grade discrepancies early, reduce financial leakage, and finally close the loop between mine plan and plant performance.

The Invisible Losses

Grade dilution doesn’t always come from bad blasts or bad feed - it often starts in the stockpile. When visual estimation or infrequent surveys are your only tools, misclassified or variable-grade material blends in unnoticed.

We’ve seen operations that only uncover the problem weeks later - when recovery drops or reconciliation flags a discrepancy. At that point, the value is already gone. And the opportunity to adjust has passed.

That’s why stockpile intelligence needs to be proactive, not reactive.

What AI-Driven Verification Actually Looks Like

At NTWIST, we deploy AI to integrate source data, drone-based surveys, loader activity, and real-time plant feedback. The result is a dynamic grade estimate for every section of the stockpile - constantly recalibrated based on what's been added, reclaimed, or measured downstream.

Key capabilities include:

• Anomaly detection: AI flags discrepancies between expected and observed grades using historic blending patterns.
• Live feedback loops: Recovery shortfalls feed back upstream to adjust stockpile models on the fly.
• Probabilistic classification: Grade values are modeled with confidence intervals, not guesses.

As noted in the Journal of Mining Science, AI systems like these are being adopted globally by majors like Rio Tinto to automate classification, reduce loss, and de-risk blending decisions.

Benefits Beyond Accuracy

Smart grade verification systems don’t just improve measurement - they change behavior:

• Planners trust the model more - and adjust more confidently.
• Dispatch aligns routing to real value, not assumed location.
• Metallurgists stop firefighting and start forecasting.

The result: less ore loss, fewer surprises, and a site that knows what it’s feeding - every hour, every shift.

Conclusion: Verification Is Prevention

If you’re still relying on end-of-month adjustments to catch ore loss, you’re too late. AI-powered stockpile verification catches dilution early, flags misclassification fast, and keeps your operation aligned from geology to grind.

At NTWIST, we don’t wait for the data to tell us what went wrong. We build systems that ensure it goes right the first time.

Explore Stockpile Management Solutions

NTWIST. (2024). How AI Fixes Stockpile Uncertainty in Open-Pit Mines. Retrieved from https://ntwist.com/blog/ai-fixes-stockpile-uncertainty/

Ali, A. et al. (2022). Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review. Journal of Mining Science. Retrieved from https://link.springer.com/article/10.1007/s12345-022-ai-mining-review