Fixing Mine-to-Mill Breakdowns for Real-Time Optimization

Fixing Mine-to-Mill Breakdowns: A Practical Guide to Mine-to-Mill Optimisation

Mine-to-mill optimisation links three worlds that often drift apart: drill and blast, haulage, and comminution. When these links fracture, every downstream circuit pays the price in lower throughput, rising energy cost, and strained maintenance budgets. This article explains where breakdowns creep in, how to spot them early, and what a data-driven recovery playbook looks like. Our goal is to help mining leaders convert “lost” tonnes into verifiable value, using proven practices rather than one-off heroics.

If you want a broader view of how AI is reshaping industrial decision-making, read our companion post AI in Mining and Manufacturing: Collaboration Over Replacement.

Why Mine-to-Mill Breakdowns Happen

• Poor fragmentation control. Variable blast energy dumps oversize boulders into the primary crusher, throttling the mill.
• Orebody surprises. Harder geometallurgical domains hit the mill without warning because geology, planning, and processing do not share live data.
• One-way feedback loops. Blast engineers seldom see mill performance metrics, so designs stay static even when throughput flags.
• Data silos. High-frequency data from shovels, crushers, and mills lives in separate historians, blocking rapid root-cause analysis.

The Cost of Letting Fragmentation Drift

The U.S. Department of Energy notes that comminution routinely consumes 30 to 40 percent of a mine’s total site energy, more than haulage or ventilation (U.S. Department of Energy, 2023). When oversize material chokes the SAG circuit, every extra kilowatt eaten by the mill is energy diverted from cash flow and sustainability targets. A modest 3 percent drop in mill utilisation across a 30 000 tpd concentrator equates to ≈330 t of lost production per day.

Four Signals Your Mine-to-Mill Chain Is Slipping

1. SAG mill power spikes during specific ore blocks in the weekly plan.
2. Load-and-carry cycle times creep up after blast patterns change.
3. Crusher liner life shortens while throughput is flat or falling.
4. Rework in short-interval control meetings rises because plan versus actual keeps diverging.

A Data-Driven Recovery Playbook

1. Establish a Shared First-Principles Model

Create a fragmentation-to-throughput model that drill-and-blast, planning, and processing teams trust. Modern cloud simulators can relate powder factor, burden, spacing, and toe hold directly to mill power draw, closing the communication gap.

2. Instrument the Chain

• Deploy fragmentation imaging at the muck pile and primary crusher.
• Stream shovel payload data and blast geometry into the same historian that houses mill SCADA tags.
• Tag each ore block with a unique identifier so mills can feed performance back to planners in near real-time.

3. Run Digital Test Blasts First

Before loading a single hole, run multiple blast designs through the model to forecast P80, energy consumption, and projected mill tonnes per power hour (t/kWh). Only field-test designs that beat the baseline by a meaningful margin.

4. Iterate in 90-Day Sprints

Lock in a quarterly cadence: baseline → analyse → optimise → control. Each sprint should target a single constraint (for instance, fines generation or oversize reduction) so improvements remain measurable instead of being lost in competing KPIs.

Proof in the Numbers: Dyno Nobel’s Drill-to-Mill Initiative

One North American metals mine partnered with Dyno Nobel to tighten its drill-to-mill value stream. By increasing the -½-inch fines fraction by up to 10 percent, the site unlocked a 15 percent lift in mill throughput and realised $58 million in added value across one year (Dyno Nobel, 2023). The entire programme hinged on four stages: baselining, analysis, optimisation, and control, exactly the cadence outlined above.

Key Takeaways for Leaders

• Mine-to-mill optimisation is continuous, not a one-off project.
• Shared data infrastructure is the foundation; without it, feedback loops stall and each group reverts to local optimisation.
• Model-driven design cuts trial-and-error. Simulate first, then fire.
• Iterative sprints sustain momentum. Quarterly targets keep wins visible and justify ongoing investment.

Conclusion: Turn Breakdowns into Breakthroughs

Every operation has latent tonnes trapped between the blast face and the mill. By treating the mine-to-mill chain as one system and committing to data-driven continuous improvement, sites can release that value while trimming power intensity and extending asset life.

Explore NTWIST Mine-to-Mill Solutions

U.S. Department of Energy. (2023). Mine-to-mill optimization brief. Retrieved from https://www1.eere.energy.gov/manufacturing/resources/mining/pdfs/minetomill.pdf

Dyno Nobel. (2023). Drill-to-Mill™ project adds $58.1 million for metals mine by optimizing mill throughput. Retrieved from https://www.dynonobel.com/.../drill-to-mill-project-adds-581-million-for-metals-mine-by-optimizing-mill-throughput.pdf