Every mining operation optimizes in silos, each stage hits its KPIs.
And yet, value leaks between the handoffs. We’re exploring a different approach.
Every mining operation optimizes in silos, each stage hits its KPIs.
And yet, value leaks between the handoffs. We’re exploring a different approach.
Every mining operation we visit runs its processes in silos—pit to port, every stage chases isolated improvements. Blast patterns. Haul times. Crusher throughput. Mill recovery. Shipment grades.
But here’s what strikes us: each stage optimizes for itself.
” The industry talks about ‘operational efficiency’
but mostly what we have built is open-loop process
— without actionable feedback & feedforward. “
What if you could test downstream impacts of every upstream choice — and loop learnings back before silos lock them in?
Feed Forward. Feedback. Near Real-Time.
→ Feed Forward
Before the blast is fired, see how the resulting fragmentation will affect crusher throughput and mill recovery.
Before the ore reaches the crusher, feed‑forward its hardness and size so the circuit can tune itself ahead of time.
“What if the processing plant could ‘see’ what’s coming from the pit and adapt before the ore arrives?”
← Feedback
When the plant sees unexpected recovery rates, that insight propagates back to grade control and dig planning immediately.
When shipping penalties hit because of grade variability, that reshapes stockpile blending strategies the same day.
“This feedback happens in monthly reconciliation meetings. What if it happened in hours?”
Simulate blasting patterns and fragmentation to see downstream effects before firing. Optimize for recovery, not just rock breakage.
Model haulage routes and scheduling scenarios. See how fleet decisions affect downstream processing capacity and timing.
Predict crusher performance under real mining conditions. Understand how upstream fragmentation affects wear and throughput.
Simulate material separation to achieve precise sizing. Feed forward to milling with predictable particle distribution.
Analyze charge motion, liner wear, and energy efficiency. Feedback to upstream when ore characteristics affect performance.
Optimize process conditions for maximum recovery. The final proof point for upstream decision quality.
The efficiency of material handling stages—conveyors, chutes, crushers, and mills—directly impacts mining productivity and costs. Because granular materials show complex behavior, Discrete Element Method (DEM) is essential for accurate modeling. In this webinar, KOT Engenharia shows how EDEM software optimizes equipment performance in mining and ore processing operations.
Multidisciplinary CAE enables fast, cost-effective diagnosis and supports corrective maintenance and renovation decisions. Choosing the right simulation approach is critical for success. In this webinar, experts from Altair and Vibracon explain how multibody dynamics delivers reliable results when finite element methods fail to meet timeline and correlation requirements.
Efficient infrastructure and logistics are vital for productive, safe mining operations. In this webinar, Altair and Naves Engenharia present a case study on optimizing mining road networks, fleet sizing under NR-22 regulations, and haul truck selection using combined Multi-Body Dynamics (MBD) and Discrete Element Method (DEM) simulations to improve performance and safety.
Building blocks are ready—simulation, IoT, and enterprise platforms.
What’s missing is a simulation-led architecture uniting pit-to-port with predictive and operational feedback.
We seek mining operations ready to test this approach, overcome silos, and unlock higher efficiencies.