Digital Twin on Eigenform AI Geo Tooltips

AI-Optimized Blast Design and Execution

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Strayos uses drone photogrammetry and ML/genetic-algorithm optimization trained on historical blast outcomes to recommend burden, spacing, and timing before you drill.
O-Pitblast and Deswik.Blast are established commercial alternatives with strong simulation support for the same design step.
Orica BlastIQ is the cloud platform connecting drill data through to blast execution, standardizing how design intent gets carried into the field.
Explosive loading itself (physically charging the holes) has the least AI penetration of the group — current systems mostly add QA/QC checks rather than optimization.
Fragmentation and vibration prediction from these tools is trained on your own historical blast data, so accuracy improves the more blasts you feed back into the system.

TL;DR

Feed drone-captured bench topography and your historical blast performance data into an AI blast-design platform (Strayos, O-Pitblast, or Deswik.Blast) to get an optimized pattern before drilling, then execute and track it through a connected platform like Orica BlastIQ.

Dispatch and Control Room

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Fleet dispatch is one of the longest-established “AI-adjacent” systems in mining — Modular Mining’s DISPATCH (now under Komatsu) and Wenco FMS (Hitachi) have run truck-shovel optimization for decades.
What’s new is the AI layer on top: Modular’s Adaptive Config is an AI-powered tuning add-on that adapts dispatch rules to changing conditions without new hardware.
Academic work (reinforcement learning for adaptive ore dispatch) is pushing beyond rule-based/heuristic dispatch toward learned policies, but this is still mostly research-stage outside the vendor add-ons.
The control room’s dispatch system maintains a live digital twin of the mine — trucks, shovels, haul roads — which is the substrate any AI dispatch layer optimizes against.

TL;DR

Modernizing dispatch with AI today mostly means turning on an AI add-on to your existing fleet management system — Modular Mining’s Adaptive Config is the clearest named example — rather than replacing DISPATCH or Wenco FMS outright.

Operational Variability & Plant Integration

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

The core problem: your design model (from testwork) and your actual plant behavior drift apart as ore feed varies — this is what “operational variability” means in practice.
Digital twins and industrial-AI platforms are purpose-built to reconcile that drift continuously, rather than catching it in a quarterly review.
Imubit builds its process model directly from plant historian data, so it naturally captures real operational variability rather than idealized design assumptions.
Rockwell’s PlantPAx and equivalent DCS platforms from Honeywell/Emerson are the control-layer backbone these AI/digital-twin tools plug into.

TL;DR

Reconciling plant variability against your design model in real time — instead of after the fact — is now a solved problem at the platform level: connect a digital-twin or industrial-AI layer (Imubit, or a Honeywell/Emerson digital twin) to your DCS historian and let it flag and adapt to drift continuously.