Process Optimization 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.

AI-Optimized Ore Blending, Stockpile & Plant Feed Strategy

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

Key Takeaways

MineMax by NTWIST is explicitly AI-driven — it sits as a supervisory layer over existing mine and plant systems, continuously learning from operational outcomes to make real-time blend and feed recommendations.
Deswik Blend, BOLT, and GO are the leading optimization-based planners for stockpile management, multi-commodity blending, and joint mine-to-market feed decisions.
Dynamic blend-consistency dispatching (routing trucks to maintain a target feed blend in real time) has been reported to raise truck cycle efficiency by roughly 11% in deployed systems.
MineMax’s four core models — OreMax, DynaMax, PlanMax, MillMax — cover ore tracking, stockpile intelligence, feed forecasting, and optimization as one connected decision layer.
All three items here (stockpile, plant feed, and blending strategy) are really one problem viewed from three points in the material flow, and increasingly solved by the same platforms.

TL;DR

Layer an AI optimization platform (MineMax/NTWIST, or Deswik’s Blend/BOLT/GO suite) on top of your existing mine and plant systems to continuously recommend stockpile allocation, plant feed mix, and dispatch blending targets from real-time ore-tracking data, rather than planning blends on a fixed weekly/monthly schedule.

Blasthole Logging

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

Key Takeaways

Hexagon Drill Assist puts AI directly into the drilling algorithm, automatically managing drill parameters from rig-sensor data as it drills — no manual parameter entry.
Operators report training time dropping from years to about 15 minutes with Drill Assist, alongside real gains in fragmentation, ore dilution reduction, and energy use per metre.
Fully automated computer-vision lithology/cuttings logging is still research-stage — sensor-while-drilling systems from IMDEX and Epiroc are the more mature adjacent capability.

TL;DR

AI blasthole logging today mostly means AI-driven drilling itself — Hexagon Drill Assist manages drill parameters automatically from live rig-sensor data, capturing consistent, high-quality blasthole logs as a byproduct, while automated cuttings/lithology vision-based logging remains a research direction rather than a deployed product.

Blasting Sequence

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

Key Takeaways

Blast sequencing and timing design live inside broader blast-design suites: Orica BlastIQ and Deswik.Blast.
BlastIQ is a cloud platform for storing, managing, and sharing blast-related information, giving quality-control visibility over blast design and execution rules.
Marked “Emerging” — sequencing modules exist inside mature commercial suites, but the AI-driven optimization of sequence/timing (versus just digitizing existing manual practice) is still a developing capability.
BlastIQ has documented API integration with Deswik.Ops, so these tools aren’t necessarily either/or — they’re increasingly interoperable.

TL;DR

Manage blast sequencing and timing through cloud-based platforms like Orica BlastIQ, which centralizes blast-related data and enforces design/loading rules, with Deswik.Blast offering a comparable sequencing capability integrated into the broader Deswik suite.

Chemical Analysis

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

Key Takeaways

On-stream XRF/XRD analyzers replace periodic lab assays with continuous, real-time concentrate-grade data.
Malvern Panalytical and Thermo Fisher both sell mature on-stream slurry analyzers used in copper concentrators today.
This continuous data stream is what makes closed-loop AI process control possible downstream (flotation, blending, smelter feed).
Without on-stream analysis, your fastest feedback loop is the next lab batch — often hours behind the plant.

TL;DR

Swap (or supplement) periodic lab assay of your concentrate stockpile with an on-stream XRF/XRD slurry analyzer, so grade data updates continuously and can feed real-time AI process-control loops instead of sitting in an hourly or shift-based lab queue.

Crushing

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

Key Takeaways

Crusher circuit optimization is one of the more measurable AI wins on this list: reported gains of 1-5% throughput and 10-15% energy reduction from AI setpoint optimization.
Imubit’s Closed Loop AI Optimization uses reinforcement learning to write optimal setpoints directly to control systems in real time, rather than relying on a static linearized process model.
ABB Ability for Mining is the more established alternative, though it depends more on linearized process models that can struggle with the genuinely non-linear behavior of crushing circuits.
Marked “Yes” — this is a production capability with measured results at deployed sites, not a lab demo.

TL;DR

Optimize your crusher circuit with closed-loop AI control — Imubit uses reinforcement learning to continuously adjust setpoints against live plant data, reporting real throughput and energy gains over traditional control approaches like ABB Ability.

Geometallurgical Reconciliation

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

Key Takeaways

Geomet reconciliation — comparing predicted vs. actual mill performance to recalibrate your models — has a documented real-world AI implementation at the Tropicana Gold Mine.
The Tropicana approach uses near-real-time recalibration of Work Index and geomet block models based on the gap between predicted and actual mill throughput/recovery.
Cancha productizes a similar reconciliation workflow for teams that don’t want to build custom ML pipelines in-house.
This is one of the more mature AI applications in the geomet space — it’s “Yes” not “Emerging” because there’s a published, working case study, not just a vendor pitch.

TL;DR

Recalibrate your geomet block model automatically by feeding actual mill performance back into your prediction model — the Tropicana Gold Mine’s published approach does this in near-real-time, and Cancha offers a productized version of the same idea.

Grinding

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

Key Takeaways

Grinding (SAG/ball mills) is one of the single largest energy consumers on a mine site — comminution overall is roughly half of total plant energy use, so small efficiency gains are worth real money.
Closed-loop AI controllers now write mill setpoints directly, rather than just advising an operator, using deep neural networks trained on historical plant data instead of a first-principles model.
Vendors report 2-5% throughput gains, lower specific energy, fewer liner strikes/shutdowns, and better downstream recovery from AI-driven grinding control.
This is a mature, commercially available category — not an emerging research idea — with multiple named vendors actively selling into mining today.

TL;DR

You do AI-assisted grinding mostly by adopting a closed-loop advanced process control (APC) platform — ABB Ability Expert Optimizer, Imubit’s Closed Loop AI Optimization, or Metso’s control stack built on the MillSense charge sensor — that learns your mill’s behavior from historical data and continuously adjusts feed rate, water addition, and mill speed in real time.

Long Term Planning

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

Key Takeaways

Long-term mine planning is one of the more genuinely AI-native categories on this list: Maptek Evolution runs many scheduling scenarios in parallel on cloud compute, with solutions that learn from each other to iteratively improve.
Whittle (Enterprise Optimizer) and Deswik.Sched are the other major players, each with their own take on schedule optimization.
This differs from a traditional single-pass optimizer: Evolution’s approach is closer to evolutionary/genetic search than a one-shot LP solve.
Marked “Yes” (not “Emerging”) because this is a shipped, production capability, not a research prototype.

TL;DR

Run long-term mine planning through Maptek Evolution, which evaluates many scheduling scenarios in parallel and iteratively improves solutions using techniques adjacent to evolutionary search, rather than one static LP schedule.

Mill Surveys

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

Key Takeaways

Traditional mill surveys are manual, periodic snapshots — a team samples the circuit for a shift and calculates a mass balance after the fact.
That’s being displaced by continuous capture: plant historians like AVEVA PI System log the same circuit variables 24/7, at far higher resolution.
This is genuinely an “Emerging” category, not a mature off-the-shelf product — most operations are still layering analytics on top of historian data rather than buying a dedicated “AI mill survey” tool.
Imubit and similar industrial-AI platforms are the closest thing to a purpose-built analytics layer for this data.

TL;DR

Replace periodic manual mill surveys with continuous historian-based data capture, then apply industrial-AI analytics (like Imubit) on top to get survey-grade mass-balance insight in real time instead of once a quarter.

Mining Block Model

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

Key Takeaways

Block modelling itself still runs on established commercial platforms: Datamine Studio RM, Maptek Vulcan, Hexagon HxGN MinePlan, and Deswik.
AI is arriving as an add-on layer, not a replacement — Datamine’s 2026 MineScape release added AI-enabled scheduling assistance built on top of existing block models.
This is marked “Emerging” deliberately: the core block-modelling engines are mature and largely non-AI; the AI value is concentrated in newer scheduling/optimization modules bolted onto them.
If you’re choosing a platform, the question isn’t “which one has AI” (most are adding it) but “how mature is the specific AI module you need.”

TL;DR

Build your mining block model in an established platform (Datamine Studio RM, Maptek Vulcan, Hexagon HxGN MinePlan, or Deswik), then look at the AI-enabled scheduling/optimization modules these vendors are now layering on top for the actual intelligence gains.

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.

Real Time Geometallurgy

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

Key Takeaways

“Real-time geometallurgy” is the umbrella term for fusing ore characterization proxies (grind/crush hardness proxies, mineral liberation, %solids) into live setpoint recommendations for the plant, rather than waiting for periodic lab testwork.
It runs on the same closed-loop AI platforms used for crushing/grinding optimization — this is an application pattern, not a separate piece of software.
The core idea: instead of running a static geomet model built from quarterly composite samples, you stream ore-property predictions into the control room as material actually arrives at the plant.
This is commercially available today via general-purpose industrial AI/APC vendors, not a mining-specific off-the-shelf product — expect a systems-integration project, not a shrink-wrapped purchase.

TL;DR

Real-time geometallurgy means piping your ore characterization proxies (hardness, liberation, clay content, %solids) into a closed-loop AI/soft-sensor platform like Imubit or ABB Ability for Mining so the plant adjusts itself to the ore in front of it, instead of running on a fixed setpoint tuned for average ore.

Real-Time Ore Grade Sensing at the Shovel

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

Key Takeaways

MineSense ShovelSense mounts high-speed XRF sensors on shovel/excavator buckets to scan material for grade in real time, bucket by bucket.
The system’s ML models are trained per ore body, so grade estimates adapt to your specific deposit’s mineralogy rather than using a generic calibration.
MineSense BeltSense is the conveyor-belt equivalent — same sensing concept applied downstream of the shovel.
Real-time grade data feeds directly into automated truck-diversion decisions, routing ore to mill, stockpile, or waste without waiting for lab assays.
This is a mature, commercially deployed technology (ai_relevant: Yes) — not experimental.

TL;DR

Fit shovel buckets with high-speed XRF sensors (MineSense ShovelSense) that scan every dig pass and feed ore-body-specific ML grade models directly into your dispatch system, so trucks get routed by measured grade instead of block-model estimate alone.

Thickening

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

Key Takeaways

AI-based thickener control is a published, validated strategy — reinforcement-learning (proximal policy optimization) controllers have reported 10-15% flocculant savings in the literature.
There’s no single dominant named “AI thickener” product yet — this capability currently ships as an add-on within general advanced process control (APC) platforms from ABB, Metso, and Yokogawa rather than a standalone thickening-specific tool.
The AI target is flocculant dosing and underflow density control — getting the right amount of chemical in at the right time as feed conditions (solids %, mineralogy) fluctuate.
Same general closed-loop AI/APC pattern as grinding and flotation control — this is part of a broader plant-wide advanced control adoption rather than an isolated project.

TL;DR

AI thickener control means applying a model-predictive or reinforcement-learning controller to flocculant dosing and underflow density — available today as a module within APC platforms (ABB, Metso, Yokogawa) or via AI-native platforms like Imubit — rather than as a dedicated off-the-shelf thickening product.