Real-Time Ore Grade Analysis

AI analyzes ore grade data from sensors on shovels and face scanners in real-time. This enables the precise blending of high- and low-grade material fed to the processing plant.

• Key Benefit: Stabilizes plant feed, maximizing recovery rates and throughput.
• Real Example: A copper mine increased overall recovery by 2.5% and reduced acid consumption by 15% through optimized blending, adding millions to annual EBITDA.

Computer vision systems on primary crushers or conveyors instantly identify and classify waste rock based on spectral signatures.

• Key Benefit: Diverts low-value material before it enters the energy-intensive processing circuit.
• ROI Impact: Reduces haulage, crushing, and milling costs for material that yields no product. One gold operation cut processing volume by 18%, directly lowering cost per ounce.

AI controllers use live ore grade and hardness data to autonomously adjust mill parameters like feed rate, water addition, and ball charge.

• Key Benefit: Maintains optimal grind size for liberation, preventing over-grinding (energy waste) or under-grinding (recovery loss).
• Business Case: A tier-1 iron ore producer achieved a 4% increase in throughput and a 7% reduction in specific energy consumption (kWh/ton), delivering a payback in under 12 months.

Integrate real-time face data with the block model, creating a continuously updated 'living' resource model.

• Key Benefit: Enables dynamic mine planning; short-term schedules can be adjusted daily to target higher-grade zones, improving head grade and project NPV.
• CIO Justification: Reduces the 'estimation to reality' gap, de-risking quarterly production forecasts and improving capital allocation confidence.

Drones with hyperspectral cameras and AI classify and map stockpiles, providing an accurate, real-time inventory of ore by grade.

• Key Benefit: Eliminates manual sampling errors and delays. Enables precise reclaim blending to meet precise plant feed specifications.
• Operational Gain: Reduces stockpile reconciliation variances from ±10% to under ±2%, minimizing value leakage and ensuring metallurgical balance.

Automated, sensor-based grade tracking creates a tamper-proof digital chain of custody from pit to product.

• Key Benefit: Provides auditable data for resource statement compliance (JORC, NI 43-101) and precise tracking of metal movement for ESG reporting.
• Strategic Value: Mitigates regulatory risk and enhances stakeholder trust by replacing manual, error-prone reporting with AI-verified data streams.

We deploy non-invasive hyperspectral imaging sensors and LIBS (Laser-Induced Breakdown Spectroscopy) units at key material transfer points. This phase establishes a digital baseline, correlating real-time sensor data with traditional lab assays to train the initial AI model.

• Key Activity: Install 3-5 sensor nodes on primary crusher feed and conveyor belts.
• Outcome: A validated data pipeline delivering >95% correlation with lab results within a 4-hour window, down from days.

The AI model goes live, providing second-by-second grade estimates. Operations teams use a dashboard to make immediate blending decisions, optimizing feed to the processing plant.

• Real Example: A copper mine used this phase to identify and isolate a low-grade ore zone (0.3% Cu) from a high-grade stream, preventing mill dilution and saving an estimated $120k in processing costs in the first month.
• Measurable Benefit: Achieve a 5-10% reduction in energy and reagent consumption per ton processed by stabilizing plant feed.

The system transitions from advisory to closed-loop control, automatically directing ore to appropriate stockpiles or processing lines. The final pilot report quantifies the hard ROI.

• Pilot Deliverable: A validated business case showing payback in <12 months.
• Quantified Metrics: +2-4% recovery rate through precise blending, 15-20% reduction in assay lab costs, and <1% deviation from monthly production forecasts.

This pilot transforms a geological data function into a core profit lever. The business case is built on three pillars:

• Revenue Protection: Maximize recovery of high-value minerals by preventing the processing of sub-economic material.
• Cost Avoidance: Slash energy, water, and chemical costs by providing a consistent, optimal feed grade to the plant.
• Strategic Agility: Enable real-time mine plan adjustments based on actual ore body knowledge, de-risking the entire value chain.

The pilot's AI model and data architecture become the foundation for a site-wide Digital Twin. Real-time grade data feeds into dynamic scheduling and predictive maintenance systems, creating a fully integrated, intelligent operation.

• Next Steps: Integrate with our Dynamic Mine Planning and Scheduling solution to adjust extraction sequences daily.
• Long-Term Value: Establish a single source of truth for ore body knowledge, enhancing resource estimation and life-of-mine planning.

A rapid, focused pilot delivers tangible evidence before a full capital commitment. It addresses key executive concerns:

• Proven Technology, Not R&D: We use field-hardened sensors and pre-trained mineralogy models, adapted to your specific ore.
• Clear Metrics, No Hype: Success is measured in dollars per ton and recovery percentage, not algorithm accuracy alone.
• Minimal Operational Disruption: Installation is conducted during planned maintenance, with no impact on production.