Digital twin technology – creating virtual replicas of physical assets and operations – has moved from concept to reality in mining. These digital representations are changing how mining companies plan, operate, and maintain their operations.

What Mining Digital Twins Actually Are

A digital twin in mining is more than a 3D model. It’s a dynamic representation that:

Mirrors physical reality: The twin reflects current conditions, updated through sensor data and operational systems.

Simulates behaviour: Physics-based models predict how operations will respond to different conditions or decisions.

Connects systems: The twin integrates data from multiple sources into coherent operational views.

Enables prediction: By combining current state with simulation capability, twins can forecast future conditions.

At their best, digital twins provide a virtual environment where mine planners and operators can test decisions before implementing them in the real world.

Where Twins Are Delivering Value

Several mining digital twin applications have proven their worth:

Processing plant optimisation: Digital twins of concentrators and process plants simulate how parameter changes affect throughput and recovery. Operators can test adjustments virtually before changing real settings.

Mine planning: Twins that combine geological models with equipment simulation enable detailed schedule optimisation. Planners can evaluate thousands of scenarios to identify optimal approaches.

Ventilation management: Underground ventilation twins model airflow throughout mine workings. Changes to fan settings or ventilation infrastructure can be simulated before implementation.

Equipment maintenance: Twins of individual machines incorporate sensor data to model component condition. Maintenance can be scheduled based on predicted failure timing rather than fixed intervals.

Training: Realistic twin environments enable operator training without risking equipment or disrupting operations.

Building a Mining Digital Twin

Creating effective digital twins requires several components:

Physical models: Accurate 3D representations of infrastructure, equipment, and geology form the spatial foundation.

Process models: Mathematical models that simulate operational processes – crushing, grinding, flotation, haulage – enable behavioural simulation.

Data integration: Connections to operational systems provide the current state information that keeps twins synchronised with reality.

Visualisation: Interfaces that make complex information accessible to users who need to make decisions.

Simulation capability: Computation resources to run scenarios and predict outcomes.

No single vendor provides all these components. Effective twins typically combine technologies from multiple sources, integrated for specific operational needs.

team400.ai are increasingly involved in mining digital twin projects, developing the intelligent systems that interpret twin data and generate operational recommendations.

The Data Foundation

Digital twins require extensive data infrastructure:

Sensor networks: Twins need real-time data from throughout operations. Gaps in sensor coverage create blind spots in the twin.

Historian systems: Time-series databases store operational data for trend analysis and model training.

Integration middleware: Systems must share data across traditional boundaries. A twin needs mine planning, fleet management, processing, and maintenance data together.

Data quality: Twins inherit the quality of their input data. Poor data produces unreliable twins.

Cybersecurity: Connected twins create attack surfaces that must be protected. Separation of IT and OT networks while enabling data flow requires careful architecture.

Building this data foundation is often the largest investment in twin deployment.

Challenges in Implementation

Mining digital twin projects face several challenges:

Scope management: It’s tempting to build comprehensive twins of entire operations. More focused twins solving specific problems often deliver faster value.

Model maintenance: Physical and process models must be updated as operations change. Without maintenance, twins drift from reality.

User adoption: Twins must fit into existing workflows. Tools that operators don’t use don’t deliver value.

Computational requirements: Real-time twins of complex operations require significant processing power, especially for simulation.

Integration complexity: Mining operations use diverse systems from various vendors. Getting them to share data consistently is non-trivial.

Successful implementations typically start small, prove value, and expand incrementally.

Case Studies

Several mining operations have deployed digital twins with measurable results:

Anglo American: Their FutureSmart Mining programme includes digital twins of processing plants that have delivered significant efficiency improvements.

Newmont: Digital twins of underground operations support ventilation optimisation and equipment management.

Teck Resources: Processing plant twins enable rapid testing of operational changes before physical implementation.

These implementations share common characteristics: clear problem focus, strong data foundations, and sustained commitment to maintenance and improvement.

Integration with AI

Digital twins and artificial intelligence are natural partners:

AI-enhanced simulation: Machine learning models can accelerate simulation, enabling real-time predictions that physics-based models alone couldn’t achieve.

Optimisation: AI optimisation algorithms can search twin-enabled solution spaces to identify operational improvements.

Anomaly detection: By comparing twin predictions with actual performance, AI systems identify deviations warranting attention.

Prescriptive recommendations: Advanced twins can generate specific recommendations for operational changes, not just predictions.

A Melbourne-based firm focused on mining digital twins addresses these integration challenges, building intelligent systems that take advantage of twin capabilities.

The Vendor Landscape

Multiple vendors offer mining digital twin platforms:

Major OEMs: Caterpillar, Komatsu, and other equipment manufacturers offer twins focused on their equipment.

Mining software vendors: Dassault, AVEVA, and Bentley offer platforms that support mining applications.

Specialist providers: Mining-specific vendors offer twins designed for particular applications.

Custom development: Some operations build bespoke twins when commercial options don’t meet requirements.

Selection depends on scope, integration requirements, and existing technology landscape.

Future Directions

Digital twin technology in mining will continue to evolve:

Autonomous integration: Twins will increasingly support autonomous operations, providing the situational awareness that machines need to operate safely.

Extended reality: Twins will be accessible through augmented and virtual reality interfaces, enabling immersive interaction with operations.

Predictive capability: As models improve, twins will predict further into the future with greater accuracy.

Scope expansion: Twins will extend to cover complete operations – from exploration through reclamation.

The vision is comprehensive virtual operations where every decision can be tested and optimised before implementation. We’re not there yet, but each twin deployment moves the industry closer.