How DTs Are Transforming Mining
What Are These Virtual Replicas Capable Of?
DTs and GenAI
Limitations of DTs
Real-World Applications for DTs
A Useful Model or an Expensive Simulation Layer?
References and Further Reading
The mining sector is consistently facing pressures to meet stringent environmental regulations, boost operational efficiency, improve safety, and maximize resource extraction. Digital twins (DTs) have emerged as a powerful enabler of digital transformation in the mining sector.1-4
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DTs are dynamic virtual replicas of physical processes, assets, and entire mine sites. These models can enable predictive maintenance, improve efficiency, enhance safety, and support data-driven decision-making.1-4
This article explores the utility of DTs: are they useful models in the mining sector or expensive simulation layers?
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In the mining sector, DTs are now actively implemented to address crucial mining challenges and top strategic priorities. DTs improve health and safety by enabling predictive maintenance of safety equipment, simulation of hazardous scenarios, and remote monitoring, which reduce human exposure to hazards.1
The simulation of operational changes assists in effectively planning decarbonization strategies, optimizing energy consumption, and modeling environmental impacts, thereby improving sustainability and decarbonization. DTs also enable improved scenario planning for infrastructure development, operational adjustments, and mine expansion.1
DTs improve efficiency by simulating various extraction plans, optimizing workflows, and identifying bottlenecks, thereby improving productivity. Real-time data can be fed into DTs to support predictive maintenance, prolong asset life, and minimize downtime, ensuring optimal equipment utilization.1
What Are These Virtual Replicas Capable Of?
The simulation and optimization capability of DTs enables confident decision-making and incremental improvements, as different operational plans, new equipment configurations, or process changes can be evaluated in a risk-free virtual space. This ensures faster optimization cycles and cost-efficient experimentation.1
Monitoring asset health, anticipating operational bottlenecks, and predicting potential failures can be achieved by integrating real-time data from Internet of Things (IoT) sensors and equipment telemetry. This predictive insight capability improves safety and reduces downtime.1
DTs offer a common operating picture for isolated teams such as maintenance personnel, engineers, and geologists. Complex data becomes actionable and intuitive through three-dimensional (3D) visualization. Thus, better cross-functional alignment, faster problem-solving, and improved communication are made possible through enhanced visualization and collaboration.1
DTs help maintain a living record of a mine site, supporting better regulatory compliance and long-term planning. This lifecycle management capability improves asset knowledge, long-term strategic planning, and streamlined compliance.1
DTs and GenAI
The integration of DTs with generative artificial intelligence (GenAI) technologies enables better strategic decision-making. Through DT and GenAI, current mine plans, extensive historical data, and operating context can be analyzed to forecast realistic production outcomes.2
If production risks are identified by these forecasts, swift action can be taken to address them. Additionally, the integration of DTs and GenAI has uncovered better opportunities, democratized access, and enabled faster insight generation, allowing non-technical users to perform and design scenario analyses using natural language for better business decisions.2
The integration also enables data-driven decisions, leading to faster and more accurate planning. Results can be predicted, even for unexpected scenarios, and preparations can be made for diverse potential conditions through this approach.2
Limitations of DTs
While DTs have shown promise to digitally transform the mining sector, they also have several limitations that largely restrict their scope of application to specific areas such as fleet monitoring, optimization of drilling and blasting processes, and predictive maintenance of equipment.1,3
Major limitations include data and model integration issues, high initial investment, high computational requirements for real-time updates in 3D models and DT environments, scalability issues, and integration with legacy systems.1,3,4
Performance issues arise while balancing the resolution of 3D models with scalability for regional and site-wide analyses. Similarly, sophisticated DT models for equipment typically operate in silos without being combined into a unified system.3,4
Thus, it is difficult to obtain a holistic view of the whole mining operation, reducing the ability of DTs to provide optimization and understanding of the mining system. Another issue is the lack of standardization in DT methodologies and frameworks used in mining, which results in inconsistencies in data collection, processing, and utilization.3,4
This creates challenges in developing interoperable systems that seamlessly operate across the different stages of mining operations. In mining, several DT solutions are developed for specific cases and lack scalability, which limits broader application of DT systems across diverse mining geographies and operations.3
Such limitations make it difficult to make significant improvements in sustainability and efficiency. Moreover, the integration of DT systems with legacy systems used in mining is another technical challenge, as several mining operations depend on legacy infrastructure that is not suitable for integration with advanced digital technologies.3
Effective data management is also a key challenge, as mining generates huge amounts of data, but the ability to collect, store, and analyze this data in real time is limited. Other data issues include data security, quality, and consistency, which complicate the development of effective DTs.3
Real-World Applications for DTs
Practical DT implementations in mining demonstrate how virtual models improve operational efficiency, safety, and decision-making. At Waihi Mine in New Zealand, OceanaGold developed a cloud-based 3D DT of the tailings storage facility (TSF) using Bentley’s iTwin® IoT platform and Seequent® software.1
The system integrated geological, geotechnical, sensor, and operational data, enabling proactive monitoring of slope stability and pore pressure. It improved reporting, collaboration, and response to events such as heavy rainfall.1
BHP has implemented DTs at Copper South Australia, BMA, and Escondida to forecast production outcomes, identify risks, and optimize mining processes. These systems support improvements in ore fragmentation, haulage, material handling, and surface operations.2
At BMA, autonomous haulage programs combine AI, analytics, and DT modeling to predict performance and enable early intervention. DTs and GenAI are also being used to improve mine planning by testing operational variability and creating more reliable plans, increasing confidence in strategic decision-making.2
A Useful Model or an Expensive Simulation Layer?
DTs continue to prove their worth as useful models in the mining sector. While they require significant investment, advanced infrastructure, and effective data management, their benefits in improving safety, productivity, sustainability, maintenance, and decision-making demonstrate their practical value.
Applications and pilot projects at mines such as Waihi, BMA, Copper South Australia, and Escondida show that DTs can deliver significant operational improvements when effectively integrated into mining systems.
References and Further Reading
- Coming of age: How digital twin technology is changing the face of mining [Online] Available at: https://www.bentley.com/wp-content/uploads/ebook-mining-coming-of-age-en.pdf.
- The role of digital twins and AI in enhancing decision-making in the mining industry. [Online] BHP. Available at: https://www.bhp.com/news/bhp-insights/2025/02/the-role-of-digital-twins-and-ai-in-enhancing-decision-making-in-the-mining-industry.
- Nobahar, P., Xu, C., Dowd, P., and Shirani Faradonbeh, R. (2024). Exploring digital twin systems in mining operations: A review. Green and Smart Mining Engineering, 1(4). https://www.sciencedirect.com/science/article/pii/S2950555024000582.
- Liang, R., Zhang, C., Li, B., Saydam, S., & Canbulat, I. (2026). Evolution of visualisation and digital twin technologies in mining. International Journal of Coal Science & Technology, 13(1). https://link.springer.com/article/10.1007/s40789-026-00866-w.
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