Digital twin technology has gained momentum across industries, and mining is no exception. But in a sector known for complex processes and legacy systems, the question remains: is it just a buzzword, or can digital twins deliver measurable value? This article explores real applications, reported returns, and where the opportunity truly lies for mining operations.
A digital twin is a dynamic, real-time digital replica of a physical asset, process, or system. In mining, this could be a mill circuit, haulage network, or even an entire processing plant. Unlike static simulations, a digital twin uses live operational data to continuously update and simulate system behavior under different conditions (ScienceDirect, 2024).
Mining companies are already using digital twins to monitor performance, simulate scenarios, and make proactive decisions:
Process Optimization: ABB implemented a digital twin at Boliden’s Aitik mine to support advanced process control in flotation circuits - improving both throughput and energy efficiency (ABB, 2023).
Material Handling Simulations: Sibanye-Stillwater deployed a backfill simulation twin to reduce bottlenecks in material movement and improve scheduling decisions (MOSIMTEC, 2023).
Strategic Planning: Companies use digital twins to run scenarios before making capital investments, helping de-risk expansion plans (Forbes, 2024).
While implementation costs can be high, early adopters are reporting compelling gains. Visual Capitalist’s industry survey showed median ROIs above 200% for digital twin deployments across energy, manufacturing, and mining sectors (Visual Capitalist, 2024).
Returns typically stem from:
Reduced downtime through predictive diagnostics
Better energy management and process tuning
Faster time-to-decision through high-fidelity simulation
At NTWIST, we help mining clients realize these returns by creating focused, modular digital twins tied to specific operational KPIs - not generalized dashboards or hype-driven platforms. The result: faster wins, clearer decisions, and measurable ROI.
Despite the promise, digital twins are not plug-and-play. Key challenges include:
Data Quality: A digital twin is only as good as the input - poor sensor calibration or outdated models can undermine trust.
Integration: Connecting multiple legacy systems into one real-time pipeline is a major technical hurdle.
Adoption: Without buy-in from both engineering and operations, even the most advanced model won’t get used effectively.
Digital twins are not hype - but they are often misunderstood. For mining operations with the right foundation, they can offer fast insights, optimized processes, and a real return on investment. The key is to start small, tie the deployment to business-critical metrics, and build from a use case that’s already bleeding value.
ReferencesABB. (2023). Case Study: The Use of a Digital Twin with Advanced Process Control at Boliden Aitik Mine. Retrieved from https://new.abb.com/mining/digital-applications/...
Forbes. (2024). Digital Twins For Mining Industries: A Transformative Technology. Retrieved from https://www.forbes.com/councils/.../digital-twins-for-mining-industries...
Visual Capitalist. (2024). Charted: The Return on Investment of Digital Twins. Retrieved from https://www.visualcapitalist.com/dp/charted-the-return-on-investment-of-digital-twins/
ScienceDirect. (2024). Exploring Digital Twin Systems in Mining Operations: A Review. Retrieved from https://www.sciencedirect.com/science/article/pii/S2950555024000582
MOSIMTEC. (2023). Case Study: Sibanye-Stillwater Nye Backfill Simulation (Process Digital Twin). Retrieved from https://mosimtec.com/.../sibanye-stillwater-nye-backfill-simulation...