article · Sustainability
Digital twin technology offers substantial potential for the mining industry by monitoring performance, simulating operational outcomes, and forecasting yields and errors. These capabilities can improve efficiency, productivity, and sustainability while supporting asset lifecycle management from initial design through to decommissioning. However, successfully integrating digital twins into mining businesses remains an unresolved operational challenge. To address this gap, existing digital twin case studies focusing on concept, design, and development are examined alongside Industry 4.0 reference architecture models and value lifecycle management principles. A multi-layered digital twin architecture framework specifically designed for the mining sector is presented to provide a structured basis for future case studies and practical implementations.
Modern mining operations face growing pressure to operate sustainably and efficiently. By outlining a digital twin framework tailored to asset lifecycle management, this work helps industrial operators understand how virtual modelling, simulation, and predictive tools can be systematically organised to improve equipment reliability, reduce operational errors, and enhance overall operational performance.
The proposed framework could enable mining companies and industrial software developers to design digital twin applications for asset monitoring and maintenance. The research is at an early conceptual stage, establishing a reference framework rather than a validated commercial product, and is intended to inform subsequent development and case studies prior to operational deployment.
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In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.
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DOI: 10.3390/su15043470
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