Introduction
The weaknesses of conventional simulations and ever-increasing capabilities offered by technological trends have compressed the spring of the surface mining industry for a giant leap towards Mining 4.0. Dynamicity and autonomy are two features that ordinary simulation models fail to address. That’s why the Digital Twin concept is taking center stage nowadays and appears to hold the key to the mines of the future, wherein real-time data exchange and cognitive decision-making govern (Hazrathosseini & Moradi Afrapoli, 2023). Digital twins are revolutionising the industrial environment by enabling the creation of virtual replicas of assets, processes and systems. The integration of data from several sources, such as sensors, enables simulation of real-life conditions and therefore helps to facilitate rapid and appropriate decision-making in relation to a given situation. This article explores the methodology for applying digital twins in mining, highlighting the benefits, challenges and prospects.
What are Digital Twins?
A digital twin is a virtual representation that mimics the structural and dynamic characteristics of a real environment. In mining, they can be used to represent an entire mining operation, individual pieces of equipment and geological structures. Simulating mining processes under different conditions enables engineers, geologists and other operators to test scenarios, analyse the results and draw conclusions that will enable them to optimise their activities without interrupting their operations on the real site.
A digital twin is an interactive system that changes in tandem with its physical counterpart, rather than only being a static model. This feature makes digital twins an effective tool for decision-making and operational efficiency in the mining sector by enabling predictive analytics, optimisation, and risk reduction.
The application of Digital Twin in mining
Digital twins can be applied in several areas of the mining sector, from exploration and ore transport to processing and safety management. In the following paragraphs, we present some key applications.
Automation and Optimisation
Digital twin technology plays a crucial role in automating and optimising mining processes, such as ore processing and transportation. Digital twins can be used to create a virtual replica of the mine site for predictive modeling and simulation. This helps companies identify potential problems before they arise and mitigate risks associated with costly investments in capital equipment. The technology also provides crucial data points that can be used to analyse the performance of existing processes and systems, allowing miners to make informed decisions about optimising operations. For instance, digital twins can determine the most efficient route for ore transportation and analyse the impact of different operations on energy consumption (10 Applications of Digital Twin in the Mining Industry – RemSense, 2023).
Mine Planning and Design
One of the most critical applications of digital twins in mining is in mine planning and design. Traditional mine planning involves significant uncertainty, as it is based on geological models that may not fully capture the complexities of the ore body. Digital twins, however, enable companies to create a dynamic, 3D virtual model of the mine, incorporating real-time data and geospatial information. These models allow engineers to simulate different mine designs, assess the feasibility of different extraction techniques, and predict ore recovery rates. By running simulations of various scenarios, mining companies can optimize the design of the mine to maximize efficiency and reduce waste, while minimizing costs and environmental impact. Additionally, digital twins can help mining companies plan more sustainable operations by incorporating data on energy usage, water consumption, and emissions into the planning process ((3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn, n.d.).
Process Optimization
Mining processes such as crushing, grinding, and flotation are energy-intensive and complex, often requiring continuous adjustments to optimize performance. Digital twins can simulate these processes, allowing operators to test different configurations and process parameters in a virtual environment. For example, a digital twin of a processing plant can be used to simulate how changes in ore composition, feed rates, or grinding media affect ore recovery and energy consumption. By running virtual simulations of different scenarios, operators can optimize process parameters in real-time to improve recovery rates, reduce energy consumption, and minimize environmental impact. This level of process optimization is difficult to achieve using traditional methods, where adjustments are often based on trial and error ((3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn, n.d.).
Equipment maintenance
Digital twin technology can significantly enhance equipment maintenance in the mining sector. They can monitor operational performance in real time by utilising data gathered from sensors on physical equipment. This allows maintenance teams or contractors to detect problems early on and take preventive measures, thus reducing the need for expensive repairs and downtime. Furthermore, digital twins can be used to provide detailed insights into the condition of equipment. That means maintenance managers and supervisor can anticipate when maintenance and repairs will be required and plan accordingly, enabling miners to reduce their overall maintenance costs while improving safety and protecting assets (10 Applications of Digital Twin in the Mining Industry – RemSense, 2023).
Benefits of Digital Twins in mining
The application Digital Twin in mining offers several advantages
Visibility into the remaining asset life of critical infrastructure
Digital twins can seamlessly integrate with advanced analytics and machine learning algorithms to forecast wear and tear, thereby providing a more accurate estimation of the remaining useful life of infrastructure components. This helps operators make informed decisions about when to repair, maintain, or replace assets, ensuring their operations’ continued efficiency and sustainability (5 Contributions of Digital Twin in Mining, 2023).
Hotspot identification and predictive maintenance
Digital twins facilitate hotspot identification and predictive maintenance by monitoring equipment performance and detecting potential issues before they escalate into costly failures. This proactive approach reduces downtime, minimizes repair costs, and extends the life of critical assets. For example, a common issue with mining excavators is the formation of cracks and other defects on the boom of mining excavators. With digital twins, operators can catch problems in advance, fixing them before they worsen and cause costly downtime. Such hotspot identification also helps identify potential safety hazards, enabling mining operators to implement preventive measures and maintain a safer working environment. Additionally, they can be used for training purposes, ensuring that employees are well-equipped to handle potential risks (5 Contributions of Digital Twin in Mining, 2023).
Cost savings and sustainability
Digital twins help mining companies reduce costs by minimizing unplanned downtime, improving resource management, and optimizing energy usage. By predicting equipment failures and optimizing processes, companies can avoid costly breakdowns and reduce operational expenses. Sustainability is a growing concern for the mining industry, and digital twins can help companies reduce their environmental footprint. By simulating the environmental impact of different processes, digital twins allow companies to optimize resource use, reduce emissions, and minimize waste. Additionally, digital twins can support the integration of renewable energy sources into mining operations, helping companies transition to more sustainable practices ((3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn, n.d.).
Challenges in implementing Digital Twins
Although the use of digital twins offers many advantages for mining companies, there are several challenges to be overcome before they can be implemented.
Data integration
Implementing an effective digital twin requires the integration of data from several sources, including on-board sensors, operational records, and so on. For the system to function properly, the quality and accessibility of the data must be guaranteed.
Cybersecurity risk
Mining operations are particularly susceptible to cybersecurity risks as they become more digitalised. Digital twins are susceptible to cyberattacks since they necessitate the transfer of substantial volumes of data across networks. To safeguard their digital assets and guarantee the security of their operations, mining companies need to put strong cybersecurity measures in place.
Technology investment
Implementing digital twin technology requires significant investment in hardware, software, and training. Mining companies must ensure that they have the necessary infrastructure, such as IoT networks and cloud computing platforms, to support the real-time collection and analysis of data. Additionally, employees need to be trained to use digital twin platforms effectively ((3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn, n.d.).
The future of Digital Twins in Mining
The potential uses of digital twin technology in mining will increase as it develops further. Future advancements could involve combining machine learning algorithms with artificial intelligence (AI) to facilitate autonomous decision-making and process optimisation. Furthermore, augmented reality (AR) and digital twins could be combined to give operators real-time visualisations of mining machinery and procedures. AI and the ongoing development of digital twins will improve predictive analytics even more and provide mining firms previously unheard-of levels of control and optimisation. The mining sector can increase productivity, cut expenses, promote sustainability, and give workers safer working conditions by utilising digital twins ((3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn, n.d.).
Conclusion
To sum up, digital twin technology is transforming the mining industry by enabling real-time simulations, predictive analytics, and process optimization. It offers significant benefits, including cost savings, sustainability, enhanced safety, and improved operational efficiency. However, challenges such as data integration, cybersecurity risks, and high implementation costs must be addressed. As technology evolves, integrating AI, machine learning, and AR with digital twins will unlock new possibilities for automation, optimization, and environmental stewardship, paving the way for smarter and more sustainable mining operations.
Reference
(3) Digital Twins in Mining: Optimizing Operations Through Virtual Simulations | LinkedIn. (n.d.). Retrieved December 4, 2024, from https://www.linkedin.com/pulse/digital-twins-mining-optimizing-operations-through-ali-soofastaei-niavf/
5 Contributions of Digital Twin in Mining. (2023, April 25). https://akselos.com/the-digital-future-of-mining-digital-twins/
10 Applications of Digital Twin in the Mining Industry—RemSense. (2023, October 31). https://remsense.com.au/10-applications-of-digital-twin-in-the-mining-industry
Hazrathosseini, A., & Moradi Afrapoli, A. (2023). The advent of digital twins in surface mining: Its time has finally arrived. Resources Policy, 80, 103155. https://doi.org/10.1016/j.resourpol.2022.103155