“…Despite showing growth in recent years, the number of DT-related publications in the minerals industry is still significantly lower even compared to the power and energy sector as shown in Figure 9. This difference is largely due to the unique challenges associated with model complexity and unpredictability of mining systems (Hodgkinson and Elmouttie 2020; Okol'nishnikov et al 2021). Figure 9 Number of DT Publications in the Minerals Industry (Data exported from Scopus)…”
Section: Applications In the Minerals Industrymentioning
confidence: 99%
“…This can be especially problematic for mining companies running legacy systems dating back by decades. DT development in the minerals industry is also facing high levels of unpredictability due to large geological uncertainties and process variations (Hodgkinson and Elmouttie 2020; Nagovitsyn and Stepacheva 2021; Okol'nishnikov et al 2021). Unlike the manufacturing industry which produces standardised products under controllable working conditions, the demanding working conditions in the minerals industry call for a more flexible approach to DT implementation that puts more emphasis on the safety and well-being of its people and communities while ensuring the accuracy of the virtual replica (Stothard 2023).…”
Digital Twins (DT) have become popular in various industries to solve complex problems. A true DT allows for bidirectional real-time data flows between the physical and virtual worlds.The term DT was often overused, leading to confusion about its capabilities therefore this paper provides a review of the current state of DT and its applications in the minerals industry.Existing literature shows a slow adoption of DTs in the minerals industry, which is largely due to challenges associated with geological unpredictability, legacy system integration, cost, skill gaps, cybersecurity, and lack of standards.Combining immersive visualisation with real-time spatial computer graphics could improve the usability and acceptance of DTs in the minerals industry which has been used successfully in training while underexplored for operational scenarios. The study concludes by highlighting the need for further research to understand the effectiveness of such an approach in mineral operations.
“…Despite showing growth in recent years, the number of DT-related publications in the minerals industry is still significantly lower even compared to the power and energy sector as shown in Figure 9. This difference is largely due to the unique challenges associated with model complexity and unpredictability of mining systems (Hodgkinson and Elmouttie 2020; Okol'nishnikov et al 2021). Figure 9 Number of DT Publications in the Minerals Industry (Data exported from Scopus)…”
Section: Applications In the Minerals Industrymentioning
confidence: 99%
“…This can be especially problematic for mining companies running legacy systems dating back by decades. DT development in the minerals industry is also facing high levels of unpredictability due to large geological uncertainties and process variations (Hodgkinson and Elmouttie 2020; Nagovitsyn and Stepacheva 2021; Okol'nishnikov et al 2021). Unlike the manufacturing industry which produces standardised products under controllable working conditions, the demanding working conditions in the minerals industry call for a more flexible approach to DT implementation that puts more emphasis on the safety and well-being of its people and communities while ensuring the accuracy of the virtual replica (Stothard 2023).…”
Digital Twins (DT) have become popular in various industries to solve complex problems. A true DT allows for bidirectional real-time data flows between the physical and virtual worlds.The term DT was often overused, leading to confusion about its capabilities therefore this paper provides a review of the current state of DT and its applications in the minerals industry.Existing literature shows a slow adoption of DTs in the minerals industry, which is largely due to challenges associated with geological unpredictability, legacy system integration, cost, skill gaps, cybersecurity, and lack of standards.Combining immersive visualisation with real-time spatial computer graphics could improve the usability and acceptance of DTs in the minerals industry which has been used successfully in training while underexplored for operational scenarios. The study concludes by highlighting the need for further research to understand the effectiveness of such an approach in mineral operations.
“…X-ray fluorescence (XRF) and near infrared spectroscopy technology (visNIR) are widely used in geological exploration, soil pollution investigation, inorganic and organic content analysis 1 . In recent years, the construction of digital mines has attracted a lot of attention, and the application of new technologies has promoted the digital process of core exploration 2 . In particular, XRF and visNIR have made great progress in the field of digital core.…”
The fusion of X-ray fluorescence spectroscopy (XRF) and visible near infrared spectroscopy (visNIR) has been widely used in geological exploration. The outer product analysis (OPA) has a good effect in the fusion. The dimension of the spectral matrix obtained by OPA is large, and the Competitive Adaptive Reweighted Sampling (CARS) cannot cover the whole spectrum. As a result, the selected variables by the method are inconsistent each time. In this paper, a new feature variable screening method is proposed, which uses the Least Angle Regression (LAR) to select the high dimensional spectral matrix first, and then uses CARS to complete the secondary selection of the spectral matrix, forming the LAR-CARS algorithm. The purpose is to make the sampling method cover all the spectral data. XRF and visNIR tests were carried out on three cores in two boreholes, and a cross-validation set, validation set and a test set were established by combining the results of wavelength dispersion X-ray fluorescence spectrometer and ITRAX Core scanner in the laboratory. The quantitative model was established with the Extreme Gradient Boosting (XGBoost) and LAR-CARS was compared to these other algorithms (LAR, Successive Projections Algorithm, Monte Carlo uninformative variables elimination and CARS). The results showed that the RMSEP values of the models established by the LAR-CARS for six rock-forming elements (Si, Al, K, Ca, Fe, Ti) were relatively small, and the RPD ranges from 1.424 to 2.514. All these results show that the high-dimensional matrix formed by XRF and visNIR integration combined with LAR-CARS can be used for quantitative analysis of rock forming elements in in-situ coal seam cores, and the analysis results can be used as the basis for judging lithology. The research will provide necessary technical support for digital mine construction.
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