Industrial applications of digital twin technology in the mining sector: An overview
Bibliographic record
Abstract
In recent years, digital twin (DT) technology has received much interest from the mining community. Despite the potential to revolutionize mining processes in the long-term, this technology is still nascent, and many companies are unsure how to leverage it for their industry-specific applications. For successful integration in the field and to meet user expectations, the roles, capabilities, and potential applications of this evolving technology need to be demystified. This paper provides an overview of industrial applications of DTs in the mining sector. To identify relevant industry examples, we performed a systematic search of electronic resources. Thirty-two real-life examples of early adopters in the field were analyzed based on the application purpose and classified into eight categories: (1) collaborative decision-making; (2) data analysis and visualization; (3) design; (4) management and coordination; (5) monitoring and maintenance; (6) operational efficiency and production; (7) optimization of the mine value chain; and (8) safety and reliability. It was found that application categories that provided immediate and profitable benefits for companies contained the most case studies. To the best of our knowledge, this is the first attempt to showcase the opportunities that DTs hold for the mining sector. This summary of state-of-the-art industry applications is the first step to demonstrate the opportunities of DTs for mining operations and support the development of this emerging field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".