The advent of digital twins in surface mining: Its time has finally arrived

Hazrathosseini, Arman; Afrapoli, Ali Moradi

doi:10.1016/j.resourpol.2022.103155

Cited by 26 publications

(10 citation statements)

References 91 publications

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“…The minerals industry is putting a large emphasis on sustainable operation in response to the rising concern about its contribution towards climate change (Ali et al 2017; Stothard et al 2019; El Bazi et al 2023), and the ongoing labour shortage among younger generations at remote locations. The concept of having DTs that can control and monitor physical entities in real-time has been explored in the literature (Temkin et al 2021; El Bazi et al 2022; Hazrathosseini and Moradi 2023; Fu et al 2023) which involves establishing bi-directional data flows through the integration of sensors, PLC, databases, and front-end HMIs for human operators to visualise as well as interact with the DT (Figure 22). The use of cloud computing in DT allows remote data access by multiple users across an organisation to maximise flexibility and redundancy in mining applications which are subjected to harsh environmental factors.…”

Section: Discussion and Future Considerationsmentioning

confidence: 99%

Digital twins in the minerals industry – a comprehensive review

Qu,

Kizil,

Yahyaei

et al. 2023

Mining Technology: Transactions of the Institutions of Mining a

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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.

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Section: Discussion and Future Considerationsmentioning

confidence: 99%

Digital twins in the minerals industry – a comprehensive review

Qu,

Kizil,

Yahyaei

et al. 2023

Mining Technology: Transactions of the Institutions of Mining a

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“…The scenarios of technological advances will be dizzying in the coming decades, changing many current concepts and paradigms by the year 2023, for which it will be necessary to be professionally prepared and thus understand and be part of the era of Industry 4.0 or perhaps of a future Industry 5.0 paradigm [ 112 ].…”

Section: Discussionmentioning

confidence: 99%

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Cacciuttolo,

Guzmán,

Catriñir

et al. 2023

Sensors

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Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

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“…Typically, the structure of traditional optimisation methods is restricted by the requirement to rerun the model if there is any change made to the mining dynamics (De Carvalho and Dimitrakopoulos, 2021). Moreover, a large number of models developed so far are both offline and non-intelligent, while Mining 4.0 demands dynamicity and autonomy at all operational levels (Hazrathosseini and Moradi Afrapoli, 2023b). Machine learning as an imperative pillar of this revolution has shown impressive results in many domains and was predicted in 2017 to need 2 to 5 years to reach the plateau of productivity (Gartner Inc., 2017), and in 2023 that plateau seems to have been reached judging by the unprecedented prosperity received by machine learning techniques, particularly in chatbots and image/video/voice generators.…”

Section: Definition Of Conventional Fmsmentioning

confidence: 99%

Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems

Hazrathosseini,

Moradi Afrapoli

2024

Mining Technology: Transactions of the Institutions of Mining a

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The mathematical methods developed so far for addressing truck dispatching problems in fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and dynamicity demanded by Mining 4.0, having led to the popularity of reinforcement learning (RL) methods capable of capturing real-time operational changes. Nonetheless, this nascent field feels the absence of a comprehensive study to elicit the shortfalls of previous studies in favour of more mature future works. To fill the gap, the present study attempts to critically review previously published articles in RL-based mine FMSs through both developing a five-feature-class scale embedded with 29 widely used dispatching features and an insightful review of basics and trends in RL. Results show that 60% of those features were neglected in previous works and that the underlying algorithms have many potentials for improvement. This study also laid out future research directions, pertinent challenges and possible solutions.

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The advent of digital twins in surface mining: Its time has finally arrived

Cited by 26 publications

References 91 publications

Digital twins in the minerals industry – a comprehensive review

Digital twins in the minerals industry – a comprehensive review

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems

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