Research progress and prospects of intelligent technology in underground mining of hard rock mines

Liu, Xiaobo; Zhang, Xingfan; Wang, Liancheng; Qu, Fuming; Shao, Anlin; Zhao, Lingyu; Wang, Huaiyuan; Yue, Xingtong; Li, Yizhuo; Yan, Wenzhen; He, Jiang

doi:10.1016/j.gsme.2024.03.007

Cited by 12 publications

(2 citation statements)

References 99 publications

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“…Interpretation of MWD features can benefit considerably from recent advances in explainable artificial intelligence, as discussed among other by Saranya and Subshini [71] and Lundberg and Lee [72]. Moreover, although having emerged only recently, deep learning models have significant potential in the analysis and interpretation of measurewhile-drilling (MWD) data in hard-rock mining [73,74]. These models are particularly well suited to deal with complex data, such as associated with panoramic borehole imaging, for example [73,75,76], or vibrational data [77].…”

Section: Future Developmentsmentioning

confidence: 99%

“…Moreover, although having emerged only recently, deep learning models have significant potential in the analysis and interpretation of measurewhile-drilling (MWD) data in hard-rock mining [73,74]. These models are particularly well suited to deal with complex data, such as associated with panoramic borehole imaging, for example [73,75,76], or vibrational data [77]. In addition, they could also better support the processing of MWD metrics when considered as time series data [78].…”

Section: Future Developmentsmentioning

confidence: 99%

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A Review of Orebody Knowledge Enhancement Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data

Goldstein,

Aldrich,

O’Connor

2024

MAKE

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Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock.

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Section: Future Developmentsmentioning

confidence: 99%

Section: Future Developmentsmentioning

confidence: 99%

A Review of Orebody Knowledge Enhancement Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data

Goldstein,

Aldrich,

O’Connor

2024

MAKE

View full text Add to dashboard Cite

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In situ loading of a pore network model for quantitative characterization and visualization of gas seepage in coal rocks

Jiao,

Chen,

Zhang

et al. 2024

Deep Underground Science and Engineering

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The flow characteristics of coalbed methane (CBM) are influenced by the coal rock fracture network, which serves as the primary gas transport channel. This has a significant effect on the permeability performance of coal reservoirs. In any case, the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM. In this study, coal samples were subjected to an in situ loading scanning test in order to create a pore network model (PNM) and determine the pore and fracture dynamic evolution law of the samples in the loading path. On this basis, the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed. The findings demonstrate that the coal samples' internal porosity increases by 2.039% under uniaxial loading, the average throat pore radius increases by 205.5 to 36.1 μm, and the loading has an impact on the distribution and morphology of the pores in the coal rock. The PNM was loaded into the finite element program COMSOL for seepage modeling, and the M3 stage showed isolated pore connectivity to produce microscopic fissures, which could serve as seepage channels. In order to confirm the viability of the PNM and COMSOL docking technology, the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined. The absolute permeability of the coal samples was also obtained in order for comparison with the measured results. The macroscopic CBM flow mechanism in complex low‐permeability coal rocks can be revealed through three‐dimensional reconstruction of the microscopic fracture structure and seepage simulation. This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.

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Sustainability and South Africa’s mature mines: The innovation imperative

Cawood,

Fenn,

Grobler

et al. 2024

Green and Smart Mining Engineering

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Research progress and prospects of intelligent technology in underground mining of hard rock mines

Cited by 12 publications

References 99 publications

A Review of Orebody Knowledge Enhancement Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data

A Review of Orebody Knowledge Enhancement Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data

In situ loading of a pore network model for quantitative characterization and visualization of gas seepage in coal rocks

Sustainability and South Africa’s mature mines: The innovation imperative

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