Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty

Ajak, Ajak Duany; Lilford, Eric; Topal, Erkan

doi:10.1016/j.resourpol.2017.10.016

Cited by 33 publications

(11 citation statements)

References 4 publications

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“…In the areas of mine planning and evaluation, research on cost estimation was performed [43,44] to predict mine operation expenses and mine planning [45][46][47] to optimize the mine design. Reserve estimation [48,49] research was conducted that considered the reserves and economic values required for mine operations.…”

Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

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Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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“…• Uncertainty on the collection of data: In data analysis, the measuring instrument parameters of interest and methods of data collection can lead to errors or missing values in the database [29]. This is the case in our dataset with missing data [2]. • Uncertainty about the processing of data: In our case study, the database contains non-integer values and negative values; these types of values are not considered by the algorithms used.…”

Section: Advantages and Limitations Of Our Approach Compared To Othermentioning

confidence: 99%

Explainability with Association Rule Learning for Weather Forecast

2021

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The reliability of the weather forecast models is a complex issue since it depends on numerous parameters and the technical infrastructure which supports them. In doing so, there is a need for advanced works oriented towards a better understanding of these models and the analysis of main associated parameters. Our approach is to study the applicability of the extracted association rules to provide a clearer understanding of atmospheric exchanges. In this work, the proposed methodology is based on the discovery of the interesting interpretable relationships between measured meteorological parameters at the Atmospheric Research Center of Lannemezan (South-West of France). In the preprocessing step, the proposed method is considered to be effectively flexible to account for data uncertainties, unlike the majority of classical evaluation methods mainly directed towards the reduction of variables and data redundancy. In postprocessing, the advantage of our approach is that the extracted rules are a metamodeling of interpretable useful knowledge for the clarity and conciseness of its representation. Moreover, in the processing, the interpretability in data sciences is recent and still in its infancy. The generated association rules with their statistical and semantic interpretations have globally highlighted the possibilities of explicit analysis of meteorological parameters. This study showed that among the generated relevant rules, three parameters (temperature, humidity, wind speed) have a high frequency in the antecedents of the rules and that the only consequence is rain. This is useful for the identification of potential improvements and gaps in the existing models of atmospheric observations, in particular, to understand the related parameterizations to the productivity of the rain phenomenon.

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“…Operational flexibility can be built into operational planning [8] and continued improvements to geological (and hydrogeological) knowledge will help optimise planning and operations. In a dynamic model, mapping while drilling, taking advantage of logging data collected while drilling (logging-while-drilling, LWD) to update geological and geotechnical maps [20,33], will provide near-instant updates to inform the digitised geological model that in turn will inform the mine extraction and on-going planning process.…”

Section: Technological Advances That May Improve the Geological Modelmentioning

confidence: 99%

“…Interruptions to mining production and problems such as unplanned dilution and unforeseen hazards are often a result of the mine operating within 'approximately-known' geological conditions [8,9]. It is clear from this, therefore, that a mining digital twin will function better if the geological domain, and its digitised version, respectively, are closely connected.…”

Section: Introductionmentioning

confidence: 99%

Cousins, Siblings and Twins: A Review of the Geological Model’s Place in the Digital Mine

Hodgkinson

Elmouttie

2020

Resources

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Digital mining is a broad term describing the enhancement of the physical mining method through the use of digital models, simulations, analytics, controls and associated feedbacks. Mining optimisation will be improved through increased digitisation and real-time interactions via a “digital twin”, however digitisation of the rock mass component of this system remains problematic. While engineered systems can be digitally twinned, natural systems containing inherent uncertainties present challenges, especially where human-intensive procedures are required. This is further complicated, since the mining system is designed not only to interact with, but to substantially and continually alter its surrounding environment. Considering digital twin requirements and geological modelling capabilities, we assess the potential for a mine’s synchronised digital twin to encompass the complex, uncertain, geological domain within which it interacts. We find that current geological (and indeed hydro-geological) models and simulations would support digitisation that could be considered to provide, at best, a digitised ‘cousin’. Based on this assessment, the digital twin’s value for medium term forecasting of mining operations may be limited and we discuss technological advancements that can mitigate this.

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Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty

Cited by 33 publications

References 4 publications

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Explainability with Association Rule Learning for Weather Forecast

Cousins, Siblings and Twins: A Review of the Geological Model’s Place in the Digital Mine

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