Responding to new information in a mining complex: fast mechanisms using machine learning

Păduraru, Cosmin; Dimitrakopoulos, Roussos

doi:10.1080/25726668.2019.1577596

Cited by 21 publications

(8 citation statements)

References 42 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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“…The method proposed to update the short-term destination policies of materials in a multiple product mining complex uses policy gradient reinforcement learning with neural network agents and extends upon the work of Paduraru and Dimitrakopoulos (2019). The method accounts for the uncertainty in the supply of different materials and the performance of equipment.…”

Section: Updating Short-term Destination Policies In a Mining Complexmentioning

confidence: 99%

“…However, the method developed requires a predefined extraction sequence to calculate the expected a posteriori improvement in the objective function during the optimization. Paduraru and Dimitrakopoulos (2019) proposed a policy gradient reinforcement learning algorithm to optimize the neural network destination policies of materials in a mining complex while accounting for supply and equipment performance uncertainty. The neural network destination policies increased the expected NPV by 6.5% compared to the mine's cut-off grade destination policies for a copper mining complex.…”

Section: Introductionmentioning

confidence: 99%

Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex

Kumar

Dimitrakopoulos

Maulen³

2020

J Intell Manuf

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A mining complex is an integrated value chain where the materials extracted from a group of mineral deposits are sent to different processing streams to produce sellable products. A major short-term decision in a mining complex is to determine the flow of materials that first includes deciding which handling facilities to send the extracted materials and then determining how to utilize the processing facilities. The flow of materials through the mining complex is significantly dependent on the performance of and interaction between its different components. New digital technologies, including the development of advanced sensors and monitoring devices, have enabled a mining complex to acquire new information about the performance of its different components. This paper proposes a new continuous updating framework that combines policy gradient reinforcement learning and an extended ensemble Kalman filter to adapt the short-term flow of materials in a mining complex with incoming information. The framework first uses a new extended ensemble Kalman filter to update the uncertainty models of the different components of a mining complex with new incoming information. Then, the updated uncertainty models are fed to a neural network trained using a policy gradient reinforcement learning algorithm to adapt the short-term flow of materials in a mining complex. The proposed framework is applied to a copper mining complex and shows its ability to efficiently adapt the short-term flow of materials in an operational mining environment with new incoming information. The framework better meets the different production targets while improving the cumulative cash flow compared to industry standard approaches.

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“…Information mining in complex networks is important in theoretical research and offers great application and socioeconomic values [1][2][3][4]. For example, if users can unearth important nodes or edges in the spread network of a virus, then they can curb the spread of the virus in a short time by isolating or cutting off the important nodes or edges in the virus network at the beginning of the virus spread and thereby eliminate unnecessary economic losses [5]. Efficient information mining in complex networks has naturally become a key topic that continues to attract the attention of many scholars.…”

Section: Introductionmentioning

confidence: 99%

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

et al. 2022

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In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

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Responding to new information in a mining complex: fast mechanisms using machine learning

Cited by 21 publications

References 42 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

Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

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