Optimization of Trackless Equipment Scheduling in Underground Mines Using Genetic Algorithms

Tenorio, Victor; Li, Guoqing; Hou, Jie; Hu, Nailian

doi:10.1007/s42461-020-00285-8

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…Artificial intelligence and machine learning play an important role in the construction of smart mines [10]. Much artificial intelligence and machine learning algorithms, such as deep neural networks, recurrent neural networks, and other deep learning algorithms [11,12], and heuristic algorithms such as genetic algorithms and self-encoding networks [13,14] have been applied to mine to solve practical engineering problems [15], and also played an important role in the intelligent construction of mine ventilation system. At present, these intelligent algorithms have been used to solve the key problems of mine ventilation resistance coefficient prediction and inversion [16,17], optimal adjustment of ventilation network [18,19], intelligent control of ventilation system [20], and rapid prediction of mine gas explosion and mine fire disaster parameters [21,22].…”

Section: Plos Onementioning

confidence: 99%

Intelligent diagnosis of resistance variant multiple fault locations of mine ventilation system based on ML-KNN

Wang

Liu²,

Deng³

et al. 2022

PLoS ONE

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The resistance variant faults (RVFs) observed in the mine ventilation system can utterly restrict mine safety production. Herein, a machine learning model, which is based on multi-label k-nearest neighbor (ML-KNN), is proposed to solve the problem of the rapid and accurate diagnosis of the RVFs that occur at multiple locations within the mine ventilation system. The air volume that passes through all the branches of the ventilation network, including the residual branches, was used as the diagnostic model input after the occurrence of multiple faults, whereas the label vector of the fault locations was used as the model’s output. In total, seven evaluation indicators and 1800 groups of randomly simulated faults at the typical locations in a production mine with 153 nodes and 223 branches were considered to evaluate the feasibility of the proposed model to solve for multiple fault locations diagnostic and verify the model’s generalization ability. After ten-fold cross-validation of the training sets containing 1600 groups of fault instances, the diagnostic accuracy of the model tested with the air volume of all 223 branches and the 71 residual branches’ air volume as input was 73.6% and 72.3%, respectively. On the other hand, To further evaluate the diagnostic performance of the model, 200 groups of the multiple fault instances that were not included in the training were tested. The accuracy of the fault location diagnosis was 76.5% and 73.5%, and the diagnostic time was 9.9s and 12.16s for the multiple faults instances with all 223 branches’ air volume and the 71 residual branches’ air volume as observation characteristics, respectively. The data show that the machine learning model based on ML-KNN shows good performance in the problem of resistance variant multiple fault locations diagnoses of the mine ventilation system, the multiple fault locations diagnoses can be carried out with all the branches’ air volume or the residual branches’ air volume as the input of the model, the diagnostic average accuracy is higher than 70%, and the average diagnosis time is less than one minute. Hence, the proposed model’s diagnostic accuracy and speed can meet the engineering requirements for the diagnosis of multiple fault locations for a real ventilation system in the field, and this model can effectively replace personnel to discover ventilation system failures, and also lays a good foundation for the construction of intelligent ventilation systems.

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Section: Plos Onementioning

confidence: 99%

Intelligent diagnosis of resistance variant multiple fault locations of mine ventilation system based on ML-KNN

Wang

Liu²,

Deng³

et al. 2022

PLoS ONE

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“…The objective function of the optimization model was to maximize the total material moved in a shift. Wang et al (2020) recently used a Genetic Algorithms Model (GAM) to solve the truck dispatching problem for an underground mine in China. In addition, their model had the objective to maximize production at the shift level (tonnes moved per shift) as it is subject to constraints on the number of loading locations available, the capacity of the trucks, the material quantities available in each level, and the distance between loading levels.…”

Section: Previous Studiesmentioning

confidence: 99%

“…t + g_tk --g_tk + = G_tk [10] The deviation from the goal for the number of shovels assigned is based on the total shovels assigned (s).…”

Section: ∑ Bj Xij ≤ C For Each Imentioning

confidence: 99%

“…The key importance here is the parameter cij, which varies for each level, depending on the distance travelled and slope at which a truck is required to travel, both empty and full. Equations [10] and [11] define the goal variables for trucks and shovels. Equation [12] defines the goal variables for gold.…”

Section: Constraints On Decision Variables Xij ≥ 0 and Integermentioning

confidence: 99%

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Untitled

2022

ESJ

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The cost of transporting mined materials in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine's total operating costs. The problem of dispatching trucks in an underground gold mine is of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine were addressed in this paper. The problem must address multiple conflicting objectives, and therefore, a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results also illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model.

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“…ACO methods often have difficulty identifying the optimal solution and need many iterations to converge. Examples of alternative methods that can be used are neural networks [99,100], simulation annealing [101], genetic algorithms [102] and evolutionary algorithms [103].…”

mentioning

confidence: 99%

The Smallest “Miner” of the Animal Kingdom and Its Importance for Raw Materials Exploitation

Xiroudakis,

Saratsis,

Manoutsoglou

2024

Mining

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The mining industry is the leading supplier of raw materials in modern society. This sector of human activity has experienced a severe crisis due to the energy transition and has been revived in recent years due to the need for critical metals that are essential in the post-coal era. In underground and open pit mining, processes such as extraction, transportation, safety, underground ventilation, waste management, and rehabilitation are of major importance, and their “design” is critical to the economic survival of the mine. All the above processes required to operate a mine are strongly reminiscent of an example of nature’s workman: the ant. The sympatric insect uses the same processes as the ones aforementioned during the creation of its nest. The ants dig to “extract material from the ground”, and they transport this material from the nest‘s site to the waste deposition location. The ants ensure the safety of the underground opening and the proper ventilation needed for them to live there for a long time. This article attempts to identify the relations between all the above processes and sub-processes, and how human mining and ant colony development correlate with each other. Furthermore, we examine how an ant colony has aided in the development of mining technology, and what more humans can learn and adopt from a “miner” that is 66 million years old, in order to improve their processes.

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Optimization of Trackless Equipment Scheduling in Underground Mines Using Genetic Algorithms

Cited by 17 publications

References 14 publications

Intelligent diagnosis of resistance variant multiple fault locations of mine ventilation system based on ML-KNN

Intelligent diagnosis of resistance variant multiple fault locations of mine ventilation system based on ML-KNN

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The Smallest “Miner” of the Animal Kingdom and Its Importance for Raw Materials Exploitation

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