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Intelligent Scheduling for Underground Mobile Mining Equipment.

Song Z, Schunnesson H, Rinne M, Sturgul J - PLoS ONE (2015)

Bottom Line: This investigation first introduces the motivation, the technical background, and then the objective of the study.A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue.The method and related algorithms which are used in this instrument are presented and discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Environmental Engineering, School of Engineering, Aalto University, Espoo, Finland.

ABSTRACT
Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

No MeSH data available.


Scheduling result of two machine sets.
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pone.0131003.g011: Scheduling result of two machine sets.

Mentions: The machine set means one set of single mobile mining machines which operate in sequence, such as one scaler, one shotcreter, one bolter, one blast-hole driller, and one explosive charger. The serial operation of a single mobile machine is different from the cyclic operation of loading-hauling-dumping (loader-truck) which can have several machines at one face; therefore, the mucking cycle cannot be included in this algorithm. The components of machine set may vary in different underground mines. In large and medium mines, there are often several machine sets, which are used to improve the excavation/production rate and be backup for each other. The machine set algorithm is briefly given in Fig 9. This algorithm is used to assign different machine sets to different mining areas if there are more than one set. The objective of this algorithm is to assign the machine sets to obtain the minimum overall timespan. This algorithm first clusters faces into different mining areas, according to the distances between faces. The number of clusters is determined by the number of machine sets. Then, it calculates the timespan of each mining area respectively, and moves one face from the mining area with the longest timespan to the mining area with the shortest timespan, until the new overall timespan gets longer than the previous one. When calculating the timespan, this algorithm will first invoke on the function of the grouping algorithm to group the faces within different mining areas, and then invoke on the function of the sequencing algorithm to obtain the minimum timespan for that mining area. Figs 10 and 11 respectively demonstrate the clustering and scheduling results in the case of two machine sets, based on the assumed case in Fig 8.


Intelligent Scheduling for Underground Mobile Mining Equipment.

Song Z, Schunnesson H, Rinne M, Sturgul J - PLoS ONE (2015)

Scheduling result of two machine sets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476722&req=5

pone.0131003.g011: Scheduling result of two machine sets.
Mentions: The machine set means one set of single mobile mining machines which operate in sequence, such as one scaler, one shotcreter, one bolter, one blast-hole driller, and one explosive charger. The serial operation of a single mobile machine is different from the cyclic operation of loading-hauling-dumping (loader-truck) which can have several machines at one face; therefore, the mucking cycle cannot be included in this algorithm. The components of machine set may vary in different underground mines. In large and medium mines, there are often several machine sets, which are used to improve the excavation/production rate and be backup for each other. The machine set algorithm is briefly given in Fig 9. This algorithm is used to assign different machine sets to different mining areas if there are more than one set. The objective of this algorithm is to assign the machine sets to obtain the minimum overall timespan. This algorithm first clusters faces into different mining areas, according to the distances between faces. The number of clusters is determined by the number of machine sets. Then, it calculates the timespan of each mining area respectively, and moves one face from the mining area with the longest timespan to the mining area with the shortest timespan, until the new overall timespan gets longer than the previous one. When calculating the timespan, this algorithm will first invoke on the function of the grouping algorithm to group the faces within different mining areas, and then invoke on the function of the sequencing algorithm to obtain the minimum timespan for that mining area. Figs 10 and 11 respectively demonstrate the clustering and scheduling results in the case of two machine sets, based on the assumed case in Fig 8.

Bottom Line: This investigation first introduces the motivation, the technical background, and then the objective of the study.A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue.The method and related algorithms which are used in this instrument are presented and discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Environmental Engineering, School of Engineering, Aalto University, Espoo, Finland.

ABSTRACT
Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

No MeSH data available.