• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.453-461
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• 2018

Shaft stations of underground uranium mines in China are not only utilized as waiting space for loaded mine-cars queuing to be hoisted but also as the principal channel for fresh air taken to working places. Therefore, assessment of how mine-car queuing processes affect shaft station radon concentration was carried out. Queuing network of mine-cars has been analyzed in an underground uranium mine, located in Quzhou, Zhejiang province of Eastern China. On the basis of mathematical analysis of the queue network, a MATLAB-based quasi-random number generating program utilizing Monte-Carlo methods was worked out. Extensive simulations were then implemented via MATALB operating on a DELL PC. Thereafter, theoretical calculations and field measurements of shaft station radon concentrations for several working conditions were performed. The queuing performance measures of interest, like average queuing length and waiting time, were found to be significantly affected by the utilization rate (positively correlated). However, even with respect to the "worst case", the shaft station radon concentration was always lower than $200Bq/m^3$. The model predictions were compared with the measuring results, and a satisfactory agreement was noted. Under current working conditions, queuing-induced variations of shaft station radon concentration of the study mine are not remarkable.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).