A linear model for surface mining haul truck allocation incorporating shovel idle probabilities

2013 ◽  
Vol 231 (3) ◽  
pp. 770-778 ◽  
Author(s):  
Chung H. Ta ◽  
Armann Ingolfsson ◽  
John Doucette
2018 ◽  
Vol 71 ◽  
pp. 00003
Author(s):  
Michał Patyk ◽  
Przemysław Bodziony

Vehicle transport is a dominant type of technological processes in rock mines, and its profitability is strictly dependent on overall cost of exploitation. Rational design of mining transportation system based on haul trucks should result from thorough analysis of technical and economic issues, including both cost of purchase and its further exploitation, having a crucial impact on the cost of minerals extraction. Moreover, haul trucks should be selected with type of payload. In this paper a development of universal family of evaluation criteria as well as application of evaluation method for haul truck and processing system selection process for a specific exploitation conditions in surface mining have been carried out. This methodology presented in the paper is based on the principles of multicriteria optimization using one of method, i.e. APEKS. The result of the research is a universal methodology, and it consequently may be applied in other surface mines with similar exploitation parameters.


2021 ◽  
Author(s):  
Ali Soofastaei ◽  
Milad Fouladgar

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks’ fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks’ fuel consumption reduction between 9 and 12%.


2014 ◽  
Vol 65 ◽  
pp. 106-117 ◽  
Author(s):  
Meng Zhang ◽  
Vladislav Kecojevic ◽  
Dragan Komljenovic

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