Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption |
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| Affiliation: | 1. School of Mechanical and Mining Engineering, CRC Mining, The University of Queensland, QLD 4072, Australia;2. Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz 71946-84471, Iran;1. School of Mines, China University of Mining & Technology, Xuzhou 221116, China;2. State Key Laboratory of Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China;1. College of Mineral Engineering, Xi’an University of Science and Technology, Xi’an 710054, China;2. Key Laboratory of Western Mine Exploitation and Hazard Prevention Ministry of Education, Xi’an 710054, China;3. State Key Laboratory of Deep Coal Mining & Environment Protection, Huainan 710054, China;1. Syncrude Canada Ltd. – R&D, 9421 17 Ave. NW, Edmonton, AB T6N 1H4, Canada;2. School of Business, University of Alberta, Edmonton, AB, Canada;3. Dept. of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada;1. MME, Queen''s University, K7L3N6, Canada;2. Mining Engineering, Queen''s University, K7L3N6, Canada |
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| Abstract: | The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study. |
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| Keywords: | Fuel consumption Haul truck Surface mine Artificial neural network |
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