Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models

Soofastaei, Ali, Aminossadati, Saiied Mostafa, Kizil, Mehmet Siddik and Knights, Peter (2016). Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models. In: Naj Aziz, Bob Kininmonth, Jan Nemcik, Ali Mirza, Ismet Canbulat and John Hoelle, Proceedings of the 2016 Coal Operators' Conference. 2016 Coal Operators' Conference, Wollongong, Australia, (477-489). 10-12 Feb 2016.

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Name Description MIMEType Size Downloads
Author Soofastaei, Ali
Aminossadati, Saiied Mostafa
Kizil, Mehmet Siddik
Knights, Peter
Title of paper Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models
Conference name 2016 Coal Operators' Conference
Conference location Wollongong, Australia
Conference dates 10-12 Feb 2016
Proceedings title Proceedings of the 2016 Coal Operators' Conference
Place of Publication Wollongong, Australia
Publisher The University of Wollongong Printery
Publication Year 2016
Year available 2016
Sub-type Fully published paper
Open Access Status Not Open Access
ISBN 9781741282559
Editor Naj Aziz
Bob Kininmonth
Jan Nemcik
Ali Mirza
Ismet Canbulat
John Hoelle
Start page 477
End page 489
Total pages 13
Collection year 2017
Language eng
Abstract/Summary Energy saving has become an important aspect of every business activity as it is important in terms of cost savings and greenhouse gas emission reduction. This study aims to develop a comprehensive artificial intelligence model for reducing energy consumption in the mining industry. Many parameters influence the fuel consumption of surface mining haul trucks. This includes, but not limited to, truck load, truck speed and total haul road resistance. In this study, a fitness function for the haul truck fuel consumption based on these parameters is generated using an Artificial Neural Network (ANN). This function is utilised to generate a multi-objective model based on Genetic Algorithm (GA). This model is used to estimate the optimum values of the haulage parameters to reduce fuel consumption. The developed model is generated and tested using real data collected from four large surface mines. It is found that for all four mines considered in this study, the haul truck fuel consumption can be reduced by optimising truck load, truck speed and total haul road resistance using the developed artificial intelligence model.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Open Access via link: http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2249&context=coal

Document type: Conference Paper
Collections: School of Mechanical & Mining Engineering Publications
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Created: Wed, 17 Feb 2016, 13:42:44 EST by Dr Saiied Aminossadati on behalf of School of Mechanical and Mining Engineering