Title: Development of a Multi-layer Perceptron Artificial Neural Network Model to
Determine Haul Trucks Energy Consumption
Organisation: The University of Queensland
Duration: From 10 – December – 2012 To 07 – June – 2013
Diesel fuel is a significant source of energy in surface mining operations. Haul trucks are the primary users of this energy resource. Based on the analysis of the data collected from mine sites, Gross Vehicle Weight (GVW), Truck Speed (S) and Total Resistance (TR) were identified to be the most influential parameters affecting the fuel consumption. The relationship between the three abovementioned parameters and the truck fuel consumption is complicated. Thus, the development of a new approach using an artificial intelligence method was essential to create a reliable model for solving this problem.
Outcomes and Benefits:
The results of this study indicate that the ANN modelling accurately predicts the truck fuel consumption.
It that Truck Speed (S) has the most parameter with the relative importance of 60%.
The sensitivity analysis showed that all the three input variables (GVW, S and TR) have an effect on the truck fuel consumption.
It that the configuration of 3 input variables, 15 hidden cells and output for the synthesised ANN model provided excellent results.
An Artificial Neural Network (ANN) model was developed to predict the fuel consumption of haul trucks in surface mines.