Mechanical and Mining Engineering, The University of Queensland, Australia, 2012-2015           

Thesis:  

Development of an Advanced Data Analytics Model to Improve the Energy Efficiency of Haul Trucks in Surface Mines

PhD Research Project:

Truck haulage is responsible for a majority of cost in a surface mining operation. Diesel fuel, which is costly and has a significant environmental footprint, is used as a source of energy for haul trucks in surface mines. Reducing diesel fuel consumption would lead to a reduction in haulage cost and greenhouse gas emissions. The determination of fuel consumption is complex and requires multiple parameters including the mine, fleet, truck, fuel, climate and road conditions as input. Data analytics is used to simulate the complex relationships between the input parameters affecting the truck fuel consumption. This technique is also used to optimise the input parameters to minimise the fuel consumption without losing productivity or further capital expenditure for a specific surface mining operation. The aim of this research thesis was to develop an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines. The most important controllable parameters affecting fuel consumption were first identified namely payload, truck speed and total resistance. These parameters were selected based on the results of an online survey.

A comprehensive analytical framework was developed to determine the opportunities for minimising the truck fuel consumption. The first stage of the analytical framework includes the development of an artificial neural network model to determine the relationship between truck fuel consumption and payload, truck speed and total resistance. This model is trained and tested using real data collected from some large surface mines in USA and Australia. A fitness function for the haul truck fuel consumption was successfully generated. This fitness function was then used in the second stage of the analytical framework to develop a computerised learning algorithm based on a novel multi-objective genetic algorithm. The aim of this algorithm is to estimate the optimum values of the three effective parameters to reduce the diesel fuel consumption.

The following studies were also conducted to enhance the analysis of haul truck fuel consumption. First, a comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation was carried out. Then, a discrete-event model to simulate the effect of payload variance on truck bunching, cycle time and hauled mine materials was developed. The influence of rolling resistance on haul truck fuel consumption in surface mines was investigated.