Project Description
Open-pit mining is known as a huge
industry in Australia. Fuel consumption cost of open-pit
mine trucks accounts for a
significant of the total cost in Australia. However, research on the
fuel consumption of mine trucks has
been hindered by low monitoring accuracy and unclear
fuel consumption patterns according o
the fuel consumption per cycle of mine trucks, this
project will analyze the fuel
consumption of transportation cycles with different types of mine
trucks. To achieve this aim,
regression analysis is applied to the patterns of fuel
consumption, which are caused by
multi-dimensional features. Then, based on
multi-dimensional features and the
XGBoost algorithm, a prediction model for the fuel
consumption of mine trucks is
proposed. To evaluate the proposed prediction model, the
R-squared and mean absolute percent
error index are used. This project is an application of
machine learning algorithms into
open-pit mining case study in Australia. However, the
project could benefit from the other
prediction models.
Keywords- Open-pit Mining, machine
learning, XGBoost, Truck fuel consumption,
R-squared, Fuel consumption.
Requirement
• Ability of programing with MATLAB or
Python
• Knowledge of machine learning algorithm
This is second Group assignment, Assignment 1 can be found in attachment.
My task is Difficulties, risks identified and strategies (6 marks)
- Difficulties encountered
- Identify data risks
- Identify modeling risks
- Risk minimization strategies