{"id":19949,"date":"2024-04-07T14:47:49","date_gmt":"2024-04-07T14:47:49","guid":{"rendered":"https:\/\/www.writemyessays.app\/blog\/questions\/forecasting-mining-capital-cost-for-open-pit-mining-projects-based-on-artificial-neural-network-approach-australian-study\/"},"modified":"2024-04-07T14:47:49","modified_gmt":"2024-04-07T14:47:49","slug":"forecasting-mining-capital-cost-for-open-pit-mining-projects-based-on-artificial-neural-network-approach-australian-study","status":"publish","type":"questions","link":"https:\/\/www.writemyessays.app\/blog\/questions\/forecasting-mining-capital-cost-for-open-pit-mining-projects-based-on-artificial-neural-network-approach-australian-study\/","title":{"rendered":"Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach: Australian study"},"content":{"rendered":"

Project Description<\/span><\/p>\n

Open-pit mining is known as a huge
\nindustry in Australia. Fuel consumption cost of open-pit<\/span><\/p>\n

mine trucks accounts for a
\nsignificant of the total cost in Australia. However, research on the<\/span><\/p>\n

fuel consumption of mine trucks has
\nbeen hindered by low monitoring accuracy and unclear<\/span><\/p>\n

fuel consumption patterns according o
\nthe fuel consumption per cycle of mine trucks, this<\/span><\/p>\n

project will analyze the fuel
\nconsumption of transportation cycles with different types of mine<\/span><\/p>\n

trucks. To achieve this aim,
\nregression analysis is applied to the patterns of fuel<\/span><\/p>\n

consumption, which are caused by
\nmulti-dimensional features. Then, based on<\/span><\/p>\n

multi-dimensional features and the
\nXGBoost algorithm, a prediction model for the fuel<\/span><\/p>\n

consumption of mine trucks is
\nproposed. To evaluate the proposed prediction model, the<\/span><\/p>\n

R-squared and mean absolute percent
\nerror index are used. This project is an application of<\/span><\/p>\n

machine learning algorithms into
\nopen-pit mining case study in Australia. However, the<\/span><\/p>\n

project could benefit from the other
\nprediction models.<\/span><\/p>\n

Keywords- Open-pit Mining, machine
\nlearning, XGBoost, Truck fuel consumption,<\/span><\/p>\n

R-squared, Fuel consumption.<\/span><\/p>\n

Requirement<\/span><\/p>\n

\u2022<\/span> Ability of programing with MATLAB or
\nPython<\/span><\/p>\n

\u2022<\/span> Knowledge of machine learning algorithm<\/span><\/p>\n


<\/span><\/p>\n

This is second Group assignment, Assignment 1 can be found in attachment. <\/span><\/p>\n

My task is <\/span>Difficulties<\/span>, risks identified and strategies <\/span>(6 marks<\/span>)<\/span><\/p>\n