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Project objective

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  Goal: from fluctuating mining rate performance to reducing its variability,  it stabilises frequent peak performance. process state improvement  Overall objectives:   • Maximising throughput  • Process stability  • Increased mining rate stability  • Decreasing equipment wear • Improving energy efficiency • Predicting failure  • Increase delivery on targets   
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Dill Visualisation Simulation

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  I did research into the possible simulation of the drill bit. This is to help ultimately in illustrating the ML model's inference on the tool. This is the email response that I got  In the interim, I will have to see how best to generate the data to replay in the Drill Visualisation System. Below is a snap shot of how the Drill visualisation system looks and feels  DVS Drill Visualisation System
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Updated Model with improved results. (data used was not correct)

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Improvements  From the last model, I noticed that I was focusing on the wrong field. the binary classification was being implemented on the last field, so I move the composition tag data to the last field and the model gave me the training results below: I am happy with the 100% Accuracy but I'm scared it might be close to overfitting, so I will perform a few more tests to see the results. Additionally, I would like to do a bit more investigation with the regression algorithm.  but with the regression model, I need geological data to see if the lithology has outcrops or is flat. Then the model can know by how many meters to pull up the drill tool bit.
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