Drillers mining parameter The ML model would focus on automating some if not most of the driller's functions performed to achieve timely drill bit composition. Functions - the abovementioned function is the manual setting of the following mining parameter. Gravel -is the material extracted from the sea flow and pumped up onto the vessel for further processing. Mining airflow: we need to take note that if the air pressure is too low, it will not be able to lift the gravel. Similarly, it's too high it will result in an overflow of gravel. one needs to achieve a correct range else the incorrect values will cause blockages. air pressure The ideal air pressure should be within 7 to 10 bars, and it is mostly prepared at 8 bars. airflow The airflow ranges from 200 - 2600, but the prepared air flow is at 2200 (need to reconfirm these...
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
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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