Project objective

 


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  



Comments

Post a Comment

Popular posts from this blog

3D Geological Data Model, NON -Euclidean Data, Geological Deep Learning, and Graph Convolutional Networks

Image
After waiting some time, I received the 3D data for the panel that was mined on the days I selected to train the model against. I intend to train two models, one with geological 3d data and the other with drill bit data. The plan is that they will work together to achieve the desired outcome or to find an algorithm that will consider both data types and produce the binary classification with a distance in meters of how much to pick up the drill bit by. This will ultimately save time because you will not need to pull up the bit to the default 8 meters knowing the geology or lithology of the sea floor. Research into GCN (Graph Convolutional Networks ) I have also found this video that speaks about GCN. The content encourages using dynamic graph CNN for learning on point clouds, in our case the .xyz file. it also advised considering the points in a point cloud data as nodes in a directed graph. these models help learn from non- euclidean data like graphs and 3d objects using a different ...
Read more

Geological impact on the Mining Rate

 From geological point of view, mining rates will be most affected by the following   Gravel volumes : the thicker the gravel volumes, the longer it takes for the holes to deplete. Thicker volumes also cause surging which prolongs presentation time. Gravel texture: the coarser the material, the lower the RPMs, then the longer it will take for the hole to deplete. Homogeneous geology improves mining rates Slab frequency:  the higher the frequency, the lower the RPMs, then the longer it will take for the hole to deplete. Lithology type: courser lithologies like large cobbles slows mining rate, while softer lithologies Hard or rough terrains such as sandstone, conglomerate s outcrops or cappings make it difficult for the tool to penetrate and thus slow the mining rate. It also affects slewing speed of the boom in terms of crawler mining systems. Steep slopes: require slow rotations of the tools and thus slow m...
Read more

Drill visualization

Image
I manage to install the drill visualisation software and received a bit of training on how to navigate it. The ultimate objective of the drill visualisation is to use the prediction data from the flask API to visualise the drill up and down composition in replay mode. this is purely experimental and might not yield the expected result since we haven't used 3d data this far when building the model.
Read more