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...
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 ...
To improve the current mining rate I chose to train the model with data from the best performing days. I choose to looked at data from three vessels and see how the binary classification result look. below is the raw data from the on of the vessel. Here I am doing a comparison of the budget vs the target. Vessel 1 Vessel 2 Vessel 3 The objective of this exercise is to look at what the budget vs actual values are and see if we can get the model to perform similar or better and hopefully stablelize the performance of the mining rate. meeting one of our initial goals which are : Overall objectives: • Maximising throughput • Process stability • Increased mining rate stability • Decreasing equipment wear • Improving energy efficiency • Increase delivery on targets
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