In last two decades in mining, several researchers developed numerous articles based on different algorithms and constrains pursuing solutions on the final pit and scheduling problems. Which are the paths no one could outline yet? And which are greater restrictions and challenges to this task?
I think that we must work on improving the reliability of the models, and understand the uncertainties.
My understanding of the mine planning optimisation limitations from a process perspective is the limited data available due to the cost of drilling the orebody, obviously this is a cost issue as reducing the drill spacing adds significant cost to defining the orebody.
Two areas of improvement that I see are:
- Incorporation of data from new down hole survey technologies, potentially aids in understanding orebody variability which can play a huge part in how the orebody is modeled and managed.
- Incorporation of geometallurgy and physical processing data to improve understanding of the orebody from a treatment perspective, as well as more accurate forecasting of lump to fines, or ultra-fines content, which could influence blasting (and therefore costs) practices.
Interesting approach. I will take this into account in my researches. Thanks.
Hi, this is a good question. In this case, many ideas come to my mind. One of them is: In my opinion, if we accept clustering as an important part of the production planning process, we can do interesting research on new clustering methods, and deposit grade distribution.
I think intelligent techniques should be adapted in mine planning and design.
Hi, In mining optimization discussions, optimization models generally have many variables that cause the solution process to take much time. It may take a month or more to solve a real model. This causes industrial companies to look for non-optimal methods that provide the answer in a short time. Well, if you can focus on reducing the number of variables or ways to reduce solution time, this is a great idea.
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