HomeIncluding process decisions in the planning optimization of a mining company
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Continuing our series of posts explaining the many benefits of using Advanced Analytics for the planning of a mining company, today we will talk about how we can take advantage of different process configurations to optimize profitability. If you missed any of our previous posts, we have already detailed how this kind of decision-support tool can be used to minimize the sales penalties of iron ore deliveries, to aid the selection of more advantageous contracts in a mid-term optimization, and to better anticipate demand peaks and maintenance stops.
The ROM characteristics are still the main driver in the production indicators like product quality and production pace. However, the impact of some machines’ setup can sometimes be unneglectable at all; even more when they are optimized for a certain type of ROM.
All processes are composed of a group of machines in the beneficiation plant. Such machines operate using a predefined configuration, for instance, a crushing velocity in the crusher or the consumption of a specific reagent in froth flotation. Most companies are keen to maintain a steady process and try their best to keep it as stable as possible. However, what could happen if new setups were available to a process? Would the model always choose the same setup? Typically, these adjustments are not controlled in real time, but could be made once per shift.
Considering the same example from the previous posts, we have a generic iron mine that generates three types of iron ores sub-products, named Nonmagnetic (NMAG), Magnetic (MAG), and Concentrate (CON). They can be sold separately or blended in different proportions to meet different quality specifications. The NMAG sub-product is higher quality than the MAG, but presents smaller productivity.
We can test with our model the addition of an alternative production mode for a certain ROM (A). This alternative mode reduces total production, but increases the production of a higher quality sub-product (MAG), as shown in Figure 1. Therefore, this mode is less productive, but yields in a greater proportion of MAG, as we can see in the figure below. Could this production mode be attractive?
The results demonstrate that for certain periods, such as weeks 3, 8, 9, and 12, it can be beneficial to use the alternative production mode during some shifts, as shown in Figure 2. This can be explained by the better use of the low-quality product in stock (blended with the greater production of high-quality product). Additionally, the model was able to deliver more products to the HQ client, which increased the profit by 0.22%.
Such alternative operative modes could be investigated at each step of the process, multiplying the economic benefits of this optimization. It is possible to turn process variabilities into a profit opportunity!
Co-authors: Fabio Silva - Senior Manager at Cassotis Consulting
Emmanuel Marchal - Managing Partner at Cassotis Consulting