Product Optimisor

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At Snowden our Mine Planning group thrives on solving complex problems quickly. Because each project is unique, we often develop our own tools to get the best bottom-line outcome for our clients.  We do this by leveraging our diverse range of skills, experiences and perspectives as mining engineers, mathematicians and software developers. This series of articles provide some examples of where we have developed innovative solutions to problems; solutions that could also help add value to your project or operation.

The Problem

Several of our recent projects required blending multiple elements simultaneously to produce a saleable product quality. The ability to maximise the amount of saleable product from the resource was of utmost importance to the project economics; after all, this sets the revenue generation capacity. This blending problem exists for many mining projects, some examples we recently worked on include:

  • An iron ore project wanting to maximise product with a minimum iron, and maximum alumina and silica targets.
  • A bauxite project seeking to maximise alumina and keep up to ten other elements within strict bounds.
  • A nickel laterite project with a pyro plant wanting to maximise nickel, but control iron and the silica/magnesium ratio.

Marginal cut-off grades, as applied in most pit optimisation packages and in Lane’s theory, are relevant to most precious and base metals projects. This approach does not work in bulk commodities where there are product quality targets outside of which the output is not saleable. In this case, the mining engineer is forced to make a decision on the cut-off grade to apply. This decision can have a large impact on the revenue generation of the mining project.

When there is only one element to consider, the task is relatively easy: Use a trial and error approach to run through a series of cut-offs of that element to achieve the desired average grade. When there are more elements to blend to achieve product specification, the problem becomes much more difficult. Do you apply the “simple” trial and error approach on the “main” element and try to juggle the other grade targets? If this doesn’t work, then perhaps you will apply a second trial and error approach to another element to bring it within tolerance. Sure, you can meet the grade constraints this way, but will this give you the most output for the combined constraints? What if there was some lower grade material that improves the blend for the other elements? What if you have different ore types with different responses? The problem gets very complex, very quickly, and given there are millions of resource blocks to consider, at this point, we are beyond the trial and error approach.

The Solution

The optimal decision on blending cut-off grades is one that maximises product for the given constraints. In seeing this problem as being material to these types of projects, Snowden decided to build a custom tool to optimise the decision. We call this our “Product Optimisor” tool. This tool enables us to maximise product while considering the dynamics of individual block grade composition and distribution, and the “tightness” of each grade target.

The case below shows a typical two element problem. To maximise the product, we want to minimise the grade of the “revenue” element above its lower limit while maximising the grade of the “penalty” element below its upper limit. The simple approach might work through a range of revenue element cut-offs and finds the one that delivers the maximum amount of product while satisfying both grade targets. This is represented by the horizontal line.


Typical two element problem
Figure 1   Typical two element problem


When we apply our Product Optimisor tool to the data set we get a sloping cut-off line. This makes sense:

  • When the penalty element is high, we need a higher revenue element grade to compensate.
  • When the penalty element is low, then we can consider a lower revenue element grade.

The tighter the penalty element grade target is, the more important it will become to the cut-off grade decision. This will be represented by a steeper optimal cut-off line.

In the example above, the optimised case resulted in over 10% more product being available for mining compared to the simple cut-off approach. The effects are significant and flow on to strip ratio, cost per tonne, revenue and profits; all key indicators in these types of projects. The approach does not require additional expensive testwork or analysis, simply a smart algorithm.

In this period of depressed commodity prices and capital availability, these types of improvements can mean the difference between a bankable and non-bankable project.

An interesting extension of this tool is the ability to consider the blending “synergies” between connected projects. Does the inclusion of a new resource mean you can get more product out of your existing project, or do they duplicate each other and reduce the overall inventory (relative to each project on their own)? This is an important consideration, given the amount of corporate activity in the market, and projects requiring more product to support their business cases.

This tool is just one of many that we have developed to solve problems that traditional software does not manage well. If you would like to get the most out of your blending project, with approaches that are custom to your project’s needs, or have your own unique problem that we might be able to assist with, please contact us at to arrange a meeting.

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