Taking the next step in processing optimisation
Maximising shareholder value: this seems to be the end goal of any publicly listed company (and so it should: the shareholders are the owners after all). But what does this mean in a processing plant on a mine site? Often, the processing of an ore body is a value maximiser step — a necessary operation that needs to be as efficient as possible to minimise waste, and in turn to reduce the amount of ore required to be mined for one tonne of product. For a mine that extracts millions of tonnes of ore a year, a small improvement in processing can deliver amplified results in the pit. This means the operation can be lower on the cost curve, making the business more resilient during a downturn and freeing up more cash for further improvements and expansions.
After commodity prices peaked in 2011 and the world outlook has become increasingly uncertain, many mining operations have implemented a continuous improvement project in some form to maintain profitability levels. As we have seen in in 2019, most mining businesses have successfully completed these projects, with production costs per tonne in some operations less than half what they were during the ‘Mining Boom’. This is reflected in the value growth of the mining industry — revenue in 2018 has increased 25% over 2011 levels even though some commodity prices have fallen since then.
As these improvement projects have concluded, many mining operations have noticed it is difficult to continue optimisation initiatives. This is due to many factors, including the current skills shortage and high staff turnover. To move into the next step of process optimisation, a different toolkit is required to be used as it gets increasingly harder to squeeze the last few percent out of the existing processing capability.
Due to advances in technology over the last few years, there have been many equipment manufacturers and automation solution providers that have crafted innovative solutions to assist plant operators to extract as much performance as possible from their operation. Building upon the more established Advanced Process Control (APC) technologies including Multivariable Model Predictive Control (MPC), the integration of innovative technologies such as artificial intelligence (AI), Industrial Internet of Things (IIoT) and machine learning creates even more efficient and interconnected solutions for plant operators. Traditionally, mining processing has lagged in terms of technology adoption compared to other heavy industries such as oil and gas, which is a demonstration of how much latent optimisation capacity is still present within all mine processing operations around the world.
However, these advanced control philosophies are only as good as the data going into them. 20 years ago, most sources of data were a single variable being fed into the SCADA system from instrumentation or sensors in the field. Recent developments in digital communication protocols and self-verification software have enabled many additional variables to be sent to the control or monitoring platforms. The tremendous increase of data available for analysis has enabled leaps in potential plant availability, utilisation and yield improvement. These smart sensors are now also being able to be connected directly to a cloud platform for further analysis, reducing the complexity and cost of the overall architecture — simplifying optimisation projects even further. With MPC for example, further optimisation would be able to occur due to the increase of information available to be fed into the optimisation software, even if it was previously utilised during a prior innovation project.
The addition of process optimisation on top of the organisational change that the mining business has implemented is this next step in maximising shareholder returns. Once a mining operation has implemented a culture of continuous improvement and operational discipline, the analysis of the data that is available will lead to the knowledge of where to focus improvement efforts next. This will in turn amplify the benefits brought by machine-led optimisation initiatives. Accurate and complete data for every sub-process in the processing plant is the best way in which each individual part of the mine can be optimised individually, leading to a much more efficient process as a whole.
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