When a processing plant is already achieving extremely high throughput and world-class availability, how can improvements be made without significant capital spending? A globally diversified mining company was interested in understanding what further optimisation was available in a large coal processing plant, without spending additional capital. The site in focus already was already achieving world-class availability and throughput. The challenge was to find value that was not discoverable through traditional business improvement approaches.
Combining the capability of Galaxy Engine™, a hyper-variable analytical platform that is capable of processing millions of data points across discrete operating scenarios simultaneously at great speed, with subject matter expertise and site operating context, a ‘Yield Optimisation Tool’ was developed to help the operator to achieve a target yield outcome by providing them with the best set-points for a selected coal feed.
The ‘Real-Time Optimising Platform’ was an ensemble of machine learning algorithms that can automatically determine the optimal set-points for the plant based on live data from coal coming into the plant.
It was demonstrated that more than 2% annualised increase in yield was possible by optimising the set-point parameters of the circuit based on incoming coal feed. This improvement was worth more than $74M on an annualised basis with little or no additional capital expenditure.