Interlate recently helped an Australian metallurgical coal processing plant to achieve product yield improvements worth more than $74M on an annualised basis. Using a combination of technology, data analytics and technical expertise, this result was achieved in just a few weeks.
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.
Interlate’s brief was to use advanced data processing and analytical technology to find value that was not discoverable through traditional business improvement approaches.
Interlate used analytics and machine learning to identify productivity opportunities.
Using advanced data analysis and machine learning, Interlate identified an opportunity to improve yield materially: 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 could be sustained annually by leveraging Interlate’s real-time monitoring technology.
Interlate applied a differentiated suite of technology and analytical solutions that could be implemented quickly to materially improve productivity, with little or no additional capital expenditure.
Interlate’s Galaxy Engine™ is a hyper-variable analytical platform that is capable of processing millions of data points across discrete operating scenarios simultaneously at great speed. Interlate combined this capability with subject matter expertise and site operating context to develop some tools that could be used to optimise yield.
The ‘Yield Optimisation Tool’ is a tool that helps 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’ is 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.