Iron Ore Mine Revenue Improvement



Iron Ore


In June 2018 Interlate collaborated with the site team at an Australian iron ore processing plant. Using a combination of expertise and data analytics Interlate helped the plant increase ore throughput by 3%, and improve product yield by 2%.



Our client was a large Iron Ore producer in Australia that was undertaking several initiatives aimed at increasing shareholder value. Some of these initiatives were to be funded by operational cash-flow. The challenge was to improve operational revenue without a significant increase in capital or operational expenditure.

Interlate was given the opportunity to demonstrate how sophisticated data analytics, combined with technology and expertise could uncover hidden value in a processing plant. Our client agreed to provide the site data and operational context for this challenge.

Interlate’s brief was to use advanced data processing and analytical technology to identify further optimisation opportunities that were not discoverable using traditional approaches to business improvement.


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Interlate created a unified data model that integrated the mining and plant data.

Once the data from multiple sources was unified, it was possible to use an ensemble of machine learning algorithms to perform sophisticated analyses. The predictive intelligence that came from the machine learning based analytics gave the site team actionable insights, even with data that it had not previously been seen. The optimisation achievable by using this approach was calculated at approximately 3% improvement in ore throughput and an associated 2% increase in product yield.


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 analytical tools that could be used to optimise throughput and yield.

The initial phase of the diagnosis was to create a unified data model of multiple datasets across the value chain at iron ore mine. Interlate ingested a vast amount of operational data, fused these disparate and unconnected datasets into a single model so that hidden patterns could be exposed.

Interlate then used a combination of mining, processing and mathematical expertise to develop practical tools that the site operational team could use to capture improvements in value in the plant. The Galaxy Engine™ provided the processing power to crunch the data. Interlate developed bespoke machine learning models to predict outcomes.