We recently spoke to Brad Allsopp, WSP’s Global Technical Executive for Processing and Materials Handling; Rafid Morshedi, Associate Data Scientist/Engineer and Simon Blake, Maritime Lead, to get a better understanding of the opportunities that digital data can bring to mine operators.
“As organisations increasingly move towards embracing Environmental, Social, and Governance (ESG) principles, WSP has taken a Future Ready approach to the challenges facing the mining sector,” explains Brad. “Put simply, we’re helping organisations maximise the value of their data.
“The challenges confronting the mining sector today are not new or unique, but they are of increasing significance.
“Mine operators share the same mindset and aspirations as businesses in other industries, particularly when it comes to achieving a profitable and sustainable competitive advantage in their market.”
The importance of big data
On a daily basis, mining organisations capture vast amounts of data. It can play a critical role in improving operational efficiencies and reducing costs – but it’s how you use that data, combine it and interrogate it that really matters.
For example, capturing and consolidating multiple data sets intelligently can help mine operators to predict market demand and optimise supply. More importantly that knowledge can be used to better manage equipment and optimise operations.
“Data loggers – devices that record and store the output of one or more sensors – yield large data sets which provide a great opportunity to unlock value, particularly in the coal chain,” explains Rafid.
“However, simply having an ability to process data on its own is insufficient. By combining a sound understanding of modern data analytics techniques with subject matter experts (that can ask the right questions), organisations can significantly increase profit margins.”
When data goes lean
Let’s take the coal chain as an example. It includes exploration; development; mining, processing, transport and logistics; waste management and disposal; and rehabilitation. All components must add value to achieve maximum return on investment. The decision-making process can be improved with data analytics across the production cycle.
“We have found that helping our clients implement lean principles in combination with advanced analytics is really powerful,” says Rafid.
“Lean techniques such as value stream mapping, non-productive time analysis, simulation modelling and benchmarking all add up to provide information about value-adding and non-value adding activities.
“Discrete event simulation models are especially useful at validating performance and identifying reasons for poor performance in existing mining production and pit-to-port logistics chains. Performance improvement strategies can then be developed, including renegotiating service contracts to better align with the mining organisation’s own KPIs.
“In addition, visual management tools can be used to standardise procedures and to communicate initiatives and results to stakeholders, encouraging collaboration within the business more broadly.”
An example of where lean techniques have delivered results is in our work on the Thomas Yard project for FMG Iron Ore. We designed their workshop around the same task occurring at the same station, and we used automation to assist with the movement of components within the facility.
“This was the first preventative semi-automated railway maintenance workshop designed in the world at that time,” explains Brad. “It maintains a fleet of more than 3000 ore cars while automating machining and maintenance of wheels and axles. The workshop has two through roads, each with two stations all capable of full car maintenance procedures.”Is all data valuable?
Just like any business, mine operators are flooded with data emanating from every facet of their operations as well as interactions with customers and their service providers.
”With the cost-effectiveness of digital storage and the ubiquity of wireless technology, data is being created and stored at enormous rates by machines, control systems and people,” explains Brad. “Every truck, light vehicle, smartphone and drill rig is fitted with a GPS, engine management system or computer. Every drone, camera and fixed plant item is fitted with digital and analogue devices that generate streams of measurements, observations and state data in a wide array of formats, with and without structure.
“Some equipment is fitted with 4G or 5G SIMs. Most control systems, management information systems, ERPs, and web interfaces collect, translate, record, display and summarise data within limited pre-determined parameters. Together, all this data can be messy, imperfect, complex, large and with low veracity. Big data is created by machines and people and can be structured like a traditional database, partially unstructured or completely unstructured, seemingly without patterns.”
Transforming data into outcomes
To make sense of all this data, mine operators often compartmentalise data by department and rely on systems to summarise and compare data in ways relevant to existing KPIs.
Rafid says, “The insights that are the most valuable arise from comparing disparate data and identifying unexpected strong correlations.”
For example, as part of an options study to upgrade or replace an existing ROM bin facility at an open cut coal mine in New South Wales, the assessment team investigated both scheduled and unscheduled downtime using available data.
Rafid explains, “The primary source of information was 12-months of hourly readings of weightometer totaliser on the downstream conveyor, with scheduled maintenance records provided for key equipment in the ROM bin facility and the downstream processing plant.
“The assessment team used Python to clean the raw data to remove spurious readings and backfill missing data, which was then cross-matched against the maintenance records to identify the performance of scheduled maintenance whilst identifying the root causes of unscheduled downtime. A statistical model was built to model tonnes between failures. This cross-matched data was depicted graphically to improve understanding of failure behaviour with a Weibull fit.
“The analysis also revealed several subtle surprises including that; the statistical model had a shape parameter less than one suggesting that the failure rate reduces with timeand that the production opportunity arising from eliminating the unplanned outages was substantial.”
What lies ahead?
While mining developers and operators have been less impacted by digital disruption than other industries, some of the world’s largest companies are ahead of the curve, embracing digital tools to achieve improvements in productivity, sustainability and profitability.
Simon concludes, “Data required to strategically achieve these goals, reduce waste, improve safety and drive towards maximum performance already exists in these organisations. Making sense of big data requires a strong combination of data analytics skills with subject matter knowledge and interpretation to arrive at the best outcomes.
“In short, data is king! It can be used to strategically achieve these goals, reduce waste, improve safety and maximise performance. The key is to capture and store the right data and consolidate it appropriately to extract value. Making sense of big data requires a combination of lean principles, data analytics skills and subject matter expert knowledge.”
