How can companies connect disparate data silos?
With automation initiatives well on their way, some major mining companies have embarked on the next step in the transformation of mining. “The intelligent mine” is a vision for an operation that can leverage new artificial intelligence technologies and machine learning algorithms that sift through all its automation and systems data to find patterns that identify opportunities for optimization, efficiencies, preventive maintenance, problem solving, safer and more environmentally sustainable operations, and more informed and strategic business decisions. It is a mine in which data from one piece of automated equipment or process is seamlessly analyzed against data from other automated systems upstream and downstream. In other words, it is a highly integrated and automated mine from pit to port and sensor to boardroom that is constantly learning from itself, adapting and improving.
The path to the intelligent mine has not yet been paved and companies are only just beginning to figure out what it will look like, but there is some consensus. It will require data integration and breaking down the silos that have arisen from the rollout of a series of discrete technologies, which made each roll out manageable on its own, but does not establish a clear path to higher level integration. And all agree it is a path best travelled one step at a time with good partners.
Automation has largely been introduced into mining operations one area at a time, whether it is haul trucks and drills with proprietary onboard systems at the mine site by various manufacturers or instrumentation and automated equipment at the processing plant. “The industry has a good degree of automation, but it’s in silos,” said Fabio Mielli, market development manager for mining, cement and heavy industry at Rockwell Automation. “They have a lot of pockets of automation in such things as their fleet, material handling processing, rail systems and shipments but you don’t have the connection between the data. Many are still using Excel or other such tools to move information from one side to the other.”
Others still have multiple legacy software applications and databases from the days when these were created as standalones. “It’s a major challenge when you have many different software systems,” said Mielli. “Collecting something from an automation system, even an older one, is still easier than when you have applications from different software from all kinds of vendors, maybe 15 or 20 years old. Then you have a major challenge because you have to simplify the infrastructure in order to collect the data.”
This is an issue Anglo American has had to tackle in its plans to move toward full integration and intelligence. “We need to make sure that we have enough reliable sensors to be able to capture relevant data and integrate it with other legacy data systems. This part is the most challenging, because many of these systems were built separately, over time, and have different access methods,” said Arun Narayanan, Anglo American’s group head of data analytics. “The integration challenge is at the heart of our data analytics program. It makes sense to look at it iteratively to best determine how we can update and sustain the reliability of these data systems over time.”
Anglo American is prepared to face the challenges to achieve integration because it has already experienced success with its data analytics program. For example, it has used advanced analytics in its marketing division to help change the way it was responding to certain commodity markets, said Narayanan. “Similarly, we’ve had measurable successes in discovery and geosciences, in terms of better understanding our ore bodies and predicting behaviour through our processing plants,” he said. “We will be using this in a live production environment onsite by mid-2019, and have other projects that implement predictive maintenance workflows.
