BIG DATA AND DIGITALISATION
Breaking out of silos
The fully owned Siemens subsidiary Chemtech was contracted to develop and commission a manufacturing execution system( MES) for Vale ' s sites in Brazil
Managing increasing volumes of data in tandem with digitalisation is a major challenge facing today’ s mining industry but one that needs to be overcome to increase efficiency and optimise real time mine management. Paul Moore reports
What will the mine of 2030 look like? In December 2016, a steering committee at Siemens tried to answer this question.“ One thing is certain: it will be autonomous and digitalised,” says Yun Zeng, Director of Digitalisation at Siemens Mining. First and foremost, the major challenge for the industry will be handling lower ore grades.“ In fact, that is already reality for traditional openpit mining operators today as they go deeper into the earth’ s surface to obtain ore and in some cases transition to underground mining. Efficient operations in these remote locations will increase in importance. The key is expected to lie in highly automated modular equipment to enhance cost and operating flexibility. Resource depletion will also push mining to new frontiers – to extreme mining.”
In the face of these hazardous and hard-toreach environments, digitalisation is seen as a major tool to increase cost efficiency and enable the automation of operations and processes in the mining industry. What is more, digitalisation will drive the integration of suppliers and partners along the value chain.“ That carries with it new levers to unlock potential for operational excellence and new business and operation models,” points out Zeng.
In short, digitalisation tools are expected to be integrated at all mining stages, from exploration, engineering and simulation to advanced process control.
Some of the attributes of the digital mine cited by Siemens – the whole up-front mineengineering process, from mine planning to process planning, will be completely digitalised and paperless. Strategic and real-time planning and scheduling will be aligned, and mine operations will be run fully remotely by realtime KPIs and decision-support systems. Furthermore, forecasts and quality management will be based on real-time data, which will allow for fast reactions to market volatility. Selflearning algorithms will identify optimised operation models to lower Capex and Opex – independent from employees’ skills.
“ And artificial intelligence will continue to develop, thereby giving way to self-maintaining robots and systems. Two constant companions will be the extensive use of digital twins and cyber security. Digital twins will be used for simulations of sequential scenarios, for forecasts, and for quality management and control. Furthermore, digital twins will be the go-to tool for plant optimisation, trainings and services. In regard to cyber security, prevention strategies will be an integral part of daily business operations, allowing for risk-free data exchange and operations.”
Overcoming interoperability and communications in the digital mine
GE told IM on the subject of data:“ Mining companies tell us that, at best, they are looking at 2 % of their data. With more and more data being generated, they need faster computing.” Doug Hanson, GE Digital Mine General Manager added:“ Using Digital Mine Operations Optimisation solutions, mining process operators can transform real-time machine data into actionable production efficiency metrics. These metrics can identify bottlenecks and
equipment issues, helping to avoid unplanned downtime and increase efficiency. On average, implementing Digital Mine Operations Optimisation solutions can save a minesite an average of 5-15 %. The world’ s top 40 mining firms alone had 2015 operating expenses of $ 531 billion and that if they captured a 1 % efficiency improvement they would yield $ 5.1 billion annually.”
GE argues that the application of big data and advanced analytics has the potential to unlock untapped areas of productivity and efficiency for the mining industry.“ From digital modelling and validation in the design process to active monitoring and diagnosis of electronic and mechanical systems, GE is at the forefront of the Industrial Internet. This exciting technology will effectively transform unique mining operations, improving performance, reliability, and operations while promoting safer mining practices through intelligent, real-time monitoring across the enterprise. Many industrial organisations have yet to capitalise on the promise of digital industrial transformation because one of the key drivers for change – data – is trapped in disconnected computer systems. This not only make data difficult to access, it makes the process of extracting value from data costly and ineffective.”
The Industrial Internet was created to change all of that through hyper-connected equipment and advanced data analytics. But to take full advantage of this, mining companies will need to reevaluate current tools, systems, and processes. For example, the computer productivity tools used in the mining industry today are highly focused on specific tasks. So, these tools not only underserve operators’ needs, they produce siloed data that add to communication and interoperability issues. This will only become more of a challenge during digital industrial transformation, because legacy systems simply aren’ t designed to handle a massive influx of new data from a variety of sources on their own.
“ GE solves this challenge with Predix— the platform for the Industrial Internet. Predix is fine-tuned to industry’ s exact transformation requirements, with the power and functionality to consolidate and process a wide variety of industrial data across multiple locations and over varying time schedules. This data can then be used to improve outcomes and streamline operations— from increasing operational safety and efficiency to predicting equipment failure and optimising logistics. Predix also offers the
16 International Mining | NOVEMBER 2017