Are we taking advantage of key new technologies in mining and manufacturing?
26 April 2019
In this article, Trolex CEO Glyn Pierce-Jones takes a closer look at the use of new technology in the mining and manufacturing sectors and examines whether they are being used effectively to improve financial stability and productivity in large-scale operations.
It is becoming a cliché to say that technology is changing the face of business sectors all over the world, but that doesn’t mean it isn’t happening and the mining and manufacturing sectors are no different. While traditionally labour-intensive and reliant on heavy, static equipment, smart and connected technology is now showing potential to completely change how mining and manufacturing businesses operate.
From remote and mobile environment and equipment monitoring, to real-time data and analytics to machine learning and automation, there is seemingly no end to the benefits new mining and manufacturing technology is bringing to historically industrial sectors.
But, while the technology is there, are enough operations actually taking advantage of it?
New technology in mining and manufacturing
• Machine learning
Machine learning is already in use in some mining operations – particularly for carrying out structural inspections to map out a mine environment.
To map the environment, the machine learning algorithm is fed environment data and taught to identify potential issues, as well as conduct rock face identification, ground analysis, and structural integrity assessments. The algorithm can complete all of this in minutes, rather than hours, delivering data-driven insights to managers in real-time.
Machine learning can essentially relieve geotechnical experts of the job of having to review the mine environment, allowing them to focus on other activities. The more data the tool is given, the ‘smarter’ and more efficient it becomes at identifying trends, making it ideal for mine and environment monitoring, which involves the continuous collection and analysis of data.
In manufacturing, on the other hand, machine learning could effectively automate entire processes, with employees on the ground just approving machine actions – ensuring the algorithm learns correctly. With machines fully automating assembly processes, operations are faster and more consistent as robots operate with surgical precision. Quality goes up, costs go down, and human operators are relieved from the burden of labour intensive work.
• Predictive analytics and remote monitoring
Predictive analytics and remote monitoring allows mine managers and manufacturers to predict when equipment or machinery could fail, evaluate and improve operational performance, assess environments and much, much more.
For example, most mine operations have strata monitoring tools and early warning devices for structural failure in place. The information recorded by these tools could then be fed into an analytics platform to determine if the environment is safe enough. Remote monitoring tools – such as telltales and extensometers – fed into an analytics platform can be used to evaluate more hazardous areas. This helps to protect employees and reduce the costs associated with mine exploration and potential accidents.
Also, predictive analytics could be used to monitor the performance of individual equipment and accurately predict when it will not be operating at optimum levels and raise early maintenance alerts – rather than waiting until equipment falters.
The result is increased operational efficiency, lower costs (as it’s easier to achieve specific actions in the manufacturing cycle) and ultimately, more revenue.
Are technologies being deployed effectively across industrial operations?
The use of emerging technologies is in its early days, but there are a number of organisations in both mining and manufacturing that are making use of the technologies available to improve productivity and reduce costs.
FRAGx, developed by Petra Data Sciences, for example, uses machine learning algorithms and 3D point cloud data to “automatically assess ore fragmentation in less than a minute with no manual processing required”. The algorithm essentially automates repetitive geotechnical engineering tasks, allowing mine operators to quickly inspect an environment.
Predictive analytics are used by mines and manufacturing plants around the world to identify when equipment failure might occur, monitor machine activities and automate processes. With predictive analytics, machine maintenance can be scheduled well in advance – minimising downtime and maximising productivity.
Ultimately, organisations need to embrace emerging technology and start thinking of how it can be implemented and used effectively to help drive business performance.
The future of technology in mining and manufacturing
It’s important to appreciate that new technology in mining and manufacturing – particularly in relation to artificial intelligence solutions – is in its infancy and has a lot of room to grow. For any business it’s all about improving productivity and reducing costs, and in the future, we can expect mining and manufacturing operations to incorporate sophisticated technologies to manage the labour-intensive aspects.
Mining and manufacturing employees then move to job functions that add more value to their processes, investigating data, validating machine actions, strategic planning and much, much more.
In the future, it’s likely that mine and manufacturing activities will be completely automated – with a few human operators on the ground to make sure everything goes according to the plan. No doubt technology is around for the long term, and as long as these solutions can help mining and industrial organisations to reduce costs, improve performance and protect employees, they will be readily deployed wherever, whenever.
About the author
Glyn Pierce-Jones is CEO of Trolex, the Manchester-based sensing system company. He has been with the company for 9 years, having earlier served in a variety of senior roles in media and industrial sector companies.
Contact Details and Archive...