Here’s How IoT is Making Mining a Better Process - HPC ASIA

Here’s How IoT is Making Mining a Better Process


A recent study by global mobile satellite company Inmarsat shows that IoT can dramatically reduce the impact of mining operations on environment. Inmarsat’s ‘The Future of IoT in Enterprise – 2017′ study reports that 47% of organisations identified monitoring environmental changes as their number one priority for their IoT deployments. IoT helps the mining companies to better monitor and manage their assets. In fact the study shows that IoT will enable the companies to react quickly to potential issues.

Komatsu, one of the leading heavy equipment manufacturers globally, has opted for industrial internet of things solution to help its customers, which are mining companies. Using the solution from Komatsu the companies will be able to monitor the large equipment used by them for in surface and underground mining. When delivering equipment solution to its mining customers, Komatsu found that more and more of them asking for connected machines, so that they are able to monitor and increase asset utilisation and productivity. The company developed Komatsu JoySmart, which is an IIoT based service that helps customers optimize machine performance using machine data and analytics.

“With increasing customer demand and more connected machines, we were looking at data growth reaching 30 terabytes per month. Our former environment limited our ability to scale, grow and innovate,” said Anthony Reid, senior manager of analytics at Komatsu. The company deployed Cloudera’s cloud-based Industrial Internet of Things (IIoT) analytics platform to develop JoySmart. The platform draws and processes the data from various equipment from Komatsu being operated by the mining companies across various locations. This helps the company to increase asset utilization and productivity, and deliver essential resources including energy and industrial minerals for the global economy.

Reid and his team used Cloudera Enterprise Solution and Microsoft Azure to develop its platform. “By deploying Cloudera Enterprise on Microsoft Azure, our teams make the invisible visible, gaining valuable insights to help customers optimize productivity and mining efficiencies. Cloudera delivered the fast performance, data security and customer support that allowed our teams to access machine data analytics and collaborate with different user groups across the world,” said Reid. According to him, Komatsu data scientists can now build and deploy machine learning models faster to understand equipment operation, provide better insights for customers and ultimately influence future mining equipment design. “With Cloudera’s modern platform, we use advanced data analytics and machine learning to power our IIoT success. We now provide customers with better recommendations on machine utilization and deliver services faster. In one example, we were able to make recommendations for a large coal mining company that enabled them to double the daily utilization of their Joy longwall system,” he added.

The company is able to drive real-time processing, machine learning, and analytics on all of their IoT data, including data in motion and data at rest. It now uses the vast amounts of data to help its mining customers manage increasing pressure to be environmentally smarter and more productive at a lower operation cost.

SAP’s Leonardo is another such platform that uses machine learning, IoT, big data and analytics to help mining companies make an informed decision about resource deployment. Australia’s CITIC Pacific Mining is SAP Leonardo IoT for SAP Vehicle Insights to improve its mining fleet set up and utilisation. The IoT solution is expected to accelerate its three strategic objectives. These include detecting and monitoring real-time changes, making informed decisions and executing strategies more efficiently. The company is now able to streamline its business processes by collecting, mapping and analysing fleet and vehicle data in real time and increase vehicle utilisation to 80 per cent and reduce maintenance costs by 10 per cent.



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