Digital Mine – Rise of the Machines!

With ever increasing focus on leveraging digital technologies to transform the mining business, it is expected that newer use cases will emerge around the mining value chain

Digital Mine – Rise of the Machines! - CIO&Leader

The pioneer in the concept of ‘Digital Mine’ has been Rio Tinto, the Australian mining major which transformed Pilbara mine into a mine of the future, with a real-time operations center in Perth, automated haulage, drilling and trains. This has resulted in significant productivity gains at lower risks due to optimized operations.

Transforming Mining with IoT, Automation

The mining industry has been at the cutting edge of innovation for several decades. For instance, Automation solutions have been in use in both surface mining and underground mining to reduce human labour, optimize costs and enhance productivity. In recent years significant investments in Big Data Analytics, Information Management and Internet of Things (IoT) is helping mining companies enhance productivity, reduce equipment downtime and manage environmental emission standards. A recent survey of mining companies in Canada revealed investments in Big Data and Analytics tooling (53%), autonomous vehicles (30%) and robotic process automation (29%). To understand the transformative value these technologies, bring to the mining industry let us quickly review how technology is transforming the mining industry across its value chain.

  • Acquire Mining Lease – Mining companies must acquire a mining lease before starting of mining operations. The mining lease is again a decision point based on exploration done in the area. The results of exploratory drilling are analyzed using Data Integration, Big Data solutions and commercial value of the lease and feasibility analyzed. A lot of historical data on similar leases and lessons learnt are vital in taking strategic investment decisions.
  • Mine Design and Plan – Companies can leverage data from mines designed for same mineral type, on similar geological formations. Such mine plan designs can be stored in a Data Lake for future analysis and trends. Geological modelling data from similar mines can be leveraged to help structure the bench plans and capacity can be derived based on the resource and production plans.
  • Drill and Blast – Placement of drill holes is done based on ore body orientation and is key to ensure overburden to be cleared is less. The GPS co-ordinates and ore body digitized maps from the Data Lake can be used to layout the drill hole efficiently. Automation is being used in drill hole blasting for accuracy as well enhancing productivity margins in open cast mines like Coal. In open cast mines like Coal, drilling and blasting account up to 10-15% of mining costs, hence the outcomes of drilling and blasting need to be effectively managed.
  • Load and Haul – Once the raw mineral is blasted, it needs to be loaded and hauled into dumpers and shovels. IoT sensors, Automation and Big Data Analytics play a pivotal role in optimizing mine operations. Dumpers run on automation solutions with dynamic route planning based on real-time dashboards. The sensor data from drill machines and dumpers mashed with GPS co-ordinates of routes help make the operations digitally aware.
  • Stockpiles – Dumpers carry the ore and place them in stockpiles. Stockpiles serve two purposes: 1) raw mineral/ore is stored temporarily before moved for processing 2) use in blending of ore depending on ore concentration. Stockpiles are now measured using drones and laser-based measurements which provide a 3D model of stockpiles thereby enabling companies to track the size and quantity stored.
  • Crushing and Processing – The ore is taken for crushing where the ore is passed through a screen to transfer the ore depending on size to either a primary crusher or secondary crusher, thereby reducing the ore size to enable transport by conveyors into the processing plant. The crushing plant must run 24*7 and hence sensors embedded in the machines provide useful data around throughput of crushing as well as equipment performance metrics that enable predictive maintenance schedules to be planned. Post crushing, there are numerous processing activities, such as sizing, ore concentration wherein the increase in percentage of the mineral in the concentrate. There are various methods of concentration like froth flotation and electrostatic separation.
  • Transport and Logistics – Most mining sites are in remote areas, resulting in transportation and logistics playing a crucial part in moving the processed minerals as well as the ore from mine site to processing plant and then by railway or shipping to consumer destinations. Use of Big Data Analytics and IoT can help track ore and processed minerals across the mining value chain. A study by US Bureau of Mines indicated that transportation costs ranged from 22-26% of the total operational costs.
  • Consumers – Consumers of minerals are mainly energy companies (like power companies for coal), cement manufacturers (for limestone, dolomite) and steel manufacturers (iron ore, manganese, etc.). In case of rare earth elements, the consumers could be drug manufacturers or battery manufactures. Minerals are also stock piled by commodity companies that trade on the commodity markets. Consumers often leverage Big Data Analytics for pricing simulations and potential demand for specific minerals based on global demand and technological disruptions like rise of electric vehicles to manage pollution level and reduce greenhouse gas emissions.

As is evident from the transformational impact of digital technologies at each stage of the mining value, companies are increasingly looking at leveraging these technologies not only for productivity enhancements and cost optimization but also to comply with stringent environmental impact parameters. For open cast mines, Suspended Particulate Matter (SPM) and dust must be effectively managed, as well as impact of mining on ground water. Mining companies manage dust through process control systems and water spraying trucks. Real-time dashboards using Data Integration and Big Data Analytics monitor metrics like dust and moisture levels at haul roads. The key benefits of digital transformation in mining industry can be summarized as:

  • Digital Mine – A real-time insights into the mining operations from mine planning, to drilling and blasting, loading and hauling, stockpiling to transportation and delivery to end consumers.
  • Cost Optimization – Managing costs by automation and leveraging IoT and Big Data Analytics to reduce costs of transportation, labour, logistics and equipment down time.
  • Environmental Hazards – Manage environmental hazards like air quality, dust levels, impact on ground water etc. Mining companies need to comply with stringent environmental regulations worldwide. This enables mining companies to engage with local communities on crucial issues like water management, waste management, etc.
  • Productivity – Automation in haulage, transportation and drilling has brought about significant productivity benefits. Using Big Data Analytics has enhanced route planning to dumpers and transportation systems from mines to processing plants and to downstream consumers. Historians and MES systems help define better control loops within a process, department across the value chain.

In India, Hindustan Zinc has built a digital mine prototype at its Sindesar Khurd mine as is adopting transformative technologies. With ever increasing focus on leveraging digital technologies to transform the mining business, it is expected that newer use cases will emerge around the mining value chain.

The author is Data Platform Solutions Lead at the Services Integration Hub in IBM and has written three books

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