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Transforming Mines, and Ore Processing Plants

by | Oct 7, 2021 | Event, Metals, Mining, Minerals

Jonas Berge

Jonas Berge

Senior Director, Applied Technology

4D: Dull, Dirty, Dangerous, and Distant

Oct 26-28 Emerson Exchange Asia-Pacific virtual conferenceMining pits and even ore processing plants are typically in remote locations where not many want to live and work, so manpower is a challenge. Getting contractors to site for urgent maintenance is a challenge too. These locations are referred to as 4D: dull, dirty, dangerous, and distant. Huge shovels, trucks, conveyors, and mills are used to move and process large amounts of ore with minimal amount of manpower.

Huge equipment also means huge corrective maintenance cost in case of breakdown. The huge spare parts are expensive and a logistical challenge to transport and replace. Personnel require special training. Repairs take long, during which the equipment is not operational meaning production loss. These are expensive assets so they should not be idle. And with unplanned downtime due to failure, a production commitment may be missed. Equipment downtime is opportunity cost.

4C: Captivating, Clean, Comfortable, and Close

Condition monitoring of electric rope shovels is now automated with real-time reliability data collection. With real-time data the symptoms of developing problems in the motors and gearboxes such as in their bearings and gear mesh can be detected much sooner while the problem is still simple to repair. Maintenance becomes predictive. Symptoms picked up may include vibration and temperature. With this early warning, all that’s required, may just be lubrication or alignment, and thereby avoiding breakdown and expensive repair. That is, cost is reduced by avoiding repair and downtime, and only maintaining when really necessary.

Moreover, maintenance personnel need not go to equipment to manually collect the data, thus also improving productivity. Another example from the mining pit is condition monitoring of trucks also automated with real-time reliability data collection on gearbox, alternator, cooling pumps, differential and drive transmission, and cooling fans. Similarly, in the ore processing plant, condition monitoring of SAG mills and ball mills is automated with real-time reliability data collection on ring gear and motors. Associated equipment such as the SAG mill water intake valve is also monitored to predict problems early. Condition monitoring of conveyors automated with real-time reliability data collection on motors, gearbox, and pulleys is another example.

Once digitalized, shovels, trucks, conveyors, mills, and other equipment in the mining pit and ore processing plant can be managed from a local site maintenance office with the data from the equipment streaming in, in real-time. But the data can also be transmitted across the Internet to an integrated operations center, or iOps center, anywhere in the world. It can be a downtown office in a major city which instead is captivating, clean, comfortable, and close – 4C instead of 4D. The iOps center can be staffed by a pool of experts on the equipment in the mining pit and ore processing plant. The same center can also manage production, safety, and sustainability.

Hyper-Automated Mines

Mines and processing plants deploy Digital Operational Infrastructure (DOI) to enable these new ways of working. The most visible aspect of this Digital Transformation is personalized dashboards with information for each role, like site manager, and energy, maintenance, reliability, integrity, production, quality, and safety. Each person has different responsibilities and different KPIs. The information in the dashboards are the up-to-the-minute real-time indexes they need to do their job better to meet their KPIs.

The information in the dashboards comes from predictive analytics software. Analytics is based on Artificial Intelligence (AI). Equipment Performance analytics such as for heat exchangers is based on first principles AI. Equipment Condition monitoring such as for compressors and pumps is rule-based AI using well-known cause & effect. Process analytics for unit processes is also first principles and rule-based AI. The analytics is descriptive and prescriptive. These apps are readymade and work out-of-the-box without programming or data science. These are purpose-built apps, not general-purpose data analytics, they are made specifically for pumps, heat exchangers, and other equipment – with embedded domain knowledge. Analytics of large complex composites of multiple pieces of equipment and end-to-end processes may use machine learning.

New data-driven work practices require new data. New data comes from advanced sensors that automate manual data collection which used to rely on portable testers or reading mechanical gauges. Sites have lots of data, but it is usually mostly process data, while equipment data is lacking. Therefore additional sensors are required. Wireless sensors are the most practical solution in existing plants were laying more cables is not practical. WirelessHART is the key standard for wireless sensors in a challenging industrial environment full of steel obstructions. Many sensors, but not all, are non-intrusive meaning they clamp on to the outside of the pipe or equipment without the need for cutting, drilling, or welding. Others reuse existing process connections. So sensors can be added while the plant is running. Sensors onboard trucks and shovels are connected to a mobile network router using the mobile network to transmit the data to the analytics.

All this automation for maintenance and reliability, but also for energy management and integrity, is referred to as monitoring and optimization (M+O). It complements the existing automation for core process control or CPC in the mining pit and ore processing plant. The standard architecture for Industry 4.0 is the NAMUR Open Architecture or NOA. M+O and CPC are two separate security zones. NOA is key to preserving the robustness and safety of the CPC while enabling flexibility and access for M+O.


As a result, mining pits and ore processing plants avoid failure thereby reducing reactive maintenance cost, as well as avoiding unplanned downtime and associated opportunity cost. Reduced preventive maintenance means reduced cost and associated inspection manpower, freeing personnel up for other tasks. Finally, reduced preventive maintenance outage and associated opportunity cost.

Interested to learn more about predictive maintenance? Join us at Emerson Exchange Asia Pacific, coming on October 26-28, 2021!

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The opinions expressed here are the personal opinions of the authors. Content published here is not read or approved by Emerson before it is posted and does not necessarily represent the views and opinions of Emerson.