Edge computing: unearthing new value for mining and metals applications

The mining and metals industries are embracing digital transformation, including IIoT technology and concepts, to achieve new levels of production efficiency.

In addition to fluctuating demand for extracted commodities, the mining and metals industries face several new challenges, namely balancing profitability and production efficiency while managing sustainability and minimising environmental impact.

These industries are embracing the digital transformation journey, which includes incorporation of IIoT technology and concepts so data can be leveraged to achieve new levels of production efficiency. Gathering, storing and analysis of production and machine data form the building blocks in this strategy and are integral to the implementation effectiveness.

Mining, like many other industrial sectors, generates large volumes of data. Much of the initial IIoT discourse focused on cloud computing, which meant that large quantities of raw data would be dispatched to cloud-based data lakes, analysed and used in optimisation algorithms to drive real-time decision-making.

Naturally, mining operations are often distributed throughout many remote locations with limited infrastructure, and this presents some connectivity challenges. When factoring in cloud architecture, data costs and latency, a pure cloud computing solution may not be the best answer to realise the full potential of digital transformation.

Process overview

Mining and metals processing operations consist of many areas with opportunities for improvements through automation and data gathering (Figure 1). Sometimes the automation and communication platforms are delivered as part of OEM machinery or equipment skids, while other times they are ‘stick built’ with equipment and systems constructed in the field.

PLCs play an important role in almost every area and may include local HMIs. Remote terminal units (RTUs) are also widely used, providing some functionality similar to PLCs and adding remote connectivity features. Increasingly, systems incorporate intelligent field devices like variable frequency drives (VFDs) that can supply extensive operational and diagnostic data. Larger processing areas may rely on a DCS and plant-wide operations may be monitored by a SCADA system.

Figure 1: Many aspects of mining operations require some level of automation, and nearly every area creates data useful for analysis and operation. For a larger image click here.

Following is a summary of typical operations:

• Extraction: Pit and underground tunnel equipment and conveyors, characterised by deployment across large areas.
• Materials handling: Crushers, stackers, screeners and autonomous vehicles.
• Comminution: First-stage SAG mills and secondary-stage ball mills.
• Separation: Flotation cells, leaching, thickeners, solvent extraction and filtration.
• Refining: Systems such as electrowinning, electrolysis and smelting for extracting and purifying metals.
• Logistics: Rail and port operations for handling mining production.
• Utilities: Includes electrical power distribution and monitoring to support other systems.
• Water management: Systems to supply, treat, store, recover and reuse water to provide the right quantity and quality needed for processing.
   

Because automation platforms and mining equipment have generally become more intelligent over the years, there are more opportunities than ever for obtaining the right field data and acting upon it to overcome operational challenges.

Edge control platforms surmount challenges

It has been possible for many years to stitch together various traditional automation technologies with satellite and radio communications, achieving some level of automated control and remote visibility. However, these solutions have usually been costly and difficult to create, operate and support.

This has changed with the introduction of a new class of automation platform called an edge controller. An edge controller combines a real-time operating system (RTOS) with a general-purpose operating system (OS) like Linux (Figure 2).

Figure 2: Edge controllers seamlessly and securely coordinate deterministic control with general-purpose computing on one platform.

The RTOS provides direct deterministic control and monitoring of field equipment, much like a PLC or RTU. In fact, edge controllers can be used just like PLCs, even if users do not immediately take advantage of additional features, providing a futureproof design.

The general-purpose guest OS enables capabilities such as advanced computing, analytics and data storage. In addition, the general-purpose OS offers much more capable communication options, even over the low-bandwidth connections commonly encountered with mining operations.

Because the RTOS and general-purpose OS are virtualised at the hardware level onboard the edge controller, they are completely independent from both a hardware and software standpoint. In fact, each OS can be independently acted upon and rebooted. However, the two OSs can communicate with each other securely using industry-standard OPC UA connectivity. This unique configuration preserves the robust always-on RTOS operation while enabling modern computing capabilities.

Edge controllers are physically built to withstand the harsh conditions found at remote mining sites, including extremes of temperature, contaminants and vibration. The onboard general-purpose OS offers the following computing advantages:

• Security: Includes defences suitable for prevalent IT-like issues such as network storms and denial-of-service attacks.
• OT connectivity: Natively supports OT-oriented communication protocols, including legacy versions such as Profinet and Modbus/TCP, as well as newer versions with built-in security like OPC UA.
• IT connectivity: Natively supports IT-oriented communication protocols with built-in security such as MQTT and secure sockets (HTTPS, SSL, FTPS), providing appropriately secure communications performance.
• Flexibility: Users can develop applications in IT-oriented languages like C, C++, Python, Java and many more.
   

OT personnel can maintain a focus on the deterministic portion of the system they are most familiar with, and IT personnel can work with the general-purpose system. The two groups can work completely in parallel, or they can coordinate and crossover as needed or desired in a clearly defined manner. Remote connectivity makes edge controllers a natural fit for mining operations, where limited staff may need to support assets distributed over large work sites or anywhere in the world.

Edge controllers can be integrated into existing systems to provide new capabilities without disrupting proven operation — or they can provide a complete control, monitoring and analytical solution for new installations.

Mining applications with edge control solutions

Below are a few specific mining applications that can benefit from edge control; namely ore tracking, leveraging vibration data, blending and stockyard optimisation and power optimisation.

Ore tracking

Managing ore characteristics and variability are a key challenge after it is extracted from the ground because optimal downstream processing is very much dependent on ore size and quality. The initial materials handling and conveying processes use large mechanical equipment that can be prone to failure if large fragments of ore pass through the system undetected. Therefore, the ability to track ore size as it progresses through processing helps mine operators identify conditions that could potentially cause downstream blockages or mechanical breakdown of equipment, resulting in hours of unplanned downtime.

By leveraging a combination of sensors, instrumentation and edge controllers, the ore loading on conveyors can be analysed. Live parameters like conveyor belt tension can be monitored and analytically compared with historic data. Potential deviations can be quickly identified, and the control system can intervene to prevent a downtime incident.

Ore quality also impacts downstream processing, so tracking ore quality is perhaps the most crucial aspect of managing mine processing plants. The mineral concentration varies in the ore deposit itself. After extraction, ore is sampled and results regarding ore size, fines content, moisture level and more can be transmitted to the edge controller.

While this sampled load of ore is being transported to the processing plant by haulage vehicles, the edge controller can use the raw data regarding ore characteristics to perform analysis through embedded algorithms, and then use the results to determine the most beneficial settings for improved control of the processing plant.

For instance, based on the incoming ore size, the edge controller may adjust the gap on the crusher circuit to ensure higher efficiency. Based on the fines content, the edge controller could adjust downstream mill speed to optimise energy consumption and ensure the milling step produces the optimal size distribution for higher recovery of metal in the flotation circuit. Additionally, the associated dosage of chemical reagents could be optimised for best effectiveness and minimised waste.

Leveraging vibration data

Mining is a mechanically intensive process with many pieces of rotating equipment. Vibration is inevitably a problem, but if it is detected and analysed it can be used to predict impending operational issues.

Mine processing equipment consists of high CAPEX and OPEX items. Tracking key process indicators (KPIs) — such as overall equipment effectiveness (OEE), equipment availability and MTBF — empowers operators to proactively manage maintenance and sustain production levels.

Equipment such as conveyors, crushers, stacker reclaimers, ship loaders and mills are prone to failure due to vibration. At the machine automation level, it is possible to collect a huge amount of data at a high frequency to measure vibration, temperature and noise via sensors and instrumentation. The raw data itself is too excessive for transmission to the cloud due to costs and bandwidth issues.

A better option is to relay this large volume of machine data to an edge controller, which can then use the raw data inputs to complete some preconfigured computations and send the essential time-series data to a supervisory system. This concentrated information is much more suitable for transmitting via low-bandwidth serial, Ethernet, cellular or other networks.

The operations team can then apply analytics to this time-series data to establish baseline equipment profiles and assess if any part of the system is becoming mechanically compromised from a processing or hardware standpoint.

Blending and stockyard optimisation

Bulk commodities like iron ore and coal are part of complex value chains, and mine operators are challenged to deliver these commodities at the required specification to end customers. Tracking and blending of ore is a vital part of the operation, and edge controllers can help customers track material and optimise supply chain logistics.

Autonomous haulage vehicles have become widely adopted in the mining industry, and it is critical to manage diesel consumption and optimise routes to manage their overall operating costs. Onboard controllers allow for sophisticated vehicle control with features like anti-collision and position monitoring; however, there is no embedded PLC and the vehicles do not have permanent connectivity to a plant network.

Mine operators can get valuable insight from monitoring fuel pump pulses and various other raw data that can be obtained via a controller area network (CAN) bus. CAN bus is an industry standard protocol used with many types of vehicles.

In terms of route planning, the haulage truck routes can be organised in conjunction with other mechanical equipment like excavators and crushers. In situations where a mine excavator goes offline, an associated edge controller can then communicate with haulage trucks via wireless to reroute these vehicles to operational excavators and avoid situations where the crusher remains idle. On the other hand, routes for multiple trucks require careful scheduling to avoid situations where too many haulage vehicles are queueing to offload ore into the crusher plant. This kind of live route optimisation can deliver significant savings for mine operators.

Additionally, raw data paired with analytics delivered by an edge controller can provide rich insights into the integrity of the vehicle, supporting preventive maintenance.

Optimisation of power consumption

Aside from the mechanical intensity of operations, energy consumption of mining assets is another significant part of overall operating costs. Much of the existing power infrastructure is based on diesel generators, and with the ongoing pressure for environmental sustainability, mine operators are also being challenged to reduce emissions associated with diesel generators.

Mining power plants typically run with a large spinning reserve, but there is often little visibility into active plant operations, specifically when energy-intensive equipment is coming online. When there is a lack of coordination for equipment going from standby to duty mode, sites are likely to experience brownouts due to power surges.

In the case of a standalone power plant, diesel generators are well equipped with instrumentation and sensors, and this data can be sent to an edge controller. The edge controller can also analyse other raw data from the processing plant to identify when more power will be needed. Armed with this information and connectivity, the edge controller can make step changes to the power plant through its real-time operating system.

In the case where large energy-consuming items like a SAG or bill mill goes into duty mode, the edge controller can anticipate the need for spinning reserve and interface with the diesel generator to effectively adjust power requirements.

Conclusion

Mining and metals operations are much more than low-tech digging ventures. These industries are looking for any technological advantage to help them efficiently and cost-effectively adapt to production and market changes in a sustainable and environmental manner. There are therefore many opportunities to realise benefits by embarking on a digital transformation.

Edge controllers are a modern automation platform for enabling digital transformation and effectively applying IIoT concepts. Because mining operations are most often remotely located, any useful technologies must be suitable for these conditions and provide extensive communication options. Edge controllers are built for this OT environment, and have the latest and most secure IT computing and networking features. Edge controllers are especially compelling for this service because they can gather and store data locally, process and analyse it, directly inform operational logic of optimal settings, and relay the most essential information to higher level systems.

Top image credit: ©stock.adobe.com/au/Dusko