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 material handling and conveying processes use large mechanical equipment which 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 which could potentially cause downstream blockages or mechanical breakdown of equipment, resulting in hours of unplanned downtime.
Traditional programmable logic controllers (PLCs) have typically formed the front-line operational technology (OT) automation platform used throughout mining operations. These devices are commonly used for real-time control, but only more recently have been tapped for implementing higher level IIoT communications and analysis. This change is enabled by improved IT computing methods which are merging with OT platforms, resulting in capable edge-located IT/OT controllers. Data available at the operational edge is now easily accessible and able to be processed and transmitted, paving the way for analytics and optimization deployments. An edge controller combines a real-time operating system (RTOS) with a general-purpose operating system (OS) like Linux.
By leveraging a combination of sensors, instrumentation and edge controllers, the ore loading on conveyors can be analyzed. 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. Tracking ore characteristics enables the mine operator to better understand energy consumption, plant feed rates and ore losses, helping them to fine tune process parameters and optimize the overall profitability per ton of ore processed. 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 optimize 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 optimized for best effectiveness and minimized waste. Because of the unique location and performance of an edge controller, it can take multiple inputs, perform analyses, and immediately respond to and act on changes in ore characteristics. This kind of edge-enabled analysis delivers significant savings on operational costs and maximum metal recovery due to instantaneous decisions made on premise – rather than the latency challenges of running analytics in a cloud-based solution and then trying to implement the associated operational changes in a timely manner.
For more information on the use of edge technology in metals and mining, check out this Emerson White Paper.
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