Bringing industry 4.0 to mining operations By Jeff DeNigris, process automation solutions division of Malvern Panalytical
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ining operations, as with many industries, are benefiting from online analytics and automation solutions that tie scientific technologies proven in the lab (such as laser diffraction) with production processes (such as milling/grinding and classifying) to enable greater understanding of the variables that affect the final product. These mined materials often change over time in their quality, composition and grindability, impacting particle size as a result. Gaining insight into these variables in real-time is needed to optimize the performance of the process. Vertical Roller Mills (VRM) are common in mining operations for size reduction. They are expensive to install and operate, and are capable of producing hundreds of tons per hour of ground product. Yet VRMs provide no data on how well it is performing in terms of the material produced. As a result, VRMs use advanced controls to help maintain process performance levels and product output specifications while adapting to raw material changes. Soft sensors are used with multi-variate control schemes to predict what a direct sensor may report based on estimates from the offline laboratory analysis data. Characteristics such as mineralogy, morphology, particle size distribution (PSD), and colour can all be factors to consider when evaluating a mill circuit’s performance and enabling time-relevant monitoring of each has been proven valuable. As reported in an article with the company Vulcan Materials1, the process improvement capability of moving from
a soft sensor to an online particle size analyzer was dramatic. Yield, energy consumption, and standard deviations all improved significantly (Figure 1). In mining, there are three main categories of processing that may need online analytics to ensure optimization is possible: dry milling, wet (slurry) milling, and separation/filtration processes. Each of these steps involves size reduction, and techniques like laser diffraction can be implemented in-line to gain time-relevant data for better control and prediction modeling. Whether wet or dry, the technique is the same: determine sampling solution, prepare the sample for measurement, measure, and export the data in real-time as actionable intelligence. Then implement a feedback loop to maintain or optimize. (Figure 2). In-process imaging can also be used in mining with monitoring of crushed ore (Figure 3) or in-situ from hydrocyclones/ hydrosizers to see the separation process results. Identifying various components within the slurry such as crystals, amorphous particles, spherical solids, droplets, and/or bubbles are all possible (Figure 4). An in-process imaging system, such as a SOPAT Probe, uses advanced neural network algorithms to resolve these images into valuable data such as: volume and number-based particle size distributions, circularity, aspect ratio, and concentration. Multiple particle types can be analyzed from the same image and highly concentrated slurries can be monitored directly without dilution or sampling. These characteristics can provide great insight into the process in near real-time and give operators the ability to drive process optimization techniques, avoiding costly downtime, recycle/rework/reprocessing, and improve yield with quantitative data. In summary, today’s drive towards true industry 4.0 requires data and lots of it. With the advancement of faster, more reliable and security-rich communications, process control enablement will be starving for data. Scientific analyzers, advanced computing power, and high-speed communications now make real-time systems reliable. This paradigm clearly needs to expand the use of online analyzers to ensure that actionable intelligence is available and robust, enabling greater performance efficiencies to strengthen manufacturing expertise and deliver not only best-inclass, but world-class operations.
Figure 1 - VRM performance data from Vulcan Materials. / Figure 1 – Données de performance d’un BRV (Vulcan Materials). 1
50 Revue minière du Québec
DeNigris,J. Murphy,J. Levonian,D., et. al., (2009 July) Reducing Costs, WorldCement https://www.worldcement.com/magazine/world-cement/july-2009/ u
