Sensor-based sorting for potash operations

by | Jan 6, 2021 | Suppliers | 0 comments

By Rebecca Gotto and Lucinda Wood, Saskatchewan Research Council

SRC’s Industrial CT Scanner. Photo provided by SRC.

Sensor-based sorting is becoming increasingly prevalent for mining operations as a method to remove waste or to upgrade ore prior to downstream hydrometallurgical processing. Sensor-based ore sorting has the potential to upgrade feed prior to milling and hydrometallurgical processes, thereby reducing plant footprints, tailings storage facilities, and energy consumption. However, potash projects and operations need to know if this technology is appropriate, and what its optimal parameters are.

The problem

Of all the ore sorting technologies available, X-ray transmission (XRT) sorters are one of the most common choices. That is because XRT sorters pass high-intensity X-rays through particles, not just their surfaces, to generate images of varying greyscale that indicate mineralogical differences within the particle and then mechanically separate them.

A popular test currently used to evaluate the amenability of the ore to X-ray sorting is to simply pass the particles through an industrial sorter and obtain grey-scale images based on default imaging parameters. This test provides information on the presence of mineralogical differences, but it does not provide an understanding of the actual mineralogical composition of the ore. It also does not necessarily pick up all the differences owing to its default imaging parameters, especially for lesser known commodities or complex ores.

The solution

A new test performed by the Saskatchewan Research Council (SRC) that combines high-resolution X-ray micro Computer Tomography (CT) with QEMScan has the ability to gather not only information about the amenability of ore to XRT sorting, but to obtain valuable information about mineralogy and optimum sensing parameters that can streamline metallurgical test work programs. To complete the test, SRC’s team conducts 3D CT scanning on potash samples to obtain volumetric information of individual mineral phases. QEMScan is then used to calibrate the greyscale values of the 3D volume, which reflects different atomic numbers of minerals in the potash sample.

This test also provides information relating to grain size and associated X-ray attenuation coefficients, desired mineral presence, and information relating to associated minerals and clays used to later assist with developing sorting algorithms. This ensures that industry can get a quick and accurate understanding of whether sorting is the right technology for their needs and have the optimum parameters to proceed with their metallurgical test work. This also results in streamlined metallurgical test work as the range of parameters to be tested have already been optimized beforehand.

Industrial CT systems offer great versatility and many advantages in analyzing large or dense materials, such as mineral containing potash, with high X-ray attenuation, while providing significantly higher spatial and contrast resolution than common scanning techniques. In addition, whole cores can be analyzed without the need for crushing them, which greatly assists with ores from exploration programs. QEMScan can provide valuable mineralogical information of associated (and mineralogically) similar minerals to better understand the upgrading potential of an X-ray sorter.

The advantages

Combining the CT and QEMScan can address many limitations of the current way to determine whether XRT sorting technology is appropriate for particular ores. Mineralogy information from QEMScan can identify incidences of mineral encapsulation, problematic clays and detrimental minerals, as well as calibrate the greyscale values of CT data. The 3D CT imaging carried out on cores provides spatial information of the ore, mineral grades and grain size distribution without crushing them. These together can form a unique dataset that helps to characterize the formation and optimize the extraction process for potash and other minerals that no other test is able to do.

The most important advantage, perhaps, is that it is a non-destructive test. It also requires no special sample preparation, is flexible in sample geometry and size, can be scanned at extremely low resolution, and the scanning time is also considerably low compared to similar tests.

The applications

There are many cases where this new test would be highly valuable to industry, including core digitization for core logging and profiling; measuring spatial information such as grade, grain size distribution, surface contact areas between minerals; helping to optimize processing parameters where ore sorting is a potential unit operation; and monitoring dynamic processes such as solution mining and geomechanical tests.

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