Integration of X-ray radiography and automated mineralogy data for the optimization of ore sorting routines. (August 2022)

• Record Type: Journal Article
• Authors: Kern, Marius; Akushika, Jorgina N.P.; Godinho, Jose R.A.; Schmiedel, Tobias; Gutzmer, Jens
• Is Part Of: Minerals engineering. Volume 186(2022)
• Publication Date: 2022-08
• Subjects: Sensor-based sorting -- X-ray transmission -- X-ray radiography -- Mineral Liberation Analysis -- Beer-Lambert Law -- Scheelite -- Mittersill Mine; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2022.107739
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Predict and optimize the success of X-ray transmission sorting. Integration of X-ray radiography and automated mineralogy data. Empirical test work with scheelite ore from the Mittersill Mine, Austria. Avoids time-consuming, expensive and inaccurate trial-and-error batch testing. Improvement of resource and energy efficiency in the raw materials industry. Abstract: X-ray transmission sorting is arguably the most successful ore sorting technique used in the mining industry today. In order to establish the suitability of X-ray transmission sorting for a specific ore deposit or raw material type, the current state of the art involves time-consuming and costly empirical testing. In this paper, we show how the success of X-ray transmission sorting can be reliably predicted based on X-ray radiographs that are quick and inexpensive to obtain for a large number of samples. We document this novel approach using a large suite of samples of scheelite ores from the Mittersill deposit in Austria. Using only the X-ray radiographs at a spatial resolution of 0.12 mm we quantify the volume of high-density scheelite at different spatial resolution. The results are compared to and validated by data obtained by computer tomography and scanning electron microscopy-based image analysis. The X-ray radiograph data set is then down sampled to spatial resolutions of 0.8 mm, 3.2 mm, 12 mm and 24 mm in order to gauge the optimum spatial resolution prior to any empirical testing. Upgrading curves can be calculated, and in combination with quantitative data obtained by complementary analytical methods, the mineralogical and chemical composition of concentrate and waste can be predicted. The workflow can be transferred without any problems to many other ore types with a significant density difference between ore mineral(s) and gangue minerals.