داده های کانی شناسی خودکار
Abstract
Graphical abstract
1. Introduction
2. Materials and methods
3. Results
4. Discussion
5. Conclusions
Declarations of interest
Acknowledgements
Appendix A. Supplementary data
References
Abstract
Assessing the success of sensor-based sorting in the raw materials industry currently requires timeconsuming and expensive empirical test work. In this contribution we illustrate the prospects of successful sensor selection based on data acquired by scanning electron microscopy-based image analysis. Quantitative mineralogical and textural data from more than 100 thin sections were taken to capture mineralogical and textural variability of two different ore types from the H€ ammerlein SneIneZn deposit, Germany. Parameters such as mineral grain sizes distribution, modal mineralogy, mineral area and mineral density distribution were used to simulate the prospects of sensor-based sorting using different sensors. The results illustrate that the abundance of rock-forming chlorite and/or density anomalies may well be used as proxies for the abundance of cassiterite, the main ore mineral. This suggests that sorting of the Hammerlein ore may well be achieved by either using a short-wavelength infrared detector € d to quantify the abundance of chlorite d or a dual-energy X-ray transmission detector to determine the abundance of cassiterite. Empirical tests conducted using commercially available short-wave infrared and dual-energy X-ray transmission sensor systems are in excellent agreement with simulation-based predictions and confirm the potential of the novel approach introduced here.
Introduction
Sensor-based sorting is a technology applied by the mining industry to reject coarse barren particles (in a size range between 10 and 100 mm) of a heterogeneous ore at an early stage of a beneficiation process (Knapp et al., 2014; Wills and Finch, 2016; Wotruba and Harbeck, 2010). The sorting process can be subdivided into four basic steps, as illustrated in Fig. 1 by the example of a chute sorter (Nienhaus et al., 2014). Before sensor-based sorting can be implemented into a flow sheet it must be the first objective to identify the sensor, which is able to detect d or even quantify d a suitable property that allows classifying the particles of the investigated ore type into concentrate and waste. Most commonly, direct identification of the mineral of interest is not possible because suitable sensors are not available. In such cases, a measureable property that correlates well with the mineral/element of interest d a so-called proxy d can be selected for sensor-based sorting. Examples of commonly-detected properties/proxies are atomic density with a (dual-energy) X-ray transmission sensor, (DE-)XRT for short (e.g. Neubert and Wotruba (2016); Robben et al. (2013); Walker (2017)), color, fluorescence, or transparency (optical sensor), shortwavelength infrared or near-infrared radiation (SWIR/NIR; e.g. Dalm et al. (2017); Phiri et al. (2018); Iyakwari et al. (2013)), or X-ray fluorescence (XRF; e.g. Nadolski et al. (2018)).