1 Sensor-based sorting – state of the art
Sensor-based sorting is used in the mining industry to separate 10- to 100-mm-large rock particles of a heterogeneous ore into a concentrate with a high content of valuable minerals and waste with a low content of valuable minerals . The sorting process can be divided into four basic steps, as shown in Fig. 1 based on the example of a belt sorter.
This iterative and empirical approach has the important advantage that it can be applied without any prior knowledge of the composition of the raw material. It also has, however, a number of disadvantages:
It is not clear why a group of ore particles is sorted out and vice versa, why the other group is yielded into the concentrate.
It is also unclear whether the tested sorting mechanism has delivered its full potential or whether potential for optimization of the sorter has remained undiscovered.
There are no indications whether parameters other than those selected for empirical testing could be used to concentrate the recoverable element or mineral more efficiently.
It can be expected that a comprehensive understanding of the mineralogical and structural properties of the raw material to be sorted enables an estimation of the efficiency of a sorting process. The required data can be provided, for example, by means of automated mineralogy (AM). It is precisely these data generated with AM, in combination with ML, which are used in a completely novel approach for the simulation of sorting efficiency.
This new approach has been developed at the Helmholtz Institute Freiberg for Resource Technology (HIF) within the framework of the r4 programme funded by Germany’s Federal Ministry of Education and Research in the projects AFK (registration number 033R128) and ResErVar (registration number 033R129). Quantitative mineralogical and microstructural data collected from a series of polished thin sections provide the suitable basis to simulate the performance of sensor-based sorting using different separation parameters. Empirical tests with commercially available sensor systems were conducted to validate the simulations experimentally. A flowchart of this simulation-based approach is shown in Fig. 2 (right). In this paper, the procedure is shown based on the example of a complex skarn ore from Hämmerlein (Ore Mountains) in Germany. Other examples have already been published in international trade journals [3; 9].
In preparation for AM analyses, polished thin sections of a suitable selection of samples of the raw material to be sorted are produced. The selection of the samples and the number of the sections to be prepared is derived from the inherent complexity of the raw material. The more complex and variable the raw material is in terms of its composition, the more sections are needed. In preparation of the sections, a sawn-off remnant always remains which is very similar to the section in its composition. These remnants can be used for sorting tests.
For the application of ML, in a first step, the high-resolution AM false colour images are adapted to the coarser spatial resolution of the sensors. Fig. 3 shows an example of this with the three phases (chlorite, quartz and cassiterite) and information from SWIR and AM. The AM image is changed in its size, and in every coarse pixel, the surface area share of every mineral and from this the percentage of the value mineral are calculated. In the second step, a threshold for the value mineral is determined, and based on this a two-class image (valuable vs. non-valuable) is compiled. The simplified two-class images are used for training and testing a suitable classification process from ML (e.g. Random Forest). The classification process is trained to predict the probability from the SWIR data co-registered with the AM data that an individual sample exceeds the threshold value and should therefore be transferred into the high-value product during sorting. With this procedure, which is explained schematically in Fig. 4, an ML-optimized prediction on the value of the sample is generated.
3 Case study: Complex skarn ore from Hämmerlein
3.1 Raw material characterization
The Skarn ore consist predominantly of iron oxides (magnetite, hematite), silicates (amphibole, garnet, epidote and chlorite) and sulphide minerals. The cassiterite concentrations vary between 0.0 und 19.2 wt.% (average content: 0.6 wt.%). The sample variability is visualized with eight selected samples from various lithounits with false colour images (Fig. 7). Table 1 contains a listing of all analysed samples from the various samples. For differentiation between the ore and gangue rock, a threshold value of 0.1 wt.% Sn in cassiterite per sample was chosen. This value is exceeded by 35 % of the samples (28 of 81). The threshold value lies below the economic cut-off grade for mining (0.5 wt.% Sn; s. ), is regarded, however, as reasonable in the sorting phase in which the ore has already been blasted, transported and comminuted (personal communication from Dr Marco Roscher, Saxore Bergbau GmbH).
AM data enable the simulation of a theoretically unlimited number of sorting tests. In the following section, upgrading and sorting curves on the basis of the cassiterite content, the chlorite content and a ML-optimized prediction of the sample value are discussed. The results of the simulations are compared with data from an SWIR sensor as can also be used in sorting systems.
3.3 Properties of the sorted products
Before the decision is taken for or against the installation of a sensor-based sorting system in a mineral processing operation, a feasibility study must be undertaken. Taken into consideration in this are primarily the recovery of the valuable mineral and separation efficiency, information made available by the AM data. With separation of low-value material, however, important parameters of the preconcentrated product (e.g. modal mineralogy, mass pull, particle sizes) change, which influences the downstream beneficiation processes (e.g. comminution, flotation, density separation and magnetic separation). Here, too, the available data enable a range of evaluation possibilities, which influence the feasibility study for the sorting system.
This is illustrated in the following with reference to the example of ML-optimized value prediction. With the ML-optimized separation characteristic, mineralogical composition, cassiterite content and the average cassiterite particle size of the concentrate and waste are modelled for 60 %, 70 %, 80 %, 90 %, 95 % and 100 % cassiterite recovery (Fig. 9). The properties of the sorted products are compared, with a focus on a cassiterite recovery of 90 % and 95 %.
With increasing cassiterite recovery to 90 %, the mineral content of the concentrate is dominated by quartz, chlorite and iron oxide minerals. The fluorite and sulphide minerals, too, are collected predominantly in the concentrate (Fig. 9
4 Experimental validation
The results show excellent agreement between the empirical test results and simulations of the separation performance on the basis of AM data. The chlorite content measured with AM could be used to predict the success of the skarn ore separation. As chlorite has characteristic absorption properties in short-wave infrared, an SWIR sensor was chosen. The data obtained could also be used to optimize the sorting process by means of ML. The properties of the minerals in the simulated concentrate can be used for planning and prediction of the functionality of downstream processes such as flotation, density separation and magnetic separation.
The sorting of skarn ore with ML-optimized prediction is a good example to illustrate the interaction between sensor-based sorting and downstream processing steps: The unavoidably high content of chlorite in the concentrate is a disadvantage for further processing and chlorite reacts to reagents typically used for the flotation of cassiterite. This makes the separation of these two minerals more difficult. Moreover, the changed composition of the ore has an effect on the subsequent density separation and magnetic separation, which must be taken into consideration in models that predict the separation behaviour of the ore.
The simulation data presented in this study are based on analysis with MLA, but datasets of equal quality can be generated with similar instrument platforms like TIMA-X (TESCAN, Brno/Czech Republic), Mineralogic (Zeiss, Oberkochen/Germany) and QEMSCAN (FEI Company, Hillsboro, OR/USA). Common to all these systems is that the analyses are time- and cost-intensive, especially with the high spatial resolution used for this study. In current research projects, several possibilities are being investigated in Freiberg to drastically reduce the cost and the time for the necessary mineralogical tests. This is aimed at making the simulation-based approach even more interesting for industrial users.
Dr. Marius Kern
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz-Institute Freiberg for Resource Technology, www.hzdr.de
Marius Kern studied Geography (B.Sc.) in Würzburg and Geosciences with specialization in Economic Geology (M.Sc.) in Freiberg. From 2014 to 2015, he worked as a mineralogist at Hazen Research (Colorado/USA), and he joined the Helmholtz-Institute Freiberg for Resource Technology in August 2015. In 2019 he completed his PhD, which was realized within the scope of a project for the beneficiation of fine-grained complex ores. Here he also addressed the issue of how ores from the Hämmerlein deposit (Ore Mountains) can be optimally sorted.
M.Sc. Laura Tus¸a, Research Associate 1
Dr. Mahdi Khodadazadeh, Research Associate 1
Dr.-Ing. Thomas Leißner, Research Associate 2
Dr. Richard Gloaguen, Head of the Exploration Department 1
Prof. Dr. K. Gerald van den Boogaart, Head of the Modelling Department 1
Dr. Jens Gutzmer, Institute Director 1
1 Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology
2 Institute of Mechanical Process Engineering and Mineral Processing, Freiberg University of Mining and Technology
 Kern, M.; Kästner, J.; Tolosana-Delgado, R.; Jeske, T.; Gutzmer, J.: The inherent link between ore formation and geometallurgy as documented by complex tin mineralization at the Hämmerlein deposit (Erzgebirge/Germany). Mineralium Deposita 54(5), 2018 (a), pp. 683–698
 Kern, M.; Möckel, R.; Krause, J.; Teichmann, J.; Gutzmer, J.: Calculating the deportment of a fine-grained and compositionally complex Sn skarn with a modified approach for automated mineralogy. Minerals Engineering 116, 2018 (b), pp. 213–225
 Kern, M.; Tus¸a, L.; Leißner, T.; van den Boogaart, K.G.; Gutzmer, J.: Optimal sensor selection for sensor-based sorting based on automated mineralogy data. Journal of Cleaner Production 234, 2019, pp. 1144–1152
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 Tus¸a, L.; Kern, M.; Khodadadzadeh, M.; Blannin, R.; Gloaguen, R.; Gutzmer, J.: Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods. Minerals Engineering 146, 2020, pp. 106–150