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Advanced separation methods in the X-ray-optical
sorting systems
Jacek Kołacz1), Andrzej Wieniewski2)
1)
COMEX AS - Norway/Comex Polska, Krakow, e-mail: jacek.kolacz@comex-group.com
2)
Instytut Metali Nieżelaznych, Gliwice, e-mail: andrzejw@imn.gliwice.pl
Abstract
A new sensor based sorting system related to complex image analysis has been developed at Comex. The main advantage of this system is connected with its universality where many material parameters are analysed in the same processing unit by a single passage. It makes it possible to separate different mineral particles using over 20 parameters describing their colour, shape, texture and size. In addition the X-ray image (XRT) is integrated with the opti-cal analysis, thus providing more information not only about the material surface but also about the internal structure of the separated particles. The paper describes sorting examples of different materials like: copper ore, zinc and lead ore, and chrome ore.
Key words: X-ray sorting, optical sorting, ore sorting, sensor based sorting
Nowoczesne metody separacji w rentgenowsko-optycznych
urządzeniach sortujących
Streszczenie
Firma Comex, opracowała nowy system sortowania minerałów i materiałów, oparty na kom-pleksowej analizie obrazu. Główną zaletą tego układu jest możliwość zastosowania kilku czujników i systemów identyfikacji w tej samej jednostce, podczas tylko jednego przejścia materiału przez system. Układ pomiarowy opisywanego urządzenia oparty jest na kombinacji analizy optycznej za pomocą kamery oraz czujnika promieniowania Roentgena, w tym sa-mym urządzeniu. Umożliwia to rozpoznanie cząstek różnych materiałów na podstawie anali-zy koloru oraz kształtu, w której cząstki te mogą być identyfikowane pranali-zy pomocy ponad 20 parametrów stosowanych do opisu koloru, rozmiaru, kształtu i tekstury. Dodatkowo, sygnał z czujnika promieniowania Roentgena (XRT) jest integrowany z analizą optyczną, co daje ważną informację na temat własności nie tylko powierzchni cząstek, ale ich wewnętrznej struktury. W artykule opisano kilka przykładów sortowania różnych materiałów, takich jak: rudy miedzi, rudy cynku i ołowiu oraz rudy chromu.
Słowa kluczowe: technologia XRT, sortowanie optyczne, sortowanie rud metali
Introduction
Sensor based sorting systems available on the market today are not flexible enough to be applied in very different conditions without complicated reprogramming and mechanical reconstruction. Typical limitations for this type of technology are: a)
ina-bility to process small and large particles at the same time, b) limited analysis com-plexity, where the analysis is very often based on simple basic colour differences, c) inaccurate rejection due to particle trajectory variations and particle rotation. The newly developed sorting system from Comex eliminates the mentioned limitations and in addition, it makes it possible to separate different particles by sophisticated image processing. The analysing system includes an optical camera and the X-ray attenuation imaging in the same equipment. The sorting system can be used with both optical and XRT analysis or separately depending on an application.
1. Current limitations
Application of the sensor based sorting systems, without any doubt, brings a lot of possible advantages in many mineral operations. In the recent years there were a number of publications covering this topic, however, very little information was given about limitations related to practical implementations of such technologies. Sensor based sorting must always be tailored for particular applications [2]. Very few of the systems available on the market today are flexible enough to be applied in very dif-ferent conditions without complicated reprogramming and reconstruction. Conse-quently, in the majority of possible implementations, its complexity makes such ap-plications cost prohibitive or significantly reduced its potential. Great majority of the sorting systems today are configured as a vibratory feeder supplying the sliding plate where the analysis is done, further followed by the rejecting air nozzles provid-ing separation. This configuration, however, despite beprovid-ing very simple, provides a lot of disadvantages. The main limitations can be listed as below:
Inability to process small and large particles at the same time. When the large particle is passing the imaging sensor, the particle cannot be properly analysed since the rejection mechanism is often installed very close to this area and there is no time to complete the analysis, make any computation and reject it on time. As a result, such sorting systems require narrow size distribution of the input material, where too large particles are eliminated in the earlier stages.
Limited analysis complexity. The analysis cannot be too sophisticated since the rejection is very close in time to the analysis, so separation is very often based on simple basic colour differences. There is no time for any material shape or texture analysis in this configuration.
Inaccurate rejection. When the particles are supplied on the sliding plate, they tend to change trajectories and in many cases they rotate while falling down. This creates many errors during rejection by pneumatic nozzles so some particles end up in wrong fractions.
Consequently, the great majority of sensor-based sorting applications is concen-trated on simple colour analysis where one of the colour planes is used to decide about the waste and product particles. Furthermore, the separation efficiency is rather poor and often ranges between 85-95%.
2. Combining different sensors
The main advantage of the new system is related to its universality and at the same time very sophisticated image processing functions, which can be carried out in the same processing unit [3]. Figure 1 shows the new system configuration, where many different analysed parameters can be used to provide particle separation. The image analysis system includes a camera installed either over the transport belt conveyor or at its discharge end. The system includes the X-ray attenuation analysis realized by the XRT system in the central part of the conveyor belt. The sorting system can be used with both optical and XRT analysis or separately depending on an applica-tion.
Particle recognition used to separate different materials is based on a complex shape and colour analysis where the particles can also be identified by over 20 pa-rameters used for shape description. Some of them are: diameter in different orien-tations, perimeter, centre of mass, moment of inertia, particle elongation factor, edge sharpness, etc. Additional combinations of these parameters can also be used for distinguishing particles of interest. The surface of particles where different colours or contours vary in intensity and frequency can be analysed by FFT filtration (Fast Fou-rier Transformation) to recognize differences in texture and structure of the pro-cessed particles. This analysis brings much more complex information about the analysed particles rather than colour recognition alone. Finally, the XRT picture is integrated into the optical analysis, which provides a lot more information about the particle surface properties and its internal structure. All these sophisticated analys-ing functions require a lot of computation power and they have to be optimized to allow high capacity sorting. This is done by special program architecture and algo-rithm solutions allowing efficient management of the calculation routines and sorting priorities. This allows achieving still high separation capacity and extremely high efficiency where the product purity can reach even 99.9%.
Fig. 1. Comex optical sorting system – configuration with the X-ray and the optical analysis
3. Signal processing algorithms
Figure 2 illustrates the options for image processing diagram where two sensing principles are involved, based on the typical case where the rejected particles
repre-sent the waste fraction. The simplest combination of the sorting unit is the individual signal treatment for the optical and X-ray properties (Figure 2a). In this case the threshold parameters are used to identify the waste particles for each signal line and the information about these particles is filtered by the AND function logical filter, before the final signal is given to the rejection mechanism. In this case the rejected particles have to be defined by both sensor systems as the waste fraction. Figure 2b shows the similar combination of the signal processing, however, with the OR logical filter used before the rejection. This allows rejecting of the particles identified by one of the sensor systems as rejected fraction or both of them. Figure 2c shows another combination where the optical system is used as the verification tool, which allows the interesting particles to be analysed by the X-ray sensors. In this case the parti-cles identified by the optical system as the waste fraction, are removed from the input picture to the X-ray signal processing line. It may look like the OR function shown earlier, but this combination allows eliminating the significant number of parti-cles from the X-ray image before its processing and thus saving processing time regarding the X-ray image calculations. This can allow much faster image pro-cessing especially for advanced filtration procedures. Finally, one can configure this processing diagram as on Figure 2d, where the similar processing is done as for the case (2c), however, with the X-ray analysis verifying the image first, removing the unnecessary particles, and allowing the rest to be processed by the optical system.
4. Unconditional principle system
The logical functions used in sorting can be configured in different ways, and there-fore it is a very important how they are used in terms of optical and X-ray image analysis integration. It is also necessary to define priorities regarding more important signals. Eventually, this process can be optimised and some of the signals from a part of the sensors can be used to mask the rest of the analysed image, to apply more complex analysis for remaining particles. One of the complex analysing mod-els is called: unconditional response principle. The operating principle of this model is similar to the function of human being, where a human brain is not involved in physical reactions on e.g. touching hot objects. The similar principle can be effec-tively applied in sorting, especially where many sensing principles are involved.
Figure 3 shows the example of the processing model employing the uncondition-al response principle. First, the X-ray and the opticuncondition-al images are combined together with the high filtration threshold, to identify all very clear waste particles from the analysed image. This identification is carried out on the very low level, when the case is “obvious” regarding what can be defined as waste and what can represent the product. This procedure is very quick and does not involve any sophisticated processing from the sorting software. Obviously, these particles are automatically registered for rejection. Furthermore, the same initial image is processed by the similar processing blocks, however, the input image is filtered so the particles rec-ognised by the previous blocks are masked. Remaining particles may require more attention and therefore, can be treated by the advanced filtration tools like pattern recognition or FFT (both in the optical or the X-ray blocks), which require much more time and processing power. Consequently, more precise analysis is possible for identifying waste particles, which are more problematic for defining weather they belong to the waste or the product fraction.
Fig. 3. Configuration example with the “unconditional response” principle
5. Pattern matching
Pattern recognition is a common technique that can be applied for the detection and recognition of objects. This technique is already known and very popular in identify-ing e.g. faces in photography, music sequences, graphics etc. Generally, pattern recognition can be carried out in two different ways: the first being template match-ing and the second bematch-ing feature detection. Durmatch-ing template matchmatch-ing, a pattern is used to produce items of the same proportions. The template matching procedure is based on the incoming sample image, which is compared with templates memorized in the system. If there is a match, the sample is identified. Feature detection models, suggest that the sample image is broken down into their component parts for identi-fication. For example, mineral surface containing layers is identified by number of horizontal and vertical lines. The second method has been present in sorting for many years, however, the first one – template (pattern) matching is not reported or mentioned as successful.
In pattern matching, the idea is really simple and compares an image according to a template image [1]. The algorithm not only searches the exact appearance of the image but also finds a certain grade of variation respect to the pattern. This can be expressed in the following way: an image A (size (W x H) and image P (size w x
h), is the result of the image M (size (Ww+1) x (H-h+1)), where each pixel M(x,y)
indicates the probability that the rectangle [x,y]-[x+w-1,y+h-1] of A contains the pat-tern. The image M is defined by the difference function between the segments of the image:
.
Pattern matching in mineral sorting will be based on memorising typical examples of particles of interests and identifying them during the sorting process. The analysed image will first go through the typical processing stages as: digitising, colour analy-sis, noise reduction and edge correction. This part of processing is very individual and has to be thoroughly carried out separately for each type of mineral. Then, the images will be displayed as the black and white patterns showing different charac-teristic shapes. Furthermore, these shapes must be statistically related to the real particle property differences, which are the intention of sorting, like visual control, chemical assays, density tests, etc. At the end, a number pattern shapes will be defined as the product and the rest will be treated as the waste fraction. During the continuous operation the, memorised temples will be compared on-line with the processed images. The difference function results, as shown above, will be used to define a threshold for qualifying the particles as the product fraction.
In the literature, there are many different projects using the template matching technique to solve various problems. However, for pattern matching used for mineral identification and sorting, the algorithms need to be optimized to provide enough processing speed of the image. Very often, it has to be supported by the feature detection models in case the match probability level (difference function) from the template match is too low to be effectively used. The current examples of pattern matching given below are based on this kind of combination, where the particles are first identified by the template matching, and when the probability function value is too low, the feature detection can verify the analysed particle, still in the reasonable time. Too complicated calculation process can make the sorting sequence too long and block the whole process. Therefore, it is of critical importance to provide an optimal signal processing route for the signals, which really require such attention, as shown earlier on Figure 3.
6. Application examples
Combinations of signal processing lines, as shown earlier, will very much depend on the processed material properties and separation targets. One the application ex-amples, is shown in Table 1 and Table 2, where the Cr ore is upgraded using differ-ent settings of the sorting system. In Table 1 the sorter had specific settings with pattern recognition marked as “a”. In this case the feed material having 37.1% Cr, was upgraded to 39.7% Cr. The Cr recovery to the product was high at about 92.7%. The waste stream contained little Cr (20.6%) and only 7.4% of Cr was re-covered to this waste stream. At this stage it was not possible to provide any further improvement of the separation model in the sorting unit, to further increase the Cr content in the product.
In the next stage, the sorting algorithm was improved and involved pattern recognition for the analysed images. The result of this separation is shown in Table 2. Here the feed had slightly higher Cr content but it was increased to 47.1% of Cr with still high recovery of 85.8 %.
Table 1. Separation of Cr ore using standard sorting algorithm with setting „a”
Fraction Weight in [%] Cr content in [%] Cr recovery in [%]
Feed material 100 37.1 100
Waste fraction 13.4 20.6 7.4
Product fraction 86.6 39.7 92.7
The waste stream had a similar Cr content as in the previous case (22.5%), howev-er, its recovery was higher (14.3%). Despite the higher losses in the waste stream, the product could be sent directly to the metallurgical process without any pro-cessing. The small amount of the waste fraction could be processed further to enrich the Cr content. This means only 14% of the material was necessary to enrich by the standard processing methods.
Table 2. Separation of Cr ore using advanced sorting algorithm with setting b”
Fraction Weight in [%] Cr content in [%] Cr recovery in [%]
Feed material 100 40.7 100
Waste fraction 25.9 22.5 14.3
Product fraction 74.1 47.1 85.8
Another application example is shown on Figure 4, where the copper ore was pro-cessed in the sorting unit. On the left side four particles are illustrated, where the content of Cu was different 0.2-2.2%. These particles are analysed by the pattern recognition system (right side) and the pattern parameters are provided near the particles for presentation. These parameters (A1, A2, B1, B2, C) describe the identi-fied areas having different patterns. By using these parameters as sorting criteria, it was possible to divide the material stream to various particle types which turned out to have different Cu content. These streams can be treated differently in further pro-cesses, thus providing optimal flotation process.
The sorting system was also tested for enrichment of the Zn-Pb ore. The results are shown in Table 3. The CXR X-ray sorting unit was compared with the traditional dense media separation [4].
Table 3. Separation of Zn-Pb ore using advanced sorting algorithm in the CXR sorter
Fraction Weight [%] Zn grade [%] Pb grade [%] Zn recovery [%] Pb recovery [%] Feed material 100 1.8 0.8 100 100 Comex product 13 10.95 5.42 79 87
The results show that the Zn-Pb ore can be alternatively processed in the Comex sorter with similar separation effect. The input ore having 1.8% Zn and 0.8% Pb, provided 13% fraction having 10.95% Zn and 5.42 Pb. The recovery of Zn was at 79% and Pb at 93%. The traditional dense media separation provided higher recov-ery, however, lower grades. The Comex sorter could be adjusted to provide a similar recovery, however, the presented results were more attractive for further processing in flotation. It was less material volume to process and higher grades were present at the inlet to the flotation process.
Fig. 4. Image of the copper ore particles having different metal content, recognised by different patterns in the sorting unit
7. Main benefits
Sensor based sorting can generally provide clear advantage, when different impuri-ties can be removed from the process in the early stage. This can reduce the gener-ation of fine slimes as waste outputs and eliminate or reduce water consumption. Quality of the product can be increased and the overall energy efficiency of the pro-cessing plant can be improved.
The sorting process can be applied as the alternative to the dense media separa-tion, where the similar process can be replaced by dry sorting without use of any water, weight materials, pumps, conditioners, recuperators, etc. Based on experi-ence the operating cost of the sorting process, as described above, ranges between 0.1 - 0.2 EUR per ton of the processed material. This cost includes all components related to energy consumption, service cost, labour etc. This is very competitive to the dense media separation, where this type of cost is about 2 - 3 EUR/t. In addition water consumption can be eliminated, which has the positive environmental impact. Finally, the sorting process can have another important role in the processing system. When installed at the input stream to the processing plant, it can provide full scanning of the material going into the plant. This information can be very valuable to optimise the operation of different processes. Comex sorting machines have sev-eral output signals, which can be used for such purposes.
Conclusions
New multiple sensor sorting provides new potential for continuous separation of particles having different size, colour, shape, texture and density. Application areas for such equipment are almost unlimited (minerals separation, recycling of metals, plastics, paper, rubber, wood etc.). It can be applied to the mineral industry in many different processing steps. Very significant savings are achievable when the sepa-rated material, representing rejects, can be removed from the process in the early stage. Additionally, important contamination particles can be removed as well. Different processing configurations of the signals from the sensors, allow for new adjustments of the sorting parameters, providing advanced particle structure analy-sis including X-ray and optical images. This may result in new possibilities for opti-mising the existing circuits and for making new installations more economical.
References
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