* MBA Polymers, 500 W. Ohio Ave., Richmond, CA 94804, net, , FAX:

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1 Value Added Color Sorting of Recycled Plastic Flake from End-of- Life Electrical and Electronic Equipment B. L. Riise*, L. E. Allen*, M. B. Biddle*, and M. M. Fishers * MBA Polymers, 5 W. Ohio Ave., Richmond, CA 9484, briise@eurthzink net, , FAX: g American Plastics Council, 13 Wilson Blvd., Ste. 8, Arlington, VA 2229, mike_fisher@plastics.org, , FAX: ntroduction The properties of products made from recycled plastics are in part determined by the level and types of impurities found in the primary plastic. The removal of other plastics is essential if the properties are to approach those of virgin resin. Purification of plastic resins is achieved by exploiting differences in material properties of the different plastic types. Differences in density and surface properties have been shown to allow for the separation of a number ofplastic iak. *2 n many cases, streams of recycled plastic are composed of flakes of a wide variety of colors. The flakes can also range from clear to opaque. Separation of plastics into groups of similar colors can greatly increase their value because they can be colored to meet reasonable specifications much more easily. Sometimes, the different colors might correspond to different types of plastics, so their separation is desirable. For these reasons, the sorting of plastics based on color may prove to be a valuable separation technique for recycled plastic flakes. Fortunately, equipment is commercially available which can sort granular materials based on color. Such equipment has been utilized in the food. and plastics4 industries. n this study undertaken jointly by the American Plastics Council and MBA Polymers, color sorting was applied to the separation of white, gray and black plastics from end-of-life electronic equipment. The composition dependence of sorting was investigated experimentally and compared with a theoretical model. Results indicate that increased levels of impurities decrease the throughput rate and result in increased impurity levels in both product and reject streams. The removal of black from a mixture of black and gray flakes was also demonstrated as a way to control product color. 1. Background The color sorter used in this study is a scanmaster 1 manufactured by- Satake. This unit separates materials based on the gray scale intensity of falling particles relative to a background intensity. The colors of the particles are made dark or light relative to other colors and a background by selection of appropriate light sources and filters. For example, red appears the same as white under a red filter but the same as dark gray under a green filter. One can thus separate flakes based on how light or dark their colors appear with a certain combination of lights and filters. The user can decide whether to pneumatically eject particles that are darker ( dark trip ) and/or lighter ( light trip ) than the background. n addition, the user can decide how much lighter or darker than the background the particle must be in order to be ejected. Fig. 1 is a schematic representation of five particles against such a background. Particles A and B can be ejected with a light trip, although ejection of B would require a high sensitivity. Particle C is the same shade of gray as the background, so it cannot be ejected. Particles D and E can be ejected with a dark trip, although a greater sensitivity would be required to eject D. The lightness of the background is controlled by physically changing the background color (white, black and various other shades are available) or by rotating the angle of the background relative to the light source and detectors /1/$1. 21 JEEE 223

2 Fig. : Ejection of particles lighter or darker than the background ejected with ejected with ligfif trio not ejected dark trip A B C D E The color sorter is composed of several charge coupled device (CCD) cameras which observe particles as they slide down a number of grooves in the color sorter. Each CCD camera has five channels cwresponding to invididual grooves. Signals fiom the CCD cameras are processed by a computer which decides whether the particles are too light or dark and whether or not they should be ejected. The sorting can work in either reflective mode or translucent mode. The choice of mode depends on whether the flakes are clear or opaque. The reflective sort mode employs both front and rear lamps, detectors and backgrounds, as shown in Fig. 2. This mode allows the removal of flakes that appear lighter and/or darker than the background. Materials are often opaque, but clear samples can also be ejected if they appear lighter or darker than the background. The unit can be run in translucent sort mode merely by changing a setting on the computer. The setting shuts off one pair of lamps, allowing optically clear particles to appear much brighter than the background. This mode is useful for removing clear materials fiom opaque materials. When a light (or dark) particle is detected in a particular channel, a signal is sent to a pneumatic ejector which ejects the particle with a blast of air. This air blast occurs a few centimeters below the CCD camera detector. Because of differences in size, shape and frictional properties of particles, falling velocities may vary. t is therefore important to tune the delay time (the time between detection and the air blast) and dwell time (the duration of the air blast) in order to remove as many of the undesirable particles as possible. There are tradeoffs, of course, since a longer dwell time causes the ejection of a larger number of good particles in addition to the reject particles. Fig. 3 shows a schematic of the probability (P(t)) that a reject particle detected at time=o passes the pneumatic ejector at time t. The shape and sharpness of this curve depend on the color sorter throughput rate as well as the particle size, shape and frictional properties. When bad flakes are ejected, good flakes are also carried with the ejector air. n order to reduce the total amount of good flakes going to the reject stream, many color sorters contain a recycle loop. Only particles ejected twice are sent to the reject stream R Fig. 4 is a schematic flow diagram of a color sorter with such a recycle loop. Fig. 2.- Schematic diagram of the Scanmaster 1 in the reflective sort mode. illuminated falling particles amput-. n = P4 delay \ejected. -1 A. J Fig- 3.- Probability that a reject particle will pass ejectors at a time t. Typical timing parameters ( delay and dwell are shown. dwell n the following sections, we describe the performance of the color sorter for two particular separations. Since many of the plastics in electronics 224

3 equipment are white, gray or black, we concentrate on separations of white from black and the removal of black from gray. The experimental details and results are separated into sections describing the particular type of separation. Fig 4: Flow diagram of color sorter with a resort F were mixed into black plastic and removed using a single pass through the color sorter (no resort loop). This simple separation with feed rate F, product flow rate P and reject flow rate R is shown in Fig. 5. These experimental results are then compared with a model derived in the Appendix. The model predicts how the impurity compositions in the reject stream (ri) and product stream (pi) depend on the feed impurity composition (fi), the fraction of detected reject particles that are actually removed (+) and the number of particles that are ejected during each ejection (N). The model also relates the reject flow rate to the feed flow rate (R/F). The main results from the model are equations (), (2) and (3). q=(l-$)j+- # N P R U Composition dependence of the removal of white from black A. ntroduction n many of the existing applications of color sorting, the amount of impurities is very small. n addition, there may be very little change in impurity composition over time. n the separation of engineering thermoplastics, however, the composition of a particular color can vary fiom trace quantities to lw?. One of the big uncertainties in color sorter performance is how well it will handle a wide variety of feed materials with a variety of impurity compositions. Variations in composition will change material throughputs by changing the proportion of material ejected. Fig. 5: Flow diagram of a simplified color sorter with no secondary sort r-7 F, 4 primary sort n order to experimentally investigate the effects of composition, various amounts of white plastic -- R - f, 9 N + -# +(# -$)A (3) B. Experimental We prepared 3 kg plastic flake samples with various amounts of white flakes as an impurity in black flakes. Only one half of the primary sorter was used in order to accommodate this relatively small sample size. The samples were color sorted with a light trip against a black background. The same sensitivity, feed rate and ejection timing were used for all the samples. After purging the previous sample material, samples were collected from both the product (p) and reject (R) streams for between 3 and 6 seconds. The timed samples were weighed to determine P and R The compositions 4, pi and ri were determined by hand sorting 3 g portions by color into white and black. C. Results Fig. 6 is a plot of the mass fiaction of white flakes in the reject as a function of feed composition. According to equation (l), this plot should be linear with a slope of (1-+/N) and an intercept of +/N. The best fit of this equation is when +/WO. 15. Fig. 7 is a plot of the mass fraction of white flakes in the product as a function of feed composition. 225

4 According to equation (2), this plot should be linear and through the origin, The best fi of this equation when +/N=.15 is for F.996. This suggests that 99.6% of the detected reject particles are ejected and that about 6 or 7 flakes are ejected per ejection (N4.7) Fig. 6: Reject composition as a function of feed composition becomes large enough to drastically reduce the production rate P once the impurity composition in the feed is above 5 or 1% L ##- :,,+#e* f*.8...,....,....,....,.... :. m : ~ - t. jo d bte Fig. 8 is a plot of the ratio R/F as a function of feed composition. The experimental data compare favorably with the model when $/N=.15 and po.996 are substituted into equation (3)..2 - *,4= :me -.ai*. 9-*,a,.- Based on the measured throughput of 55 bhr per chiinnel and the average mass per particle, roughly.1 to.2 particles pass the ejector per milllisecond of dwell time. With a dwell of 1 milliseconds, we expect N to be between 1 and 2. The higher number (Na.7) may be due to the ejection of glossy black particles that appear lighter than the background. u. Preparation of dark and l it plastic prodncts from a mixed feed A. ntroduction Streams of plastic from electronic equipment often occur as mixtures of gray and black plastic flakes. R some cases, the value of the product can be increased if the material is sorted into a product stream with very little black material and another product stream with very little gray material. n the gray stream, any black material will make the product pellets a darker gray. This color may be fine for many applications, but it prevents the material from being colored with any color other than black (to make dark gray). n the primarily black stream, the presence of Ti2 (as occurs in gray plastic) makes it difficult (or impossible) to make the plastic black. Since many buyers prefer a black material, it is important to remove as much gray as possible from the black product. B. Experimental A typical lot of flake material was passed through the color sorter twice. Dark colors (black and dark gray) were ejected into the reject stream. The feed material, the product fiom the first pass, the product from the second pass and the reject from the first pass were collected for color analysis, pelletization, injection molding and ash tests (to determine Ti2 loading). Color analysis consisted of categorizing all material in a gram sample of flakes as either light or dark. The light material included gray, 226

5 clear, white and colors. The dark material included black flakes. Ash tests were performed on portions of test specimens that were molded fiom pelletized flakes. C. Results Table 1 gives the weight fractions of light and dark and the ash content in the four samples. Table : Analysis of color sorted flake material Feed Product1 J product Reject These results indicate a sharp improvement in light composition with the first pass, and a smaller improvement with the second pass through the color sorter. The amount of TiOl in the first pass reject is roughly U3 of the amount in the feed stream. This is close to the ratio of % light in the two streams. The reject will require significant cleaning (with low throughput rates) to remove enough of the Ti2 to allow it to become black. As shown in Fig. 9, the products are lighter and the reject darker than the feed material. There is little visual difference between the first and second pass products. There is a slight difference in the?! light and % solids, however. These results suggest that color sorting is a useful tool for the production of light and dark streams from a single material stream. A single pass can eliminate about 8% of the dark flakes from a primarily light material. Further purification also helps with the purity, but to a lesser extent. The reject stream is enriched in the dark material. Purification of this stream will be slow, especially if nearly all of the light material must be removed. Fig. 9: Molded specimens of feed, products and rejects from the color sorter. i V. Conclusions Separations based on color can be important for a number of reasons, including the improvement of product quality or a more consistent product color. n this study, color sorting was applied to the separation of white, gray and black plastics. Results for the removal of white from black plastic flakes are compared with a model predicting the feed composition dependence of product purities and throughputs (see Appendix). Both the experimental results and the model indicate that increased levels of feed impurities decrease the product throughput rate and result in increased impurity levels in both product and reject streams. Another set of experiments demonstrated the creation of light and dark products by removing black flakes from gray flakes. Extrusion of the feed material and products showed that one product is lighter, and the other darker, than the feed material. V. Appendix A model for product and reject compositions and throughputs as functions of feed composition When a particle is significantly darker (or lighter) than the background, a computer signals a pneumatic ejector to fire a blast of air beginning at a time delay past detection for a duration of time dwell. We assume that all particles are the same weight so that number fractions can be equated to mass fractions. Though this is not the actual case, the results should be qualitatively correct. The impurity particle, once detected, is ejected with a probability 9. This probability depends on the flow of particles and is a function of both delay and dwell. f the delay is properly selected, is expected to increase with dwell and approach a value of unity. Other particles are also ejected along with impurity particles causing the ejection. The number of particles per ejection N is given by where V is the number of particles passing the ejector per millisecond (if dwell is in units of milliseconds). The total number of impurity particles Ni ejected during an ejection includes both the primary ejected particle and a random number of other impurity particles (proportional to the impurity composition in the feed). 227

6 The reject impurity composition ri is therefore given by For a given set of ejection parameters, r, should be linear with the feed composition. Plotting ri as a fimction of fi should give a slope of l+" and an intercept of +/N. The parameter $/N tells us what fraction of the ejected particles are actually the recognized ejected particle. This parameter would equal one if no extra particles were ejected. We would also like to relate the impurity concentration in the feed to that expected in the product. For relatively small levels of impurity in the feed, we assume that the detectors can detect all impurity particles and that the ejectors can fire for each particle detected. n this case the only impurities reaching the product are 14 for each ejection. The impurity composition in the product is equal to this amount divided by the total amount of particles that are not ejected WJ. Pi -# NP This number Np is the average number of product particles between each ejection event. N, can be related to N and the flow rates F and R as follows. YP(f-1). A species balance for i yields an equation for F/R as a hction of compositions in the various streams. of rj versus fi) should give the fraction Q of particles that are detected and ejected. This number should be close to one. One can also express the ratio of the reject flow rate to the feed rate (UF) as a function of feed composition. Combining the previous equations gives the result. R f. N'- The ratio UF is thus a function of the feed impurity composition and parameters and N (which are associated with the color sorter settings). References 1. Morley, N., Polymer Recycling, 3 (3), , Biddle, M. B. and Fisher, M. M. Proceeedings of the SP 22" Annual Conference, Structural Plastics Division, Washington, DC, Larson, B. E. and Robe, K., Food Processing, USA, 45 (5) 174,176, Satake, S., to, T. and keda, N., US Patent , Zhang, M., Ludas, L.., Morgan, M. T., Krutz, G. W. and Precetti, C. J., Proceedings of 1998 Precision Agriculture and Biological Quality (SPE), , Schut, J. H., Plastics Technology, 38 (lo), 15, Bain, D. R., Botje, G. and Scholt, J. C., Proceeedings of the Recycle '94 Conjerence, Satake, USA, Houston, TX, wwwsatake-usacom. The previous three equations can be combined to yield an equation for pi that is Linear in 4. The product impurity concentration can be plotted as a function of the feed impurity composition. The slope (combined with +/N determined from the plot 228