Quantitative Parameters to Evaluate Mixing in a Single Screw Extruder

Transcription

1 Quantitative Parameters to Evaluate Mixing in a Single Screw Extruder By Kiana Kalali Degree of Master of Engineering Department of Chemical Engineering McGill University Montréal, Québec, Canada March 2011 A thesis submitted to the Graduate and Post-doctoral Studies Office in partial fulfillment of the requirements for the degree of Masters of Engineering Kiana Kalali, 2011 All rights reserved

2 Contents Abstract... 1 ABRÉGÉ... 2 Acknowledgments General Introduction Main types of Mixing Distributive and Dispersive Mixing Mixing measurement methods Interface growth and flow pattern Mixing in a single screw extruder References Residence time distribution Introduction Strain distribution in the extruder Modeling Experimental section Material Equipment Experimental Procedure Calculation of experimental RTD functions Results and discussion Experimental verification of the models Conclusion References Image analysis Introduction Theory Experimental section Material Equipment Experimental procedure

3 3.4 Results and discussion Conclusion References General Conclusion Summary Future Work Appendix Calculating viscosity and Power-law exponent (n) Matlab codes Sample Images

4 Abstract Mixing is crucial in most polymer processing operations towards obtaining high-quality products (e.g. tubing, tire treads and wire coverings). Material type, screw design, and processing conditions all affect mixing profoundly. Different types of mixing elements have been developed to improve mixing in the single screw extruder; however, the selection of these mixing elements is not trivial. In this work, our purpose is to provide quantitative tools to select the best mixing element. Residence time distribution (RTD) and image analysis were used to compare mixing in three different mixing elements: single flight, Maddox, and Saxton. Residence time distributions were used to indirectly grasp an insight about the strain distribution inside the extruder. Experimental RTD data were derived from silica tracer studies and compared to various mixer models. A model based on a plug flow mixer in series with two continuous stirred tanks best fit the experimental data in all three different mixing elements. For the image analysis method the degree of mixing was determined. Mixtures of polyethylene resins with carbon black were extruded and sliced. Subsequently, sliced samples were scanned to provide images showing the distribution of the carbon black in the resin. The RTD experiments showed that the mean residence time is highest in the Saxton mixer and the lowest in the single flight element. Also, the RTD was broadest in the Saxton mixer and narrowest in the single flight mixer. This means that the polymer in the Saxton mixer experiences the widest range of strains and gets mixed more thoroughly. These results were confirmed by image analysis, which showed that polymers mixed in the Saxton mixer were more homogenously mixed compared to the two other mixing elements. 1

5 ABRÉGÉ Les phénomènes de mélange dans les extrudeuses monovis ont été étudiés en détail depuis de nombreuses années. Le défi principal est le choix des éléments de mélange les mieux appropriés pour une tâche de mélange donnée. A ce jour, les fabricants d'équipements de mise en oeuvre des matières plastiques s'appuient fortement sur des données expérimentales et leur expérience pour opérer ce choix. Dans ce travail, notre objectif est de développer des critères d'évaluation quantitatifs pour différents éléments de mélange dans les extrudeuses monovis. A cet effet, nous comparons l'effat de mélange dans trois éléments de mélange différents (à savoir une zone de vis à filet simple, une zone de mélange à filet barrière de type Maddock, et une troisième zone à picots de type Saxton) en utilisant la distribution du temps de séjour et l'analyse d'images. Pour la distribution du temps de séjour, une matière de traçage est injectée dans la machine, et sa concentration dans l'extrudat déterminée par la pesée du résidu solide des échantillons. Pour l'évaluation optique du mélange, des images de copeaux d'échantillons étaient analysées au moyen d'un logiciel Matlab. Donc, les différents éléments de mélange sont caractérisés, pour les matières utilisées dans cettte étude (des polyoléfines), par la distribution du temps de séjour et la qualité de mélange obtenue par analyse d'image. Mis ensemble, nos résultats confirment que la qualité de mélange obtenue est directement liée à la distribution du temps de séjour; le meilleur résultat est obtenu avec le mélangeur de type Saxton. Les deux paramètres peuvent être utilisés non seulement pour l'évaluation, mais aussi la prédiction de l'effet de mélange dans d'autres conditions et configurations. 2

6 Acknowledgments I would like to thank my supervisor Prof. Milan Maric for his advice and guidance and his support through the project. Without his help I would not be able to finish this project. I also would like to thank Maillefer Company for sponsoring this project and for letting me use all the laboratory equipments needed for this project. Special thank to Dr Schläfli from R&D department I cannot thank him enough for his advice and guidance and for his great efforts to support me through the project. I would have been lost without him. I must also acknowledge Dr Rakhshanfar who has guided me through Matlab coding and image analysis. I wish to thank all my group mates who have helped me with working in the laboratory. I wish to thank my family for helping me get through the difficult times, and for all the financial and emotional supports and in particular, I must acknowledge my mother, Nayer Moghimi, and my sister,katayoon Kalali, without whose love, encouragement, I would not have finished this thesis. I thank the technical staff of the Department of Chemical Engineering for their help in understanding the workings of the building and the proper safety protocol to follow which helped reduce the risk of accidents in the laboratory. 3

7 1 General Introduction Mixing, defined as a process in which various components are subdivided and distributed throughout the entire volume of a system, plays a vital role in almost every polymer processing operation. Nowadays the need for developing new polymeric materials with improved properties relies more on mixing existing polymers and additives together than on the synthesis of chemically new polymers 1. To develop improved materials, different additives and reinforcing agents are mixed with polymers; the aim of mixing is to produce a uniform mixture of polymers and these additives 2. Thus, efficient mixing in extrusion is essential in obtaining a consistent, high-quality extruded product. Basically, the reduction of the non-uniformity is accomplished by inducing physical motion upon the ingredients in the mixture. There are three different mixing mechanisms according to different types of motions: diffusive mixing, turbulence mixing, and convective mixing. Diffusion and turbulence are limited to low viscosity materials. The high viscosity of polymer melts suggests that convective mixing is the major mechanism for polymers. Diffusion is the spontaneous spreading of particles without any external driving forces which is critical in the mixing of gases and low viscosity liquids. In most polymer mixing operations the mixing time is too short and therefore the contribution of diffusion is negligible 3. Turbulent flow is the motion of a fluid having local velocities and pressures that fluctuate randomly. The criterion to achieve turbulent flow depends on the Reynolds number. N Reynolds = (Density Velocity Diameter)/ Viscosity Generally if the Reynolds number exceeds 2000, the flow will be turbulent 4. Although it is an effective mechanism for mixing, the viscosities of polymer melts are too large (in range of 100 to 100,000 Pa.s) 3 to achieve a sufficiently high Reynolds number. The exceedingly low Reynolds number in polymer processing dictates the flow is laminar. Convective mixing, a predominant mixing mechanism in polymer processing, is induced by imposing laminar shear, elongation (stretching), and squeezing deformation on the mixture. Convective mixing is 4

8 generally achieved by pressure flow caused by a pressuregradient, or drag flow created by a moving boundary, or a combination of both. Both of these processes occur in screw extruders, the most ubiquitous piece of processing equipment used in the polymer industry. 1.1 Main types of Mixing Mixing operations are often categorized as a function of the state of the materials which are to be mixed together. If the materials to be mixed are both solids, solid-solid mixing occurs. This type of mixing becomes important when both the polymer and the filler are in powder form, such as the inlet to the extruder 3. More frequently solid fillers are mixed with the molten polymers; this type of mixing is termed solid-liquid mixing. Another important mixing type in polymer processing is mixing of two or more polymer melts; this type of mixing is known as liquid-liquid mixing. In some cases, the gas is mixed with the polymer melt, which is referred to as liquid-gas mixing such as in a foaming operation Distributive and Dispersive Mixing It is quite common to classify a mixing operation by the physical properties of the components involved. According to this classification two types of mixing, distributive and dispersive, are distinguished. Distributive mixing occurs in systems with negligible cohesive forces. In this type of mixing interfacial area between the components is increased by stretching and components are spread throughout the volume. Therefore, in distributive mixing, shear stresses are irrelevant to the mixing mechanism and it can be described by the extent of deformation or strain that is experienced by the components. Distributive mixing can take place in the solid as well as liquid state. When solid particles are mixed without any changes in particles sizes, distributive solid-solid mixing has occurred. When a particulate solid is mixed with molten polymer without any reduction in the particle size, distributive solid-liquid mixing has occurred. Furthermore, mixing of two miscible polymer melts is an example of liquid-liquid distributive mixing. 5

9 Dispersive or intensive mixing, causes the reduction of the size of a component which is being held by cohesive forces. In dispersive mixing, the minor component needs to be broken down, but this happens only when a sufficient degree of stress is applied to the components, such as clusters of solid particles (agglomerates) or droplets suspended in a liquid. Similar to distributive mixing, there are different types of dispersive mixing such as solid- solid dispersive mixing, solid- liquid dispersive mixing, and liquid-liquid dispersive mixing. An example of dispersive mixing is the mixing of carbon black agglomerates into a polymer by which the agglomerate size has to be reduced below a certain minimum size to achieve good surface quality of the final product. The main difference between distributive and dispersive mixing is the role of the local stresses applied on the materials. Thus, the measurement of the stress, strain, and interfacial area could be used as key indicators of the state of mixing Mixing measurement methods To achieve a complete characterization of the state of mixture, it is required to specify the size, shape, orientation, and spatial location of every particle of the minor component. Generally, mixtures are characterized by gross uniformity, texture, and local structure. Gross uniformity means measuring quantitatively the distribution of the minor component throughout the system. Perfect gross uniformity is indicated by identical concentrations formed in all samples taken from a system. In a random mixing process, the maximum uniformity attained is given by the binomial distribution 5b. 5c Consider a sample, randomly extracted from the mixture containing n particles, where n is large enough for statistical treatment and small enough compared to the total number of particles in the mixture. When the fraction of minor particles in the entire mixture is p, then b, the probability that this randomly selected sample has exactly k minor particles, is given by the binomial distribution 5b. b k; n. p = n! k! n k! pk (1 p) n k (1) As shown in equation (1) the distribution of the minor component depends on both the average concentration of the minor component, p, and on the size of the sample, n. This is clear by the definition of the variance of the binomial distribution given below 5b. 6

10 σ 2 = p (1 p) n (2) For a reducing in the non-uniformity in the mixture, the random distribution of minor components is vital. This means that the probability of appearance of the minor component at any place in the mixture must be constant. The best quality of the mixture occurs when the probability of finding a particle of any component is the same at all positions in the mixture. When the materials to be mixed have different physical properties, segregation occurs. In a segregated mixture, the particles of one component preferentially appear in one part of the mixture more than in other parts. The distance between the mixing state of each sample and that of the statistically random one is measured by the degree of mixing 5b. The degree of mixing, M, can be written as: M = σ 2 S 2 (3) where σ 2 is the variance of the perfectly random sample (binomial distribution) and s 2 is the experimentally measured variance of the samples expressed by Equation (4): S 2 = 1 N 1 N i=1 (x i x ) 2 (4) In Equation (4), x i is the volume fraction of the minor component in the test sample i. It is obvious that for a perfect random mixture M = 1 5b. The state of mixing should improve with time, so it is possible to monitor the rate of mixing by measuring the mixing index at various times. The most important particle property that affects the tendency to segregate usually is the particle size. Particles > 75µm in size have a strong tendency to segregate; particles 75 µm in size usually show little tendency to segregate. In the intermediate range, as the size of the particles decreases, the tendency toward segregation will reduce 3. As mentioned, difference in particle sizes is the main reason for getting segregation and, therefore, making the particle sizes as uniform as possible can reduce the segregation. In distributive solid-solid mixing making particles as small as possible is an appropriate approach to 7

11 reduce the segregation, while dispersive mixing involves the reduction in size of a cohesive component. Mixtures can also be characterized by texture as well as gross uniformity. The term texture means that some compositional non-uniformity is reflected in patches, stripes, and streaks 5b. There are two parameters that characterize the texture of samples: scale and intensity of segregation. The texture of the mixture in relation to the quality of mixing depends on the combination of both scale and intensity of segregation. However, mixing can only influence scale of segregation. This is because the reduction of intensity of segregation occurs only by diffusion, which is largely absent in mixing of polymers. Regarding scale of segregation, the size of undistributed portions of the components can be reduced by supplying mechanical energy. Turbulence, de-agglomeration and re-combination, fluid deformation by stretching or kneading, and shear are the mechanisms used by mixing devices to reduce the scale of segregation 5b, c. To measure the size of the undistributed portion of the components, the coefficient of correlation, R(r), is used 5b. R(r), measures the degree of correlation between the concentrations at two points separated by distance r. This is achieved by randomly throwing a dipole of length r, and measuring the concentrations of the component in question at each point of the dipole. R(r) is expressed as: R r = N i=1 C i x C (C i x+r C ) N S 2 (5) where C (x) and C(x +r) are concentrations at the two points, C is the mean concentration, N is the total number of couples of concentrations, and S 2 is the variance given by : S 2 = 2N i=1 C i x C 2N 1 2 (6) The values of the coefficient of correlation vary in the range from -1 to 1. It is -1 when the origin and end of the vector r lie in different axes. The coefficient equals unity when both the origin and the end of the r vector lie in the same axis. Finally, it is zero when the correlation is random or, equivalently, when knowledge of the composition at the origin provides no information about the composition at the end of the vector 5c. 8

12 The scale of segregation is the integral of the coefficient of correlation from distance r = 0 (R (0) = 1) to distance ζ at which there is no correlation (R (ζ) = 0): ζ S = R r dr 0 (7) One of the techniques to gauge the quality of the mixing is the measurement of the variance. To measure the variance a number of small samples from the mixtures are extracted and consequently the composition of each sample is determined. There are different techniques to determine the composition of samples such as light transmittance, electrical conductivity, titration, and particle counting 5c. Using the best technique for the particular material, a large number of measurements easily can be taken and the concentration can be measured accurately. Consider C i as the composition of individual samples and N as the total number of the samples. Thus, the experimental average composition is: C = 1 N i=n i=1 C i (8) The square of the standard deviation of the sample composition, S 2, is the variance which is given by S 2 = 1 N 1 i=n (C i C ) 2 i=1 (9) In homogenous mixtures, the compositions of all samples will be close to the average composition and thus the variance will be close to zero. Tucker showed in his work that the sample variance depends on not only the shape of the sample but also the size of the sample 6. If all the samples have equal size and shape, the sample variance is measured in a straightforward fashion. As mentioned before, the most significant mixing operations in polymer processing is mixing of two or more polymer melts, a type of liquid liquid mixing. In polymer processing, due to the high viscosities of polymer melts, the flow is laminar. Interfacial surface area first was used by Spencer and Wiley to measure liquid-liquid mixing in polymer processing. 7 In mixing of polymer melts, components are generally immiscible, so it is easy to identify the two 9

13 components and their interface. Thus, we can use the increase of interfacial area as a quantitative measure for the extent of mixing. The striation thickness, r, is defined as the thickness of each adjacent layer in a lamellar structure. The interfacial area per unit volume, A v, and the striation thickness are related as follow 5c : A v = 1 r (10) As mixing progresses, the interfacial area per unit volume increases and the striation thickness decreases. Thus, it is possible to calculate the quality of mixing by determining the interfacial area growth, or equivalently the striation thickness reduction. The striation thickness is defined as the total volume divided by half of the total interfacial surface: r = V A/2 (11) If the minor component is initially introduced as randomly oriented cubes of height H and with a volume fraction φ, the striation thickness can be expressed as: r = 2 H 3φγ (12) This equation indicates that striation thickness is inversely proportional to total strain and volume fraction of the minor component. It is clear that for the large particles or in the case of low volume fraction of the minor component, more strain is required to make any significant reduction in striation thickness. As a result, it is more difficult to mix a small amount of a minor component into a major component compared with a mixture of equal concentrations. Consider an element subjected to simple shear flow which is determined by three directional angles, a x, a y, and a z. When the material is sheared with the primary flow in the x direction relative to the y direction, the ratio of the two areas at t 0 + t and t 0 is obtained as follows: 10

14 A A 0 = 1 2 cos α x cos α y γ + γ 2 cos 2 α x 1/2 (13) where cos α x, and cos α y are directional cosines and, γ, is the total strain undergone by material mixed with each other. Equation 13 shows that the interfacial area growth depends on its initial orientation and on the magnitude of the shear strain imposed by the mixer. For large values of γ, Equation 13 reduces to: A 0 A = cos α x γ (14) This equation indicates that the interfacial area growth in a two-component material undergoing large shear deformations is linear with shear strain 5b, c. Due to the total strain being directly proportional to the interfacial growth function, it is considered an important variable for the quantitative characterization of the mixing process. Besides the increase in interfacial surface area, the reduction in striation thickness can be used to evaluate mixing. Mohr, Saxton, and Jepson used striation thickness for evaluating mixing 8. As shown in Equation 11, the striation thickness is inversely proportional to interfacial surface area. Therefore, Equation 14 can be easily re-written in terms of striation thickness instead of interfacial surface area as shown below: S = 1 S 0 1+γ 2 (15) Equation 15 reveals the reduction in striation thickness is inversely proportional to the shear strain; this is plotted in Figure

15 Striation thickness ratio (S/S 0 ) strain (γ) Figure 1-1 striation thickness versus shear strain Figure 1-1 shows that initially, striation thickness is reduced dramatically, however, the striation thickness is reduced more slowly after the first rapid reduction. When the material undergoes shear strains of 80 and greater, the reduction of the striation thickness will be equal to zero. This indicates that the mixing is efficient at first, however, after about ten units of shear, the mixing becomes inefficient. The mixing efficiency changes due to the change in orientation of the element deformed in the shear field. The most favorable orientation, which is normal to the flow direction is obtained initially. Later, as the element is sheared, its orientation will change with shear strain. In the absence of the reorientation, the fluid particles orient just in the direction of the flow which is not the favorable orientation for mixing. Therefore, in absence of reorientation, the mixing efficiency reduces as the shear strain is increased. To avoid inefficient mixing, reorientation is critical. Furthermore, according to Equation 13, at low strains, depending on initial orientation, the interfacial area may increase or decrease. A decrease of interfacial area occurs when the strain imposed on material de-mixes (which occurs when the fluid is sheared in one direction a certain number of shear units, and equal and opposite shear will take the fluid back to its original state) the components 5a-c, 8. As shown in Equation 14, initial orientation plays an important role in interfacial area growth. Maximum increase in the interfacial area is achieved when the initial surface is oriented perpendicular to the flow field. For maximum stretching of the area, the optimal orientation can be calculated by differentiation of Equation

16 da dγ = A 0cosa x cosa y (16) The optimal orientation is achieved when the interfacial area element, along the z axis, is perpendicular to the direction of shear, and the xy plane is at 45 0 to it. A A 0 max = e γ/2 (17) This important result indicates that if an interfacial area element is constantly maintained at 45 0 to the direction of the shear, stretching becomes exponential with shear. This situation is difficult to reach in extruders as there is always a possibility for slip to occur and not all of the particles can face the mixing section in a desirable direction. Therefore, frequent reorientation is desirable. As is shown below, in simple shear flow, reorientation reached by randomizing mixing sections greatly improves the generation of interfacial area. The interfacial area at the outlet of the short mixing section disrupting the simple shear field is given by the following (mixing section produces randomly oriented minor component): A 1 = 1 2 A 0γ 1 (18) where γ 1 is the total shear strain the fluid is exposed to before mixing section. It is assumed that the shear strain in the mixing section itself is negligible. If the simple shear field is restored after the mixing section, the total interfacial area after another exposure to shear strain γ 1 becomes: A 1 = 1 2 A 0γ 1 = A 0γ 1 γ 1 = A 0 ( 1 2 γ 1) 2 (19) and n shear strain exposure of the same magnitude ᵞ1, the total interfacial area will be: A n = A 0 ( 1 2 γ 1) n (20) 13

17 Thus, adding mixing sections that randomizes the minor component increases the interfacial area. In a dynamic mixing device, such as an extruder, it is difficult to design mixing sections with the most favorable orientation, but random orientation is more feasible 5a. 1.4 Interface growth and flow pattern To determine the flow pattern most favorable in terms of interface growth, three strain fields are considered: planar elongation, uniaxial elongation, and simple shear. These are compared in Table 1-1. Table 1-1 The average interface growth which is the integration of all the possible orientation and maximum interface growth as a function of F(ɛ or ᵞ) where ɛ= dl is shown l Type of strain Interface growth function A/A 0 Maximum Interface growth function A/A 0 Average Planar elongation Exp(ɛ) (1/2) exp(ɛ) Uniaxial elongation Exp(ɛ /2) (4/5)exp(ɛ /2), for ᵞᵒ 1 (4/5)exp(-ɛ), for ᵞᵒ 1 Simple shear ᵞ ᵞ/2 According to Table 1-1, it is obvious that in terms of interfacial growth, elongational flows are more efficient than simple shear. As for the identical reduction in striation thickness in comparison with total elongation, higher total shear strain is needed. Therefore, to achieve effective mixing, the interfacial area must not only be stretched, but also reoriented, as well as randomized throughout the volume 5a, 5c. 14

18 To provide an efficient mixing, mixers must have complex geometries. Efficient mixing is defined as the level of distribution of minor component into the matrix, which is a function of both mechanical strain and the orientation of the interfacial area. When a mixer is irregular in geometry, the entrance flows give extensional character. Also, the orientation given by shear flow is disrupted and, consequently, mixing efficiency is increased 5a, Mixing in a single screw extruder Single screw extruders are widely used in the polymer industry. This machine has been extensively used for blending or compounding to produce uniform mixtures of polymers 10. A regular single screw extruder is constructed of a rotating screw which is rotated inside the extruder barrel (Figure 1-2). Extruders can be fed by either a polymer melt or with solid polymer particles. Solid particles are fed into plasticating extruders throughout the hopper; the material is conveyed, melted and pushed forward towards the end of the machine as the screw rotates. Therefore, the most important functions in a single screw extruder are: solid conveying, melting, melt conveying, and mixing. First, polymers are melted inside the extruder. As the solid particles are conveyed in plug flow there is no relative motion between the solid polymer particles. Thus, there is no or limited mixing while polymers are solid. Figure 1-2: A single screw extruder 11 15

19 To analyze mixing in a single screw extruder, the exact flow pattern is required. There are many studies concerning mixing measurements proposing different methods such as weighted average total strain (WATS) 12, the strain distribution by Moher et al 8, McKelvey 13, and Pinto and Tadmor 12, and the Weighted Average Deformation Characteristics ( WADC) 14. The overall understanding about the flow pattern has been provided by monitoring the fluid element through the extruder 15. The mixing capacity of a single screw extruder is weak; therefore, to improve the mixing capacity, mixing elements are designed to provide better mixing 16 16a.In single screw extruders, the incorporation of mixing sections in the melt channel helps shear flow by increasing the pressure drop in the extruder, and so the shear strain is increased. More significantly, the mixing sections reorient the polymer interfaces, reducing the striation thickness in the material coming out of the extruder 9. As mentioned previously, the extent of mixing directly relates to the total strain undergone by fluid elements travelling down the screw channel. The strain history of each fluid element depends not only on the shear rate but also on the time the element stays in the extruder, so the residence time is a key parameter for characterizing the mixing performance of extruders. The residence time is actually a distribution of residence times because the velocity field is not uniform across the channel cross -section, and correspondingly, there is a distribution of strains undergone by fluid elements 5c, 10. Mixing phenomena in single screw extruders have been investigated for many years; many studies, experimental and computational, textbooks and patents on this subject have been published. Currently, for the designer of extrusion equipment, the key challenge is to select the best mixing elements from the different existing mixing elements to accomplish satisfactory mixing. Maillefer Company, a manufacturer of extrusion equipment, who sponsored this project, uses various types of mixing sections in their screws. This company design many mixing sections mainly based on experience and general consideration, but unfortunately the lack of reliable quantitative parameter sometimes results in unsuccessful designs. To avoid this and for providing quantitative values to characterize mixing phenomena in different mixing section, this project aims at establishing a quantitative tool for evaluating different mixing sections. Thus, this thesis proposes the comparison of the three most common mixing sections: one of the most basic 16

20 mixing sections (single flight), one of the most common distributive mixing sections (Saxton) and one of the most common dispersive mixing sections (Maddox) to evaluate mixing in a single screw extruder. The residence time distribution indirectly provides insight about the total strain experienced in the material and this was consequently measured here. Further, an image analyzing techniques was used to evaluate the degree of mixing. This thesis will determine how we can use residence time distribution and image analyzing methods to quantitatively compare different mixing elements in a single screw extruder. 17

21 1.6 References 1. Manas-Zloczower, I., Analysis of mixing in polymer processing equipment. Case Western Reserve University: 1997; pp Rauwendaal, C., Mixing in polymer processing. M. Dekker: New York :, Rauwendaal, C., Polymer mixing : a self-study guide. Hanser Publishers ; Hanser/Gardner Publishers: Munich; Cincinnati, Rauwendaal, C., Mixing in polymer processing. M. Dekker: New York, (a) Rauwendaal, C., Polymer extrusion. Hanser Gardner Publications: Cincinnati, OH :, 2001; (b) Tadmor, Z.; Gogos, C. G., Principles of polymer processing. Wiley-Interscience: Hoboken, N.J. :, 2006; (c) Baird, D. G.; Collias, D. I., Polymer Processing: Principles and Design. Wiley-Interscience: NY, 1998; (d) Manas- Zloczower, I.; Tadmor, Z., Mixing and compounding of polymers : theory and practice. Hanser Publishers ; Distributed in the USA and Canada by Hanser/Gardner Publications: Munich ; New York : Cincinnati :, Tucker, C. L., Sample variance measurement of mixing. Chemical Engineering Science 1981, 36 (11), Galaktionov, O. S.; Anderson, P. D.; Peters, G. W. M.; Meijer, H. E. H., Mapping approach for 3D laminar mixing simulations: application to industrial flows. International Journal for Numerical Methods in Fluids 2002, 40 (3-4), Mohr, W. D.; Saxton, R. L.; Jepson, C. H., Mixing in Laminar-Flow Systems. Industrial & Engineering Chemistry 1957, 49 (11), D. I. Bigio, J. D. B. L. E. D. W. G., Mixing studies in the single screw extruder. Polymer Engineering & Science 1985, 25 (5), Kim, S. J.; Kwon, T. H., Measures of mixing for extrusion by averaging concepts. Polymer Engineering & Science 1996, 36 (11), Maric, M., single screw extruder Chemical Engineering 584, p. P., Lecture7- Extrusion, Ed Pinto, G.; Tadmor, Z., Mixing and residence time distribution in melt screw extruders. Polymer Engineering & Science 1970, 10 (5), J. M. McKelvey, Polymer Processing. John Wiley and Sons: New York, Kwon, T. H.; Joo, J. W.; Kim, S. J., Kinematics and deformation characteristics as a mixing measure in the screw extrusion process. Polymer Engineering & Science 1994, 34 (3), Amin, M. H. G.; et al., In situ quantitation of the index of mixing in a single-screw extruder by magnetic resonance imaging. Measurement Science and Technology 2004, 15 (9), (a) Han, C. D.; Lee, K. Y.; Wheeler, N. C., A study on the performance of the maddock mixing head in plasticating single-screw extrusion. Polymer Engineering & Science 1991, 31 (11), ; (b) Hu, G.-H.; Kadri, I.; Picot, C., One-line measurement of the residence time distribution in screw extruders. Polymer Engineering & Science 1999, 39 (5),

22 2 Residence time distribution 2.1 Introduction Large strains and frequent reorientation are key parameters for achieving good mixing. The total strain experienced by a fluid particle is equal to the time integral of its strain rate, and thus, the residence time distribution (RTD) indicates the total strain distribution imposed on the fluid element. It is therefore highly useful to measure the RTD. Besides grasping insight about mixing characteristics of the single screw extruder, the data obtained by the RTD function is of interest for overall performance of the extruder, especially when time is an important element to be considered (eg. reactions, degradation, etc.). RTD results can be used to investigate effects of the different variables such as screw speed, temperature profile, pressure drop, and screw design on the time it takes for the material to exit the extruder. Furthermore, it is possible to directly measure the RTD in single screw extruders under almost normal operating conditions 17. RTD has been extensively studied and some previous examinations of RTD in extruders include the following: theoretical evaluation of RTD functions in melt extruders proposed by Pinto and Tadmor 12 ; and the experimental evaluation of RTDs by Wolf and Resnick 18 ; and the non- Newtonian power law model fluid derived by Bigg and Middleman 19 from an isothermal Newtonian flow model. RTD was measured experimentally in both plasticating and melt extruders by Wolf and White 20. Influence of the leakage flow and radial temperature distribution on the RTD in an extruder was reported by Sek 21, Roemer and Durbin 22, Kim and Skathkov 23, and Tadmor and Klein 24. Almost all analyses that seek information about mixing and strain distribution begin with the definition of a RTD. The amount of time a fluid particle spends in the extruder to move from the hopper (the entrance to the extruder) to the die( the exiting section from the screw) is known as the RTD. The RTD was first introduced by Danckwerts (1953) as a function, f(t), so that the exit age distribution, E(t)= f(t) dt defined as the fraction of exiting fluid which has spent a time between t and t+dt in the system 25. If Q denotes the exiting volumetric flow rate and dq the fraction of it with residence time between t and t+dt, then the RTD function is given by: 19

23 E t = f t dt = dq Q (1) A cumulative RTD function, F(t) can be defined as: F t = t f(t)dt (2) t 0 where t 0 is the minimum residence time. It is obvious that F(t) is the fraction of exiting fluid that has spent a time less than or equal to t. The mean residence time, t, is then equal to: t = t f(t)dt (3) t 0 If the velocity profiles are known in a laminar flow system, the volumetric flow rate can be calculated and consequently the RTD function can be determined. For cases where the RTD cannot be calculated theoretically, experimental methods are used. These methods are based on introducing tracers into the system and recording the concentration of the tracer at the exit of the extruder 5b, c, Strain distribution in the extruder 20 As discussed earlier, the total strain plays a vital role in the quality of laminar mixing. Because fluid elements follow different paths in the extruder and also have different residence times, elements of fluid experience different strains. Strain distribution functions (SDF) are introduced to quantitatively evaluate the fluid element's various strain histories and can be related to RTDs. The SDF, f(γ) dγ, is defined as the fraction of exiting flow rate that experienced a strain between γ and dγ.the cumulative SDF,F(γ), is defined by the following expression: F γ = f γ dγ γ γ 0 (4)

24 where γ₀ is the minimum strain. F(γ) represents the fraction of exiting flow rate with strain less than or equal to γ. The mean strain of the exiting stream is defined as 5b, c : γ = γf γ dγ (5) γ 0 As the parallel plate geometry forms a simple model of melt extrusion, the strain distribution in an extruder as exemplified by the SDF, and it can be examined in parallel plates with combined pressure and drag flow 5b. The derivation for the SDF in a single screw extruder often starts with the following simplification. Assume a Newtonian fluid between parallel plates with a superimposed pressure gradient and drag flow. The flow is fully developed, isothermal and laminar. The velocity profile V z is given as follows: V z = ξ + 3ξ q p q d (1 ξ) V (6) where ξ = y/h (H = gap between the plates), V is the velocity of the moving plate, and q p and q d are the pressure and drag volumetric flow rates per unit width. H q = V 0 z dy = V H 2 (1 + q p q d ) (7) f γ dγ = dq q = 2 1+ q p qd ξ + 3ξ q p q d (1 ξ) dξ (8) 1+3 q p qd (1 2ξ) γ = γ t = L H ξ+3ξ q p qd (1 ξ) (9) 21

25 The strain rate,γ, is derived above and it is indicated by a time derivation of γ, (γ = dγ/dt) F ζ = 1 ζ 2 1+ q p qd 1 + q p q d (3 2ζ) (10) γ ζ = 1+ 3 qp q d 1 2ζ ζ[1+3 q p 1 ζ ] (1+ 2 q d qp q d )γ (11) γ = 2 L H(1+q p q d ) For 1 3 q p q d 1 3 (12) It is clear that the ratio of the pressure to the drag volumetric flow rate plays a dominant role on the SDF and also the mean strain. A positive pressure gradient via pressure increase in the direction of flow (q p /q d 0) will not only increase the mean strain, it will also reduce the breadth of the distribution. Obviously, a negative pressure gradient has the opposite effect. This result supports the experimental observation that an increase in positive pressure gradient in the extruder improves mixing 12. The mean strain, as defined in Equation 12, is proportional to L/H. This means that mean strain depends on geometry, and it is possible to improve mixing by designing long and shallow conduits. As shown in Equation 7, to calculate the SDF we need to know the velocity profile of all fluid elements inside the extrusion channel. Calculating the velocity profile is very difficult; therefore, instead of calculating SDF for analyzing mixing characteristics we used the RTD. This is reasonable as the strain history of each fluid element depends not only on the shear rate but also on the time the element stays in the extruder, so the residence time is a key parameter for characterizing the mixing performance of extruders. Also it is possible to model the RTD experimental results by using the cumulative residence time, F(t), and exit age residence time, E(t), which will discuss later. In order to enhance our understanding about the mixing characteristics of a single screw extruder here in this work we compared mixing performance of three important mixing sections 22

26 (single flight, Maddock, Saxton). As discussed earlier, RTD is an easy and a direct way to study the strain distribution function in a single screw extruder. Therefore, it is used as a method to quantitatively evaluate mixing in these three different mixing sections. Also, we have chosen the most general models among all different theoretical models developed for the RTD curves to suggest the best model for each of the three different missing sections. The best model is defined as the one that provides the statistical best to the experimentally measured RTD. 2.3 Modeling Experimental RTDs are usually analyzed by E(t), and F(t) curves which determine the variation in the concentration of tracer at the exit with time and the cumulative quantity of the tracer at the exit with time, respectively 27. The screw geometry has significant effects on RTD because different screw geometries have different hold-up volumes and mass flow patterns. 28 Therefore, screws equipped with different mixing elements have different RTDs and consequently different models can predict RTDs in different mixing elements. There are many models developed to predict RTD in an extruder. The extruder geometry, shear and velocity profiles were employed by Janssen et al (1979) and Chen and Pan (1993) 28.With the assumption of considering the melt material as a power-law fluid, a model for a two-dimensional flow field in an extruder proposed by Bigg and Middleman (1974) 29. Levenspiel (1972) 30 and Wolf and Resnick (1963) 18 used a series of ideal tank reactors to model the extrusion process. H. De Ruyck developed a model through which the extruder was simulated as a complex reactor including continuously stirred tanks as subreactors 31. The parallel plate and curved-channel flow models was used by Pinto and Tadmor to derive the RTD functions for Newtonian flow in single-screw extruders 12. Chen et al. developed an RTD model based on dividing the screw as a set of individual turns connected in series with the assumption that each turn had a statistically independent RTD 32. Experimental RTD curves of an extrusion process typically consisted of an initial delay time, then a rise in concentration of tracer particle and then followed by a gradual tail. The plug flow behavior refers to the initial delay time while the tail characteristics are indicative of wellmixed behavior. Thus, both plug-flow and well-mixed behaviour are provided by the RTD curves of most extrusion processes. The plug-flow behaviour results in narrow residence time 23

27 distributions which represents good laminar mixing and also prevents the risk of thermal degradation as there is no long-term tail for RTD 5b. The RTD tail represents the amount of over-processing 1 which determines the product quality 28. Here we used the most general models to figure out which model is appropriate to describe the RTD experimental data of each mixing section. The model of plug flow in series with continuous stirred tank reactors (CSTRs) having dead volume fraction proposed by Kumar, Ganjyal, Jones, and Hanna (2007) is used as a model number 1 in this thesis 27. Model number 2 is a plug flow in series with a cascade of CSTRs without any dead volume. This model was first used by Jager et al. (1995) to characterize F(t) curves and later by Singh and Rizvf (1997) to characterize E(t) curves 28. The model of an infinite series of continuously stirred tank reactors which was first proposed by Levenspiel (1972) and later by Davidson, Paton, Diosady, and Spratt (1983) is used as model number 3 here 30, 33. Furthermore, model number 4 is a plug flow in series with a perfect mixer and is known as Wolf-White Model, which was first derived by Wolf-White (1976) 18. Figure 2-1 shows the schematic diagram of the three first models. 1 Over-processing occurs when materials stay in extrusion more than a time required to process them. 24

28 Figure 2-1 Schematic diagram of models 1, 2, and 3 used to describe mixing in a single screw extruder 25

29 To identify the parameter values of the above-mentioned RTD models, a least-squares error-fit method was used. Through this model the optimum values are those that provide the minimum S, which is given as: S Error = n i=1 (y i f x i ) 2 (13) where y i represents experimental data and f (x i ) refers to the given model data. Besides computing each model parameters such as number of CSTRs,n, used in the model,and the dead volume fraction, d, this method gives the best model as the one that has the minimum S when the optimum parameter values are used. The four mentioned models used to model the RTD are described theoretically below. Model number 1: Plug flow in series with CSTRs having dead volume fraction. This model consists of four parameters: mean residence t, fraction of plug flow p, number of CSTRs n and dead volume fraction d. It includes plug flow as well as dead volume. The E (t) curve for this model is derived as following: E θ = b b θ p n 1 n 1! exp b θ p (14) where b is calculated as: b = n 1 p 1 d (15) and the fraction of plug flow is: p = t min t (16) where t min represents the first time the tracer starts appearing at the exit of the extruder. In this model normalized time, θ, was used as following: 26

30 θ = t t Consequently the dimensionless RTD curves are: E θ = E(t) t F θ = F(t) t (17) (18) (19) where t is calculated by means of experimental data. (See equation 30) Model number 2: Plug flow in series with cascade of CSTRs. This model is similar to model number 1 expect that there is no dead volume for CSTRs. This model hast three parameters: mean residence t, fraction of plug flow p, and number of CSTRs n. In this model, the dimensionless time is derived as following: θ = t pt t pt (20) The following equation is developed for this model to calculate E (t): E t = 0 ; for 0 θ p (21) E t = 1 t pt n (θ) n 1 1 n 1! e n(θ) (22) The two parameters n and p are estimated by using the least-square curve fitting model and t is calculated from experimental data. Model number 3: Cascade of perfectly mixed reactors or continuously stirred tank reactors (CSTRs). This model includes two parameters: mean residence t, and number of CSTRs, n. The E(t) is calculated in this model as following: 27

31 E t = 1 t n(nθ) n 1 1 n 1! e nθ (23) Model number 4: Plug flow in series with a perfect mixer. This model is similar to model number two except this model consists of a plug flow mixer in series with a perfect mixer. There are two parameters: mean residence t, and fraction of plug flow p in this model.the F(t) for this model is calculated as: F t = 1 e ( 1 1 p )(1 pθ θ ) ; F t 0 (24) The E(t) curve for this model is plotted by using the following equation: E t = 1 (1 p)t e θ p ( 1 1 p ) ; for θ p (25) E t = 0; for 0 θ p (26) These four mentioned models were used in this work to fit the experimental data grasped out of three different mixing elements: single flight, Maddock, and Saxton. 2.4 Experimental section Different in-line and off-line methods have been proposed in the literature for RTD measurement. An ash containing method based on thermal degradation was used for our experiments. This method relies on weighing the ash residue and was used by Carneiro (2000) with the same tracer, silica 34. Kim and coworkers (2004) used the same method but they used aluminium flakes as a tracer

32 2.4.1 Material A polyethylene extrusion grade resin (Dowlex NG 5056D) was used as received; it was kindly donated by Maillefer Company. A silica gel (Grade 7734, pore size 60 Å, mesh) was bought from Aldrich; it was used as received Equipment The first part of the experimental work, collecting samples, was done at Maillefer Company where a single-screw extruder NMC 45-24D (screw speed 215 rpm max, motor power 29 kw) was used. The three most important interchangeable mixing sections (single flight, Maddock, and Saxton) were used separately. Figure 2-2 exhibits the three mixing elements used in this experiment. The second part, burning samples, was done at McGill University where a Fisher Scientific Isotemp Muffle Furnace, Model , was used to burn the samples and an electronic lab scale was used to weigh the resulting ash residues. Figure 2-2 Three Different Mixing Elements studied: a) single-flight b) Maddock c) Saxton (all with Length=270mm, and diameter= 45mm) 29

33 2.4.3 Experimental Procedure Fluid free of the tracer was extruded until steady output was achieved. After the extruder reached steady state, and temperature was stabilized at 250 C, 100 gr of the tracer (silica gel) was injected very quickly into the polyethylene feed stream and a stopwatch was started. The extruder was operated at 20rpm screw speed and a feed rate of 9 kg/hr. Samples of extruded strips (each weighing around 4 gr) were collected at each 30 sec intervals. This experiment was performed for each of the three mentioned mixing sections. After collecting samples and numbering them, each sample was weighed and replaced in a crucible noted with the sample number. The crucibles were weighted before addition of the sample. Then the weight of crucibles including samples was recorded. The crucibles filled with the samples were placed in the oven and heated up to 550 C for 3 hrs. After heating, the silica gel remained in the crucibles while all the polyethylene parts had been degraded. The tracer concentration was determined by weighing the silica gel residues of the samples. The experimental procedure is schematically shown in Figure 2-3. Figure 2-3: RTD experimental procedure, a plus injection of silica gel into the extruder operating polyethylene, samples were cut numbered and RTD results grasped by weighing the residue of the silica gel after PE were degraded. 30

34 2.4.4 Calculation of experimental RTD functions By using experimental data and applying the following equations the residence time distribution function, E(t),cumulative residence time distribution, F(t), the mean residence time, t, and the variance, σ t 2, were computed 36. E t = 0 C = C dt c c t 0 (27) t F t = E t dt = E t t = 0 0 t t 0 c t 0 c t (28) The concentration, C, represents the relative concentration of silica gel in each sample. In our case different samples have different scales ranging from 3 gr up to 5 gr, thus, tracer concentration, C, was determined with respect to the sample weight and the silica gel residue. C = W R W S (29) where,w R, is the weight of silica gel residue in each sample, and W S is the sample weight. The mean residence time, t, is defined as: t = 0 0 tcdt = cdt tc t 0 c t 0 (30) And the variance σ t 2, is obtained from: σ t 2 = t t 2 E t dt = t t 2 E t t 0 0 (31) The variance of the residence time was used to evaluate the spread of the distribution Results and discussion The experimental RTD results for three mixing sections: single flight, Maddock, and Saxton are shown in Figure 2-4.In this figure the operating conditions and screw speed were kept constant for all three different mixing sections. The mean residence time and the variance for these three different mixing sections are presented in Table 2-1. In addition, the mean residence time and variance of the single flight mixing section and Saxton mixing section at two other screw speeds are shown in Table 2-1. This was done since according to V. J. Davidson 31

35 (1983), operating variables such as screw speed have significant effects on RTDs 33. Figure 2-5, 2-6 and Table 2-1 present experimental RTD results at three different screw speeds: 20, 50, and 80 rpm in two different mixing sections: single flight and Saxton. Figure 2-4 shows the residence time curves for three different mixing sections and each have a similar shape, with the Saxton providing the broadest distribution and the single flight providing the narrowest distribution. This is confirmed by comparing the variances for these three different mixing sections (Table 2-1). As expected the highest variance occurs in the Saxton mixing section and the lowest variance occurs in the single flight. The time at which the tracer first emerges at 20 rpm in the Saxton mixing element is about twice that of the single flight element at 20 rpm. The time at which the tracer first emerges in the Maddock mixing section is more than one and a half times that of the single flight (Figure 2-4). This means that material was exiting the extruder sooner in the single flight element than in either the Maddock or Saxton screw configurations (Figure Error! Reference source not found.). It can be seen that there are noticeable differences between the single flight and Saxton curves in terms of the initial emergence time, height, and spread, while there are no large differences between Maddock and Saxton curves. Only in the tail of the Saxton element is there is significant spread compared to the Maddock element. The mean residence time for the Saxton mixing section is the highest and that of the single flight is the lowest. In the Saxton element, the mean residence time is about one and a half times of that in the single flight. The mean residence time in the Maddock is about 1.3 times of that in the single flight element. Table 2-1 Values of t and δ 2 for three different mixing sections at different screw speeds at Q= 9 kg/hr Screw Configuration Screw Speed(rpm) Mean residence time(min) Variance δ 2 (s 2 ) single flight x 10 5 single flight x 10 4 single flight x 10 4 Maddock x 10 5 Saxton x 10 5 Saxton x 10 4 Saxton x

36 Figure 2-4: Comparison of RTD for three different screw types (N= 20 rpm, Q= 9 kg/hr) As shown in Figure 2-5 and 2-6, the screw speed influences the RTD. As the screw speed increases, the RTD curve becomes narrower. For both the single flight and Saxton elements, the mean residence time and variance decreased with increasing screw speed. In both the single flight and Saxton elements, the reduction in mean residence time and variance is more significant about 52% when the screw speed changes from 20 rpm to 50 rpm. Also there are no significant changes in the mean residence times and variances for the single flight and Saxton elements, as the screw speed change from 50 rpm to 80 rpm. In addition, the initial emergence time is inversely proportional to the screw rotation speed (Figures 2-5, 2-6). For the single flight mixing section, the E(t) curve at 20 rpm ended after 24 min, at 50 rpm, after 14 min and at 80 rpm, after 12 min. For the Saxton mixing section, the E(t) curve at 20 rpm ended after 31 min, at 50 rpm, it ended after 13 min, and at 80 rpm, it ended after 11 min. By increasing the screw rotation speed, more shears was applied to the material and consequently the shear heating increased and materials were melted sooner. Therefore, more slip and more back-leakage occurred which 33

37 results in more axial mixing. The screw speed also affects the flow pattern by altering the holdup volume in each screw configuration, and thus, RTD will change by changing the screw speed. Many researchers studied the effects of screw speed on RTD. Altomare and Ghossi (1986), and Yeh and Hwang (1992) found that increasing screw speed shifts the RTD to the shorter times and decreases the main residence time Ziegler and Aguilar (2003) showed that screw speed linearly affects RTD 37. Figure 2-5: Effect of increasing screw speed on the RTD for single flight mixing section(q= 9 kg/hr) 34

38 Figure 2-6: Effect of increasing screw speed on the RTD for Saxton mixing section(q= 9 kg/hr). Figure 2-7 shows the dimensionless plots of E(t) for three different mixing sections: single flight, Maddock, and Saxton at 20 rpm screw speed and feed rate of 9 kg/hr. E(ɵ) is the dimensionless RTD function and ɵ is the dimensionless residence time (ɵ = t t ). It can be seen that the dimensionless RTD curves of E(ɵ) versus ɵ for the three different mixing sections overlap with each other. This is in contrast with the results found by Xian-Ming Zhang (2008), who showed that when the ratio of feed rate to screw speed is constant for a fixed screw configuration, the dimensionless RTD function curves, E(t) curves, overlap with each other regardless of screw speed and feed rate 38. Although the ratio of feed rate to screw speed was constant here, the screw configuration however was not fixed, the E(t) curve overlap with each other. 35

39 Figure 2-7: The dimensionless RTD of the three different screw types (N= 20 rpm, Q= 9 kg/hr) Experimental verification of the models To determine which of the 4 different models is the best-fit model for the three different mixing sections, Figures 2-8, 2-9 and 2-10 show the fits of experimental data to each of the models. The validity of the four different models for the single flight mixing section was experimentally examined (Figure 2-8). As shown in Table 2-2, the best value for dead volume fraction d in model number 1 according to the least squares method is zero, and therefore, there is no difference between model number 1 and 2. According to the calculated errors for the different models presented in Table 2-2, plug flow in series with cascade of CSTRs (model number 2) is the best-fit model for the single flight mixing section. In terms of initial delay time, a cascade of CSTRs (model 3) was in poor agreement with the experimental data. However, 36

40 model 3fits the data very well in the tail region. Plug flow in series with a perfect mixer (model 4) was the best model in terms of the RTD tail but it disagrees poorly with the initial delay time. Figure 2-8: Experimental RTD curves and models for the single flight mixing section.(screw speed = 20rpm, feed rate = 9 kg/hr) Table 2-2 RTD models parameter values for the single flight mixing section by least square error method, Model 1 has three parameters(n, d, and p), Model 2 has two parameters ( n, and p), Model 3 has one parameter (n) and Model 4 has one parameter ( p) to be calculated by least square error method, errors (S Error )are calculated by applying Equation 13 Models n D P Error (S Error ) % % % % 37

41 Figure 2-9: Experimental RTD curves and models for the Maddock mixing section.(screw speed = 20rpm, feed rate = 9 kg/h( r Figure 2-9 shows the RTD models 1, 2, and 3 are in good agreement with experimental data in terms of the first delay time for the Maddock mixing section. Here again there is no difference between models 1 and 2 as d = 0 (Table 2-3). For the Maddock mixing section, the best fit is also the plug flow in series with cascade of CSTRs with the lowest error (Table 2-3). 38

42 Table 2-3: RTD models parameter values for the Maddock mixing section by least square error method. Model 1 has three parameters (n, d, and p), Model 2 has two parameters (n, and p), Model 3 has one parameter (n) and Model 4 has one parameter (p) to be calculated by least square error method, errors (S Error )are calculated by applying Equation 13 Models n d p Error (S Error ) % % % % Figure 2-10: Experimental RTD curves and models for the Saxton mixing section.(screw speed = 20rpm, feed rate = 9 kg/hr) 39 As shown in Figure 2-10, similar to the single flight and Maddock mixing sections, the best fit model for the Saxton mixer was plug flow in series with cascade of CSTRs (model 2). Compared to the two other mixing sections, the Saxton element has the greatest disagreement with model 3 and in terms of fraction of plug flow, p, in model 4 it provides the largest fraction (Table 2-4). This means that the Saxton mixer shows more plug flow or a perfect mixer behavior compared to the two other mixing sections. This is reasonable as in terms of mean residence time

43 as the Saxton has the highest mean residence time. Also in terms of mixing degree, which will be discussed more throughly in nexet chapter, it will be shown that the Saxton mixer has the highest dgree of mixing. Taking this all together, the Saxton mixing section exhibited behaviour closer to the plug flow than either the single flight or the Maddock mixers. Table 2-3: RTD models parameter values for the Saxton mixing section by least square error method. Model 1 has three parameters (n, d, and p), Model 2 has two parameters (n, and p), Model 3 has one parameter (n) and Model 4 has one parameter (p) to be calculated by least square error method, errors (S Error )are calculated by applying Equation 13 Models n d p Error (S Error ) % % % % 2.6 Conclusion The RTD studies were used to evaluate three different mixing sections: single flight, Maddock, and Saxton. According to RTD curves, the Saxton mixer showed the widest RTD and the narrowest RTD belonged to the single flight element. Also, the Saxton element exhibited the highest mean residence time and the single flight mixing section showed the lowest mean residence time. This means that material was exposed to a higher range of strains in the Saxton mixer and was mixed more thoroughly. This finding is reasonable as the Saxton element has a more complicated geometry with more flow restrictors, and thus, more back mixing occurs in this screw configuration compared to the two other mixing sections. In addition, screw speed affected RTD curves and the mean residence time decreased as the screw speed increased for different mixing sections. This change was more significant for increasing screw speed from 20 rpm to 50 rpm than from changing screw speed from 50 rpm to 80 rpm. Four different models were tested for three different mixing configurations: Model 1, Model 2, Model 3, and Model 4. A plug flow in series with a cascade of CSTRs best fit the experimental data in all three different mixing elements. For all three different mixing sections 40

44 the dead volume fraction d, which elongates the tail in the distribution was equal to zero. The plug flow component of the RTD in model 4 for the Saxton mixer was the highest and for the single flight was the lowest. 41

45 2.7 References 1. Manas-Zloczower, I., Analysis of mixing in polymer processing equipment. Case Western Reserve University: 1997; pp Rauwendaal, C., Mixing in polymer processing. M. Dekker: New York :, Rauwendaal, C., Polymer mixing : a self-study guide. Hanser Publishers ; Hanser/Gardner Publishers: Munich; Cincinnati, Rauwendaal, C., Mixing in polymer processing. M. Dekker: New York, (a) Rauwendaal, C., Polymer extrusion. Hanser Gardner Publications: Cincinnati, OH :, 2001; (b) Tadmor, Z.; Gogos, C. G., Principles of polymer processing. Wiley-Interscience: Hoboken, N.J. :, 2006; (c) Baird, D. G.; Collias, D. I., Polymer Processing: Principles and Design. Wiley-Interscience: NY, 1998; (d) Manas- Zloczower, I.; Tadmor, Z., Mixing and compounding of polymers : theory and practice. Hanser Publishers ; Distributed in the USA and Canada by Hanser/Gardner Publications: Munich ; New York : Cincinnati :, Tucker, C. L., Sample variance measurement of mixing. Chemical Engineering Science 1981, 36 (11), Galaktionov, O. S.; Anderson, P. D.; Peters, G. W. M.; Meijer, H. E. H., Mapping approach for 3D laminar mixing simulations: application to industrial flows. International Journal for Numerical Methods in Fluids 2002, 40 (3-4), Mohr, W. D.; Saxton, R. L.; Jepson, C. H., Mixing in Laminar-Flow Systems. Industrial & Engineering Chemistry 1957, 49 (11), D. I. Bigio, J. D. B. L. E. D. W. G., Mixing studies in the single screw extruder. Polymer Engineering & Science 1985, 25 (5), Kim, S. J.; Kwon, T. H., Measures of mixing for extrusion by averaging concepts. Polymer Engineering & Science 1996, 36 (11), Maric, M., single screw extruder Chemical Engineering 584, p. P., Lecture7- Extrusion, Ed Pinto, G.; Tadmor, Z., Mixing and residence time distribution in melt screw extruders. Polymer Engineering & Science 1970, 10 (5), J. M. McKelvey, Polymer Processing. John Wiley and Sons: New York, Kwon, T. H.; Joo, J. W.; Kim, S. J., Kinematics and deformation characteristics as a mixing measure in the screw extrusion process. Polymer Engineering & Science 1994, 34 (3), Amin, M. H. G.; et al., In situ quantitation of the index of mixing in a single-screw extruder by magnetic resonance imaging. Measurement Science and Technology 2004, 15 (9), (a) Han, C. D.; Lee, K. Y.; Wheeler, N. C., A study on the performance of the maddock mixing head in plasticating single-screw extrusion. Polymer Engineering & Science 1991, 31 (11), ; (b) Wong, A. C. Y.; Lam, Y.; Wong, A. C. M., Quantification of dynamic mixing performance of single screws of different configurations by visualization and image analysis. Advances in Polymer Technology 2009, 28 (1), Manas-Zloczower, I.; Tadmor, Z., Mixing and compounding of polymers : theory and practice. Hanser Publishers ; Distributed in the USA and Canada by Hanser/Gardner Publications: Munich; New York; Cincinnati,

46 18. Wolf, D.; Resnick, W., Residence Time Distribution in Real Systems. Industrial & Engineering Chemistry Fundamentals 1963, 2 (4), Bigg, D.; Middleman, S., Laminar Mixing of a Pair of Fluids in a Rectangular Cavity. Industrial & Engineering Chemistry Fundamentals 1974, 13 (3), Wolf, D.; White, D. H., Experimental study of the residence time distribution in plasticating screw extruders. AIChE Journal 1976, 22 (1), Sȩk, J. Technical University, Łódź, Poland Roemer, M. H.; Durbin, L. D., Transient Response and Moments Analysis of Backflow Cell Model for Flow Systems with Longitudinal Mixing. Industrial & Engineering Chemistry Fundamentals 1967, 6 (1), V. S. Kim, V. V. S., and Yu. V. Stungur, Plaste u. Kautschuk, 1978, 25, Tadmor, Z.; Klein, I., Engineering principles of plasticating extrusion. Van Nostrand Reinhold Co.: New York, Jaluria, Y., Fluid Flow Phenomena in Materials Processing---The 2000 Freeman Scholar Lecture. Journal of Fluids Engineering 2001, 123 (2), Lidor, G.; Tadmor, Z., Theoretical analysis of residence time distribution functions and strain distribution functions in plasticating screw extruders. Polymer Engineering & Science 1976, 16 (6), Kumar, A.; Ganjyal, G. M.; Jones, D. D.; Hanna, M. A., Modeling residence time distribution in a twinscrew extruder as a series of ideal steady-state flow reactors. Journal of Food Engineering 2008, 84 (3), Singh, B.; Rizvi, S. S. H., RESIDENCE TIME DISTRIBUTION (RTD) AND GOODNESS OF MIXING (GM) DURING CO2-INJECTION IN TWIN-SCREW EXTRUSION PART II: GM STUDIES. Journal of Food Process Engineering 1998, 21 (2), Bigg, D.; Middleman, S., Mixing in a Screw Extruder. A Model for Residence Time Distribution and Strain. Industrial & Engineering Chemistry Fundamentals 1974, 13 (1), Levenspiel, O., Chemical reaction engineering. Wiley: New York, De Ruyck, H., Modelling of the residence time distribution in a twin screw extruder. Journal of Food Engineering 1997, 32 (4), Gao, J.; Walsh, G. C.; Bigio, D.; Briber, R. M.; Wetzel, M. D., Residence-time distribution model for twinscrew extruders. AIChE Journal 1999, 45 (12), Davidson, V. J.; Paton, D.; Diosady, L. L.; Spratt, W. A., Residence Time Distributions for Wheat Starch in a Single Screw Extruder. Journal of Food Science 1983, 48 (4), Carneiro, O. S.; Caldeira, G.; Covas, J. A., Flow patterns in twin-screw extruders. Journal of Materials Processing Technology 1999, 92-93, Carneiro, O. S.; Covas, J. A.; Ferreira, J. A.; Cerqueira, M. F., On-line monitoring of the residence time distribution along a kneading block of a twin-screw extruder. Polymer Testing 2004, 23 (8), Zhang, X.-M.; Xu, Z.-B.; Feng, L.-F.; Song, X.-B.; Hu, G.-H., Assessing local residence time distributions in screw extruders through a new in-line measurement instrument. Polymer Engineering & Science 2006, 46 (4), Ziegler, G. R.; Aguilar, C. A., Residence time distribution in a co-rotating, twin-screw continuous mixer by the step change method. Journal of Food Engineering 2003, 59 (2-3), Zhang, X.-M.; Feng, L.-F.; Hoppe, S.; Hu, G.-H., Local residence time, residence revolution, and residence volume distributions in twin-screw extruders. Polymer Engineering & Science 2008, 48 (1),

47 3 Image analysis 3.1 Introduction Mixing of various ingredients is an important task in many industries such as food engineering, pharmaceuticals, chemicals, cosmetics, cement, glass-making and polymer processing 1. The single screw extruder is one of the most important processing tools in the polymer industry to compound and process plastics, rubbers, and fibers. However, the single screw extruder fitted with a standard screw is an inefficient mixing device because it applies insufficient low shear strain on the material 2. To obtain the desired extent of mixing, the single screw extruder may be equipped with specialized screws, modified feed zones, mixing sections, and vents 3. It is important to evaluate the extent of mixing in the extrusion process, as the presence of poor regions of ingredient dispersion may cause products failure 4. The mixing quality of mixtures has been assessed by many methods such as measuring the total interfacial area between the two mixture components as presented by Spencer and Wiley (1951) 5, calculating the weighted average total strain (WATS) used by Fenner (1979) 6, determining the scale and intensity of segregation as proposed by Danckwerts (1952) 7, and evaluating striation thickness as developed by Spencer and Wiley (1951) 5. Besides these theoretical approaches for evaluating mixing, there are some experimental techniques for evaluating mixing such as residence time distribution (RTD), extrudate sectional cuts, flow visualization, and image analysis. Shah (1979) And Gailus (1980) proposed using extrudate sectional cuts to count stratiforms to evaluate mixing characteristics in a single screw extruder 7. Mohr, Clapp, and Starr (1961) used transparent barrel for flow visualization 7. Heeschen (1995) 8 and Wightman (1996) 1 used image analysis method to quantitatively study mixing characteristics. Realpe (2003) used image analysis to study concentration of powder mixtures 9. Image analysis methods are an automated method to analyze mixing when there are many sections to be analyzed. For an example, there are dozens of stratiforms for each cross section when an extrudate is sliced (Figure 3-1). Thus, the acquisition of mixing data for each specimen 44

48 could be quite tedious without using any automated form of stereology 2 in which a computer does the analysis 7, 10. Figure 3-1 visualization of sliced extrudate 7 Image analysis is being used increasingly for mixing evaluation as data processing equipment grows more powerful and less expensive. This method also has been used in the food engineering field for determination of food quality. Surface color and surface texture of final extrudates was measured by Brosnan and Sun (2004) 11 and Tan, Gao, and Hsieh (1994) 12 by using an image processing method. Smolarz et al (1989), Alahakoon et al (1991), Barrett and Peleg (1992) all used image analysis methods to evaluate structure of extruded food products 12. There are three main steps for the image analysis method: 1) obtaining a section, 2) acquiring a digital image of the section, and 3) analyzing the image 10. By using image analysis, in-line, on-line and off-line measurements for assessing the mixtures quality are possible. The differences of these measurements, in-line, on-line, and off-line, are based on the two first steps of image analyzsis: obtaining a section and acquiring an image. In terms of analysis, there is no difference whether pictures are taken in an in-line, on-line or off-line modes, as the analysis is always performed on static images or frames. In the first group (in-line) the interior sections of the mixture are analyzed by employing tomographic 3 methods. Tomographic methods are based on Maddock s cold screw extrusion technique, achieved by stopping the screw to cool the barrel and screw by pushing or pulling the screw out of the barrel, and taking samples from the screw channel 13-1, 13b, 14. In on-line measurements, a transparent barrel provided visual observation of the homogenization process 4, 15,15b. Using on-line methods, the dynamic mixing characteristics of a single screw extruder was evaluated by Wong et al (1997) 15b the spatial interpretation of sections 3 - Tomography is imaging by sections or sectioning, through the use of any kind of penetrating wave. 45

49 46 For analyzing the image, different numerical simulations have been proposed by different researchers. Alemaskin 13a, 17 and Wang 4 used Renyi/Shannon entropies to quantify distributive mixing in polymer processing equipment.. Measuring the coefficient of variation was used by Latif to evaluate the variation in the grey levels between black and white 2. The Mean Gray Values (MGV) was used by Obregon to develop a mathematical model for predicting calibration curves 18 and Realpe used gray image analysis 9. Tan used statistical and spectral approaches such as mean, standard deviation, and third moments to assess colour and surface texture characteristics 12. Wightman was used image analysis to determine the mean, mode, starndard deviation, variance, and skewness of local composition in the mixtures 1. The variances of the light intensity were used by Wong to quantify the mixing quality 4. The aim of this work is to develop a quantitative tool to assess the mixing characteristics in a single screw extruder in three different kind of mixing sections. The degree of mixing of carbon black with polyethylene in three important mixing sections: single flight (one of the most basic mixing sections), Saxton (one of the most common distributive mixing sections), and Maddock (one of the most common dispersive mixing sections) were measured by the image analysis method. In this work, the off-line method in which the specimens were collected from exiting extrudates exiting the extruder was used. After collecting samples and taking images from them, images were analyzed using Matlab software version 7.1 (see appendix). 3.2 Theory Color homogeneity is one of the most important aspects of mixing which can be used to determine the quality of the mixture. Due to the fact that the color homogeneity relies on the mixing quality, evaluating the color homogeneity in a mixture can be a quantitative tool for evaluating mixing efficiency in a single screw extruder. To analyze the color homogeneity in each sample, the grey-level of each pixel is measured. In order to perform the analysis, we should first determine the sample area that is a relevant region to process. To do this we should recognize the boundary of each sample, so that the desired area to be analyzed is defined as the area contained within the boundary. To detect sample edge in the image, this work was done for examining derivatives in two dimensions: horizontal and vertical. The pixel s gray-level changes sharply at the edges, therefore, derivatives at edge points have large values. By taking the absolute value of these

50 derivatives and comparing them with a specified threshold, we can detect the edges. The gray level values are ranged between 0 for black to 255 for white. To set the best value for the threshold, according to the absolute values of the derivative (Figure 3-4) we can see that there are no signals with changing in gray level more than 10 values before the first and after the last signal magnitude jumps. Therefore, we set the threshold at 10. This means that the pixel gray level changes at least 10 levels at the edge of sample image. Figure 3-2 shows the image of a Maddock mixing section and a selected row as a sample. Figure 3-3 shows this row as a onedimensional signal and Figure 3-4 shows the absolute values of the derivative. The first and last value above the threshold defines the starting and finishing points of images edges in a row. This algorithm was applied to all rows and columns in order to find the desired area. All the pixels outside the desired area were recalled as removed pixels so that they were not used in the analysis. Selected row Figure 3-2 selected row in the image referring to the mixture of polyethylene (Dowlex NG 5056D) and carbon black in the Maddock mixing section at 20rpm and feed rate of 10 kg/hr, operating tempreture at 250 C 47

51 Figure 3-3 One dimensional signals of selected row in Figure 3-2 Figure 3-4 The absolute values of the derivative of Figure

52 The next step is defining the mixing degree. In our experiment the best-mixed sample is a homogenous sample that is all black and the worst case is a sample containing a white nonmixed region. Figure 3-5 shows the hypothetically ideal figures of the best and the worst mixtures. Figure 3-5 a) the best mixture with mixing degree of 100% b) the worst mixture with mixing degree of 0% The mixing degree for the best case is considered to be 100% and for the worst case it is set to 0%. A simple criterion for measuring the mixing degree can be the ratio of black areas to the whole sample. Therefore, the gray level pixels should be converted to a bi-level image i.e. black and white pixels. For converting gray level pixels to bi-level pixels, the threshold method was used. Using this method, values greater than the threshold are considered as white pixels and values less than the threshold are considered as black pixels. Mixing degree is calculated as follows: D = P P min (1 P min ) (1) where mixed region ration, P, is: P = B T (2) and where B represents the number of black pixels. T represents the total number of pixels including both black and white pixels. The minimum value of P,P min, is a hypothetical value as the mixing degree equal to zero, which is not feasible. Here P min is set at 50% which refers to the condition where half of the mixture is white and the other half is black. 49

53 There is a major drawback with the above-mentioned algorithm. As shown in Figure 3-6 in some cases, it is possible that two different samples have the same amount of degree of mixing, D, while according to their images, it is obvious that one of them is the better mixture than the other one and the mixing degree of one should be higher than the other. In Figure 3-6, there are two samples which both have mixing degrees equal to 82%, but the a seems to be better mixed compared to the b sample. Figure 3-6 Two samples with mixing degree of 82% An image filter was used to solve this problem. Through image filtering, the narrow lines in the images can be removed. The length of the filter depends on the image resolution and the extent of narrow lines that needed to be removed. Here we have used the Gaussian 5X5 filter to filter the images shown in Figure 3-7. After filtering, the mixing degree for sample a is 84% and for sample b, the mixing degree is 76%. 50

54 Figure 3-7 Filtered images a) filtered image with 84% mixing degree b) filtered image with 76% mixing degree Here in this work, we have calculated the mixing degree of images by using equation 1 and 2 after filtering them with Gaussian 5X5 filter. 3.3 Experimental section The experimental procedure is divided into two sections: sample collecting and taking images. The first section, sample collecting, was done at Maillefers laboratory, while the other section, taking images was done at McGill University Material To assess the effects of different materials on mixing, here we used 2 different polyethylene resins from which two different mixtures were prepared. All of the used materials were kindly donated by Maillefer and they were used as received. The two different polyethylene resins were: a linear low-density polyethylene extrusion grade resin (Dowlex NG 5056D), and a 51

55 medium density polyethylene extrusion grade resin (Eraclene FB 506). A linear low density black polyethylene masterbatch (Plasblak LL4932) was used as a black pigment (i.e. tracer). Two different mixtures of the afore-mentioned polyethylene resins were Mix 1 (25% weight Dowlex, 73% weight Eraclene, 2% weight Plasblak) and Mix 2 (70% weight Dowlex, 28% weight Eraclene, 2% weight Plasblak) Equipment For collecting samples, a single-screw extruder, (Maillefer NMC 45-24D, screw speed 215 rpm max, motor power = 29 kw) was used. To record rheological information such as viscosity and power-law exponent (n) for the different materials and mixtures, the extruder was attached to a slit die (Maillefer rheological die ENS Filiere rheologique). Figure 3-8 shows the die used here. The die is constructed out of two sections: in one section, it is 7 mm in depth, length=100 mm and a shallow section with 2.5 mm depth and length= 40 mm. There were four pressure transducers measuring the pressure at the beginning and at the end of each section. The most important part of the extruder was the interchangeable mixing sections all with length = 270 mm and diameter = 45 mm: single flight, Maddock, and Saxton, which were each used separately. First channel Second channel Pressure transducer Figure 3-8 Rheology die 52

56 Figure 3-9 shows schematically the two sections of the die and the place and name of each pressure transducer. The plastic cutter machine (Leitz Wetzlar) was used to slice the samples (Figure 3-10). The optical microscope (Olympus BX60) was used to take pictures of the sliced, compressed samples. A hydraulic compression press (Macklow-Smith S/N R33/92) was used to make compressed samples out of sliced samples for taking images by microscope. The second part, taking images from the sliced samples, was done at McGill. A scanner (Canoscan 4400F) was used to take pictures from the sliced samples. «Rheo 1» for deep channel «Rheo 2» for deep channel «Rheo 1» for shallow channel «Rheo 2» for shallow channel Figure 3-9 schematic picture of two sections of the die Figure 3-10 Plastic cutter 53

57 3.3.3 Experimental procedure The materials (Dowlex, Eracelen, Mix1, and Mix2) with aconstant feed of the carbon black masterbatch at 2% weight were extruded. To analyze the effects of screw speed on mixing, the extruder was operated at two screw speeds: 20 rpm and 80 rpm. Feed rate at 20 rpm screw speed was 10 kg/hr and at 80 rpm was 40 kg/hr rpm. After reaching to steady output and stabilized temperature at 250 C, the samples were collected. Before collecting samples, extruder operating conditions such as the average melt temperature, rate of output, and pressure profile in the die were recorded by four pressure transducers located in the die. The extruder operating conditions for each used material at different mixing sections and different same screw speeds are shown in Table 3-1, 3-2, 3-3 and 3-4. The data presented in Table 3-1, 3-2, 3-3, and 3-4 was used to automatically calculate the rheological properties (viscosity and power-law exponent (n)) of each material shown in Table 3-5(see Appendix). Table 3-1 The operating conditions table for Eraclene FB506 material Mixing section Screw speed Output (kg/hr) Ave melt Temp C Height of the channel (m) Length of channel (m) Pressure at Rheo1 (bar) Pressure at Rheo2 (bar) Maddock Maddock Single flight Single flight Saxton Saxton

58 Table 3-2 The operating conditions table for Dowlex NG5056G material Mixing section Screw speed Output (kg/hr) Ave melt Temp C Height of the channel (m) Length of channel (m) Pressure at Rheo1 (bar) Pressure at Rheo2 (bar) Single flight Single flight Maddock Maddock Saxton Saxton Table 3-3 The operating conditions table for Mix1 material Mixing section Screw speed Output (kg/hr) Ave melt Temp C Height of the channel (m) Length of channel (m) Pressure at Rheo1 (bar) Pressure at Rheo2 (bar) Single flight Single flight Maddock Maddock Saxton Saxton Table 3-4 The operating conditions table for Mix2 material Mixing section Screw speed Output (kg/hr) Ave melt Temp C Height of the channel (m) Length of channel (m) Pressure at Rheo1 (bar) Pressure at Rheo2 (bar) Single flight Single flight Maddock Maddock Saxton Saxton

59 According to Table 3-1, 3-2, 3-3 and 3-4, we expect to have different mixing characteristics for four different materials used here (Dowlex, Eracelen, Mix1, and Mix2). After cooling the sample, it was sliced into 2.5 mm thick slices (5 slices for each sample). For microscopy, the 2 slices of each sample were compressed at 200 C under pressure = 12 kg/cm 2 for 20 min. After compressing samples, the microscope was used to take images with 2.5X magnification and pixel/mm resolution. The sliced samples were scanned as a grey scale positive film with 1200 dpi resolution and pixel/mm resolution. The experimental procedure is schematically shown in Figure Figure 3-11 Schematic experimental procedure, (a) the samples are compressed after being sliced and the microscope is used to take images, (b) sliced samples were scanned 3.4 Results and discussion Figure 3-13, 3-14, and 3-15 show zoomed images of different blends: Dowlex, Eraclene, Mix1, and Mix2 in various mixing sections. These pictures were taken at 2X magnification and 56

60 747.5 pixels/mm resolution. As shown in Figure 3-13 the mixing quality becomes better as the screw speed increases. Here we can see that a mixture of Dowlex/carbon black masterbatch has fewer coarser striations at 20 rpm compared to the other mixtures. According to rheological data shown in Table 3-5, Dowlex has the lowest viscosity of all the materials studied. This is important in terms of mixing. First, lower viscosity fluids are more effective for distributive mixing as there is no need to break any agglomerates down in distributive mixing. There low viscosity permits more relative motion between the components in the mixture. This result is confirmed with the Calculated degree of mixing shown in Table 3-6 that were derived from scanned images by using Equations 1 and 2. In Table 3-6, we can also see that the degree of mixing is increased as the screw speed is increased. Comparing the degree of mixing for one screw configuration at a particular screw speed, we can see that a mixture of Dowlex polyethylene resin with the carbon black masterbatch has the highest degree of mixing, and the lowest degree of mixing belongs to the Eraclene/carbon black mixture. This finding is confirmed by comparing viscosities provided in Table 3-5, which shows that Dowlex has the lowest viscosity and Eraclene has the highest viscosity. Table 3-5 Viscosity and Power-law exponent calculated by rheology die for different material Material Viscosity 200 C (Pa s -1 ) Power-law exponent (n) Dowlex NG5065G Eraclene FB Mix Mix Comparing degrees of mixing at a screw speed of 20 rpm screw speed for one type of material but for different mixing sections (Table 3-6) we can see that mixtures in a single flight mixing section had significantly lower degree of mixing compared to the Maddock and Saxton elements. These results confirm the findings from the residence time distribution (RTD) experiments discussed in Chapter 2. This finding is reasonable according to the geometries of these three mixing elements (single flight, Maddock, and Saxton). As shown in Figure 3-12, we can see that the single flight mixing element consists of a single primary helical flight but the Maddock and Saxton have more complicated geometries. The Maddock mixer consists of a series of grooves parallel to the screw length. The ends of each series are open which alternately 57

61 ends to the feed flow and discharge or exiting flow. There are two lands at each row: discharge or mixing lands that are located between the entrance and discharge groove with diameter slightly lower than the screw diameter and the wiping lands that are located between the discharge and next entrance groove with the same diameter as the screw. The materials are subjected to the high amount of shear at the mixing lands. The Saxton mixer consists of a multi-flighted screw section with a reverse pitch channel cut into the flights. There are primary and secondary flights in the Saxton mixing section, and the secondary flights have different helix angle than the primary flights. The melt stream is sheared and recombined by facing the interrupting secondary flights. There are no mixing regions in the geometry of the single flight mixing section; there are high shear regions in the Maddock mixing section; in the Saxton mixer the material are both sheared and reoriented, thus, the mixtures blended in the Saxton mixing section have good qualities. 3-12: a) Maddock mixing section b) Saxton mixing section, c) single flight mixing section 58

62 Table 3-6 Degree of mixing calculated from scanned pictures Mixing section Screw speed (rpm) Mixing degree % Dowlex Mixing degree % Eraclene Mixing degree % Mix1 Mixing degree % Mix2 Single flight Maddock Saxton : Zoomed images of different mixtures of polyethylene resins, each containing 2 w% carbon black masterbatch in a single flight mixing sections at two different screw speeds 20 rpm and 80 rpm (magnification = 2.5X, resolution.= pixels/mm) As shown in Figure 3-14 as speeds are increased, the mixing quality improves in all cases. Mixing in the Dowlex/carbon black blends is easiest, even in the single flight element. In the more viscous mixtures, the striations are quite noticeable and not fine. In the blends compounded in the Maddock mixer (Figure 3-14) compared to those blended in the single flight 59

63 mixer (Figure 3-13) the striations become much finer in the Maddock mixer, even in the higher viscosity mixtures. Figure 3-15 shows the zoomed images of the Saxton mixing section, where we can see even at 20 rpm screw speed in the highest viscosity blend, the mixing is better compared to the single flight or Maddock mixing element. This is quantified in Table 3-6. The degree of mixing noticeably improves by increasing the screw speed in the single flight mixer, the degree of mixing increased around 69% as the screw speed increases from 20 rpm into 80 rpm. This increase is much less noticeable for the Maddock (around 28%) and the Saxton (around 18%), which means that mixing was sufficiently good at the lower screw speeds Zoomed images of different mixtures of polyethylene resins, each containing 2 w% carbon black masterbatch in a Maddock mixing sections at two different screw speeds 20 rpm and 80 rpm (magnification = 2.5X, resolution.= pixels/mm) 60

64 Figure 3-15 Zoomed images of different mixtures of polyethylene resins, each containing 2 w% carbon black masterbatch in a Saxton mixing sections at two different screw speeds 20 rpm and 80 rpm (magnification = 2.5X, resolution.= pixels/mm) 3.5 Conclusion A new method for evaluating degree of mixing was developed as a means of comparing three different elements in a single screw extruder. According to this method, the scanned images were filtered by Gaussian 5X5 filter, and then the degree of mixing was calculated. As is sometimes obvious from the visual inspection, the degree of mixing quantitatively provided a means of comparison for the different elements. The Saxton mixing section provided mixtures with the highest degree of mixing and mixtures compounded with the single flight mixing section had the lowest degree of mixing. This finding is corroborated by the results of the RTD experiments in Chapter 2. Moreover, screw speed and material both affected mixing degree. The degree of mixing increased as the screw speed increased in all elements. This change was more significant for mixtures getting mixed by the single flight mixing section- it required the most energy to achieve the mixing that was otherwise accomplished at lower screw speeds in more intensive mixing elements such as the Saxton or Maddock elements. The effects of viscosity on material on mixing quality were also investigated. The material with the lowest viscosity, Dowlex 61

65 NG5065G, provided the highest degrees of mixing in all screw speeds and configurations. The mixtures of Eraclene FB506, the material with highest viscosity, had the lowest mixing degree in all three different mixing sections. 62

66 3.6 References 1. Wightman, C.; Muzzio, F. J.; Wilder, J., A quantitative image analysis method for characterizing mixtures of granular materials. Powder Technology 1996, 89 (2), Latif, L.; Saidpour, S. H., Assessment of pigment distribution in molded samples using an image processing technique. Advances in Polymer Technology 1996, 15 (4), Rauwendaal, C., New directions for extrusion: compounding with single screw extruders. Plastics, Additives and Compounding 2002, 4 (6), Wong, A. C. Y.; Lam, Y.; Wong, A. C. M., Quantification of dynamic mixing performance of single screws of different configurations by visualization and image analysis. Advances in Polymer Technology 2009, 28 (1), Spencer, R. S.; Wiley, R. M., The mixing of very viscous liquids. Journal of Colloid Science 1951, 6 (2), Fenner, R. T., Principles of polymer processing. Chemical Pub.: New York, Strasser, R. A.; Erwin, L., Experimental techniques for analysis distributive laminar mixing in continuous flows. Advances in Polymer Technology 1984, 4 (1), Heeschen, W. A., A quantitative image analysis method for the determination of cocontinuity in polymer blends. Polymer 1995, 36 (9), Realpe, A.; Velázquez, C., Image processing and analysis for determination of concentrations of powder mixtures. Powder Technology 2003, 134 (3), Rauwendaal, C., Polymer mixing : a self-study guide. Hanser Publishers ; Hanser/Gardner Publishers: Munich; Cincinnati, Brosnan, T.; Sun, D.-W., Improving quality inspection of food products by computer vision--a review. Journal of Food Engineering 2004, 61 (1), Tan, J.; Gao, X.; Hsieh, F., Extrudate Characterization by Image Processing. Journal of Food Science 1994, 59 (6), (a) Alemaskin, K.; Manas-Zloczower, I.; Kaufman, M., Entropic analysis of color homogeneity. Polymer Engineering & Science 2005, 45 (7), ; (b) Dal Grande, F.; Santomaso, A.; Canu, P., Improving local composition measurements of binary mixtures by image analysis. Powder Technology 2008, 187 (3), Wightman, C.; Muzzio, F. J., Mixing of granular material in a drum mixer undergoing rotational and rocking motions I. Uniform particles. Powder Technology 1998, 98 (2), (a) Kim, S. J.; Kwon, T. H., Enhancement of mixing performance of single-screw extrusion processes via chaotic flows: Part II. Numerical study. Advances in Polymer Technology 1996, 15 (1), 55-69; (b) Wong, A.; Lam, Y., Visualization study on the dynamic mixing quality during singlescrew extrusion. Journal of Polymer Research 2008, 15 (1), Muerza, S.; Berthiaux, H.; Massol-Chaudeur, S.; Thomas, G., A dynamic study of static mixing using on-line image analysis. Powder Technology 2002, 128 (2-3), Alemaskin, K.; Manas-Zloczower, I.; Kaufman, M., Color mixing in the metering zone of a single screw extruder: Numerical simulations and experimental validation. Polymer Engineering & Science 2005, 45 (7), Obregón, L.; Velázquez, C., Discrimination limit between mean gray values for the prediction of powder concentrations. Powder Technology 2007, 175 (1),

67 4 General Conclusion 4.1 Summary Mixing in single screw extruders has been studied extensively for many years. The key challenge is to select the best mixing elements from among the different existing mixing elements to accomplish satisfactory mixing. To develop quantitative parameters for evaluating different mixing elements in single screw extruders, here we compared mixing in three different mixing elements using residence time distribution and image analysis methods. For residence time distributions, the tracer was injected and the concentration was detected by weighing the ash residues of the sample. For image analyzing, the mixture of polyethylene based black master batch (a mixture of carbon black dispersed in polyethylene) and polyethylene resins were extruded, and the resulting samples for analysis were taken from sliced samples that were scanned after collecting and slicing. Matlab software was used for analyzing the scanned images and calculating the degree of mixing. The residence time distribution studies provided mean residence times to evaluate different mixing elements. The image analyzing method provided degree of mixing as a quantitative parameter to compare different mixing elements. Taken together, our results confirmed the experimental data and demonstrated that for non-thermal sensitive polymers, the higher the mean residence time, generally the better was the mixing. The degree of mixing was higher in Saxton mixing section compared to single flight and Maddock mixing sections. There were fewer striations in images of mixtures mixed by the Saxton element than in mixtures taken from the Maddock and the single flight element. The single flight element was clearly an inefficient mixing section. These two parameters, residence time distribution and 64

68 degree of mixing, not only can be used as quantitative parameters to evaluate different types of mixing elements but also can be used as a data basis for predictive simulations. 4.2 Future Work To further complete this study, more mixing sections must be tested. Here we compared three most common mixing sections, but for making an application window for different mixing sections, more mixing sections are needed to be studied. Also different mixing mechanisms, dispersive and distributive, must be studied for these three mixing sections. According to the fact that the axial shear rates in the center region of the screw channel are very low, for improving axial mixing in extrusion processes, the mixing sections must be used to redistribute the material from the center region to the outer region of the channel. Observing the complex geometries of these two mixing sections (Figure 4-1) we can see that there are some random slots between the flights in Saxton mixing section while in the Maddock section, the flouted flights are lined perpendicular to each other and there are no slots between each flight. So we can propose that the mixing characteristic in a Maddock mixing section can be improved by creating some slots to vary the spacing between the flights. By doing this in fact, streams of different axial velocities will be created. 4-1 a) Maddock mixing section b) Saxton mixing section 65

69 Appendix Calculating viscosity and Power-law exponent (n) The following equations are set in the control panel of the single-screw extruder (Maillefer NMC 45-24D) which are automatically calculated as the four pressure transducers are the die be activated. η = m 0 exp (β 200) m 0 = exp(ln ( m 0 )) ln m 0 = linest (y i, T i, M i ) y i = ln(dp H 2 ) M i = ln(v) ( n F f p w H 2) V = output rho/3600 β = linest (y i, T i, M i ) n = linest (y i, T i, M i ) Nomenclature in data sheets: Output Mass throughput kg/h Mass Temp(T i ). Melt temperature at exit C Rheo 1 Pressure reading of first transducer bar Rheo2 Pressure reading of second transducer bar H Height of the channel m L Length of channel m W Width of channel m rho estimated melt density kg/m3 fp Estimated shape factor of rectangular channel - n F Power law exponent (fitted) beta temperature shift factor m 0 viscosity constant at 0 C 66

70 Mix2 Data set rho 770 w n 0.49 Starting value- Compare to value from LINEST Output Mass temp. Rheo 1 Rheo2 H L fp y T M=LN(VRate*(2+1/n)*2/(fp*W*H*H)) dp meas dp calc Ratio n beta LN(m 0 ) m 0 R min

71 Mix1 Data set rho 770 w n 0.46 Starting value- Compare to value from LINEST Output Mass temp. Rheo 1 Rheo2 H L fp y T LN(VRate*(2+1/n)*2/(fp*W*H*H)) dp meas dp calc Ratio n beta LN(m 0 ) m 0 R min

72 Dowlex NG5056G Data set rho 770 w n 0.55 Starting value- Compare to value from LINEST Output Mass temp. Rheo 1 Rheo2 H L fp y T LN(VRate*(2+1/n)*2/(fp*W*H*H)) dp meas dp calc Ratio n beta LN(m 0 ) m 0 R min

73 Eraclene FB506 Data set rho 770 w n Starting value- Compare to value from LINEST Output Mass temp. Rheo1 Rheo2 H L fp y T LN(VRate*(2+1/n)*2/(fp*W*H*H)) dp meas dp calc Ratio n beta LN(m 0 ) m 0 R min

74 Matlab codes %% RTD modeling program clc clear close all load processed_data %% tmin_single20 = 12; t=deltat*(0:length(et_single20)-1); p_single20 = t(tmin_single20)/tbar_single20; theta_single20 = t/tbar_single20; tmin_maddox20 = 23; t=deltat*(0:length(et_maddox20)-1); p_maddox20 = t(tmin_maddox20)/tbar_maddox20; theta_maddox20 = t/tbar_maddox20; tmin_sax20 = 24; t=deltat*(0:length(et_sax20)-1); p_sax20 = t(tmin_sax20)/tbar_sax20; theta_sax20 = t/tbar_sax20; %% %singleflight 20 disp (' Table 2 Singleflight '); disp ('data n d p error'); Et_single20 = tbar_single20*et_single20; Et_maddox20 = tbar_maddox20*et_maddox20; Et_sax20 = tbar_sax20*et_sax20; Et_single20(1:8) = 0; Et_maddox20(1:18) = 0; Et_sax20(1:18) = 0; hold on plot(theta_single20, Et_single20,'ok','LineWidth',3); [n,d,p] = find_flow_params(1,et_single20',theta_single20,p_single20); e_single20_1 = flowmodel(1,theta_single20,n,p_single20,d); e_single20_1 = max(et_single20)*e_single20_1/max(e_single20_1); err_single20_1 = sum((e_single20_1-et_single20').*(e_single20_1- Et_single20')); plot(theta_single20, e_single20_1,':r','linewidth',2); disp (['Model1 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_single20_1)]); [n,d,p] = find_flow_params(2,et_single20',theta_single20,p_single20); e_single20_2 = flowmodel(2,theta_single20,n,p_single20,d); e_single20_2 = max(et_single20)*e_single20_2/max(e_single20_2); 71

75 err_single20_2 = sum((e_single20_2-et_single20').*(e_single20_2- Et_single20')); plot(theta_single20, e_single20_2,'+b','linewidth',2,'markersize',6); disp (['Model2 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_single20_2)]); [n,d,p] = find_flow_params(3,et_single20',theta_single20,p_single20); e_single20_3 = flowmodel(3,theta_single20,n,p_single20,d); e_single20_3 = max(et_single20)*e_single20_3/max(e_single20_3); err_single20_3 = sum((e_single20_3-et_single20').*(e_single20_3- Et_single20')); plot(theta_single20, e_single20_3,'--g','linewidth',3); disp (['Model3 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_single20_3)]); [n,d,p] = find_flow_params(4,et_single20',theta_single20,p_single20); e_single20_4 = flowmodel(4,theta_single20,n,p,d); e_single20_4 = max(et_single20)*e_single20_4/max(e_single20_4); err_single20_4 = sum((e_single20_4-et_single20').*(e_single20_4- Et_single20')); h = plot(theta_single20, e_single20_4,'-.m','linewidth',3); disp (['Model4 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_single20_4)]); xlabel('normalized time'); ylabel('residence time distribution function E(t)'); title ('Singleflight'); legend('experimental','model 1','Model 2','Model 3','Model 4'); xlim([0 4.2]); saveas(h,'sigleflight_20','tif') ; %% %maddox disp (' '); disp (' Table 3 Maddox '); disp ('data n d p error'); figure hold on plot(theta_maddox20, Et_maddox20,'ok','LineWidth',3); [n,d,p] = find_flow_params(1,et_maddox20',theta_maddox20,p_maddox20); e_maddox20_1 = flowmodel(1,theta_maddox20,n,p_maddox20,d); e_maddox20_1 = max(et_maddox20)*e_maddox20_1/max(e_maddox20_1); err_maddox20_1 = sum((e_maddox20_1-et_maddox20').*(e_maddox20_1- Et_maddox20')); plot(theta_maddox20, e_maddox20_1,':r','linewidth',2); disp (['Model1 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_maddox20_1)]); [n,d,p] = find_flow_params(2,et_maddox20',theta_maddox20,p_maddox20); e_maddox20_2 = flowmodel(2,theta_maddox20,n,p_maddox20,d); e_maddox20_2 = max(et_maddox20)*e_maddox20_2/max(e_maddox20_2); err_maddox20_2 = sum((e_maddox20_2-et_maddox20').*(e_maddox20_2- Et_maddox20')); 72

76 plot(theta_maddox20, e_maddox20_2,'+b','linewidth',2,'markersize',6); disp (['Model2 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_maddox20_2)]); [n,d,p] = find_flow_params(3,et_maddox20',theta_maddox20,p_maddox20); e_maddox20_3 = flowmodel(3,theta_maddox20,n,p_maddox20,d); e_maddox20_3 = max(et_maddox20)*e_maddox20_3/max(e_maddox20_3); err_maddox20_3 = sum((e_maddox20_3-et_maddox20').*(e_maddox20_3- Et_maddox20')); plot(theta_maddox20, e_maddox20_3,'--g','linewidth',3); disp (['Model3 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_maddox20_3)]); [n,d,p] = find_flow_params(4,et_maddox20',theta_maddox20,p_maddox20); e_maddox20_4 = flowmodel(4,theta_maddox20,n,p,d); e_maddox20_4 = max(et_maddox20)*e_maddox20_4/max(e_maddox20_4); err_maddox20_4 = sum((e_maddox20_4-et_maddox20').*(e_maddox20_4- Et_maddox20')); h = plot(theta_maddox20, e_maddox20_4,'-.m','linewidth',3); disp (['Model4 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_maddox20_4)]); xlabel('normalized time'); ylabel('residence time distribution function E(t)'); xlim([0 3.2]); title ('Maddox'); legend('experimental','model 1','Model 2','Model 3','Model 4'); saveas(h,'maddox_20','tif') ; %% %saxton 20 disp (' '); disp (' Table 4 Saxton '); disp ('data n d p error'); figure hold on plot(theta_sax20, Et_sax20,'ok','LineWidth',3); [n,d,p] = find_flow_params(1,et_sax20',theta_sax20,p_sax20); e_sax20_1 = flowmodel(1,theta_sax20,n,p_sax20,d); e_sax20_1 = max(et_sax20)*e_sax20_1/max(e_sax20_1); err_sax20_1 = sum((e_sax20_1-et_sax20').*(e_sax20_1-et_sax20')); plot(theta_sax20, e_sax20_1,':r','linewidth',3); disp (['Model1 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_sax20_1)]); [n,d,p] = find_flow_params(2,et_sax20',theta_sax20,p_sax20); e_sax20_2 = flowmodel(2,theta_sax20,n,p_sax20,d); e_sax20_2 = max(et_sax20)*e_sax20_2/max(e_sax20_2); err_sax20_2 = sum((e_sax20_2-et_sax20').*(e_sax20_2-et_sax20')); plot(theta_sax20, e_sax20_2,'+b','linewidth',2,'markersize',6); disp (['Model2 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_sax20_2)]); 73

77 [n,d,p] = find_flow_params(3,et_sax20',theta_sax20,p_sax20); e_sax20_3 = flowmodel(3,theta_sax20,n,p_sax20,d); e_sax20_3 = max(et_sax20)*e_sax20_3/max(e_sax20_3); err_sax20_3 = sum((e_sax20_3-et_sax20').*(e_sax20_3-et_sax20')); plot(theta_sax20, e_sax20_3,'--g','linewidth',3); disp (['Model3 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_sax20_3)]); [n,d,p] = find_flow_params(4,et_sax20',theta_sax20,p_sax20); e_sax20_4 = flowmodel(4,theta_sax20,n,p,d); e_sax20_4 = max(et_sax20)*e_sax20_4/max(e_sax20_4); err_sax20_4 = sum((e_sax20_4-et_sax20').*(e_sax20_4-et_sax20')); h = plot(theta_sax20, e_sax20_4,'-.m','linewidth',3); disp (['Model4 ' num2str(n) ' ' num2str(d) ' ' num2str(p) ' ' num2str(err_sax20_4)]); xlabel('normalized time'); ylabel('residence time distribution function E(t)'); title ('Saxton'); xlim([0 3.1]); legend('experimental','model 1','Model 2','Model 3','Model 4'); saveas(h,'saxton_20','tif') ; function [e] = flowmodel(model_num,theta,n,p,d) switch model_num case 1 case 2 case 3 b = n/(1-p)*(1-d); theta(theta<p) = p; x1 = b*(theta-p); f1 = floor(n)-1; f2 = n-1; fac = f2*factorial(f1)/f1; x2 = b*(x1.^(n-1))/fac; e = x2.*exp(-x1); theta(theta<p) = p; theta = (theta-p)/(1-p); x1 = n*(n*theta).^(n-1); f1 = floor(n)-1; f2 = n-1; fac = f2*factorial(f1)/f1; x2 = x1/fac; e = x2.*exp(-n*theta); x1 = n*(n*theta).^(n-1); f1 = floor(n)-1; 74

78 f2 = n-1; fac = f2*factorial(f1)/f1; x2 = x1/fac; e = x2.*exp(-n*theta); case 4 e = (1/(1-p))*exp(-(theta-p)/(1-p)); e(theta<p) = 0; otherwise disp('error in model number'); end function [n,d,p] = find_flow_params(model_num,et,theta,p) switch model_num case 1 N_V = 2:0.1:4; D_V = 0:0.01:.3; lse = zeros(length(n_v),length(d_v)); r=1; for n = N_V c=1; for d = D_V e = flowmodel(model_num,theta,n,p,d); etheta = max(e)*et/max(et); lse(r,c) = sum((e-etheta).*(e-etheta)); c=c+1; end r= r+1; end case 2 [val di] = min(min(lse)); [val ni] = min(min(lse')); d = D_V(di); n = N_V(ni); p = []; N_V = 2:0.1:4; r=1; lse = zeros(1,length(n_v)); for n = N_V e = flowmodel(model_num,theta,n,p,[]); etheta = max(e)*et/max(et); lse(r) = sum((e-etheta).*(e-etheta)); r= r+1; end [val ni] = min(lse); n = N_V(ni); d = []; p = []; case 3 N_V = 2:0.1:4; 75

79 76 r=1; lse = zeros(1,length(n_v)); for n = N_V e = flowmodel(model_num,theta,n,p,[]); etheta = max(e)*et/max(et); lse(r) = sum((e-etheta).*(e-etheta)); r= r+1; end [val ni] = min(lse); n = N_V(ni); d = []; p = []; case 4 P_V = 0:.01:.9; r=1; lse = zeros(1,length(p_v)); for p = P_V e = flowmodel(model_num,theta,[],p,[]); etheta = max(e)*et/max(et); lse(r) = sum((e-etheta).*(e-etheta)); r= r+1; end [val pi] = min(lse); p = P_V(pi); n = []; d = []; otherwise disp('error in model number'); end % Experimental compare clc clear close all load experimental_results deltat = 10.0; smooth_single20 = relative_weight_single20; [maxd maxi] = max(relative_weight_single20); smooth_single20(maxi:end) = smooth(relative_weight_single20(maxi:end),3); us_single20 = resample(smooth_single20,3,1); us_single20(us_single20<0) = 0; s_single20 = sum(us_single20*deltat); Et_single20 = us_single20/s_single20; Ft_single20 = cumsum(us_single20*deltat); t=deltat*(0:length(us_single20)-1); tbar_single20 = sum(t'.*us_single20*deltat)/s_single20; t_single20 = t/tbar_single20; smooth_single50 = relative_weight_single50; [maxd maxi] = max(relative_weight_single50); smooth_single50(maxi:end) = smooth(relative_weight_single50(maxi:end),3); us_single50 = resample(smooth_single50,3,1);

80 us_single50(us_single50<0) = 0; s_single50 = sum(us_single50*deltat); Et_single50 = us_single50/s_single50; Ft_single50 = cumsum(us_single50*deltat); t=deltat*(0:length(us_single50)-1); tbar_single50 = sum(t'.*us_single50*deltat)/s_single50; t_single50 = t/tbar_single50; smooth_single80 = relative_weight_single80; [maxd maxi] = max(relative_weight_single80); smooth_single80(maxi:end) = smooth(relative_weight_single80(maxi:end),3); us_single80 = resample(smooth_single80,3,1); us_single80(us_single80<0) = 0; s_single80 = sum(us_single80*deltat); Et_single80 = us_single80/s_single80; Ft_single80 = cumsum(us_single80*deltat); t=deltat*(0:length(us_single80)-1); tbar_single80 = sum(t'.*us_single80*deltat)/s_single80; t_single80 = t/tbar_single80; smooth_sax20 = relative_weight_sax20; [maxd maxi] = max(relative_weight_sax20); smooth_sax20(maxi-7:end) = smooth(relative_weight_sax20(maxi-7:end),3); us_sax20 = resample(smooth_sax20,3,1); us_sax20(us_sax20<0) = 0; s_sax20 = sum(us_sax20*deltat); Et_sax20 = us_sax20/s_sax20; Ft_sax20 = cumsum(us_sax20*deltat); t=deltat*(0:length(us_sax20)-1); tbar_sax20 = sum(t'.*us_sax20*deltat)/s_sax20; t_sax20 = t/tbar_sax20; smooth_sax50 = relative_weight_sax50; [maxd maxi] = max(relative_weight_sax50); smooth_sax50(maxi:end) = smooth(relative_weight_sax50(maxi:end),3); us_sax50 = resample(smooth_sax50,3,1); us_sax50(us_sax50<0) = 0; s_sax50 = sum(us_sax50*deltat); Et_sax50 = us_sax50/s_sax50; Ft_sax50 = cumsum(us_sax50*deltat); t=deltat*(0:length(us_sax50)-1); tbar_sax50 = sum(t'.*us_sax50*deltat)/s_sax50; t_sax50 = t/tbar_sax50; smooth_sax80 = relative_weight_sax80; [maxd maxi] = max(relative_weight_sax80); smooth_sax80(maxi:end) = smooth(relative_weight_sax80(maxi:end),3); us_sax80 = resample(smooth_sax80,3,1); us_sax80(us_sax80<0) = 0; s_sax80 = sum(us_sax80*deltat); Et_sax80 = us_sax80/s_sax80; Ft_sax80 = cumsum(us_sax80*deltat); t=deltat*(0:length(us_sax80)-1); tbar_sax80 = sum(t'.*us_sax80*deltat)/s_sax80; t_sax80 = t/tbar_sax80; 77

81 smooth_maddox20= relative_weight_maddox20; [maxd maxi] = max(relative_weight_maddox20); smooth_maddox20(maxi-7:end) = smooth(relative_weight_maddox20(maxi-7:end),3); us_maddox20 = resample(smooth_maddox20,3,1); us_maddox20(us_maddox20<0) = 0; s_maddox20 = sum(us_maddox20*deltat); Et_maddox20 = us_maddox20/s_maddox20; Ft_maddox20 = cumsum(us_maddox20*deltat); t=deltat*(0:length(us_maddox20)-1); tbar_maddox20 = sum(t'.*us_maddox20*deltat)/s_maddox20; t_maddox20 = t/tbar_maddox20; %% hold on %plot(t_single20,tbar_single20*et_single20,'--ks','linewidth',2, 'MarkerSize',8); %plot(t_single50,tbar_single50*et_single50,':*k','linewidth',2, 'MarkerSize',8); %h_single = plot(t_single80,tbar_single80*et_single80,'-.ok','linewidth',2, 'MarkerSize',8); plot((0:length(ft_single20)-1)*deltat,tbar_single20*et_single20,'-- sr','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot((0:length(ft_single50)- 1)*deltat,tbar_single50*Et_single50,':*b','LineWidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_single = plot((0:length(ft_single80)-1)*deltat,tbar_single80*et_single80,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('singleflight'); ylabel('e(t)'); xlabel('residence time'); legend('20 rpm ','50rpm','80 rpm'); xlim([0 1800]); figure hold on %plot(t_sax20,tbar_sax20*et_sax20,'--ks','linewidth',2, 'MarkerSize',8); %plot(t_sax50,tbar_sax50*et_sax50,':*k','linewidth',2, 'MarkerSize',8); %h_sax = plot(t_sax80,tbar_sax80*et_sax80,'-.ok','linewidth',2, 'MarkerSize',8); plot((0:length(ft_sax20)-1)*deltat,tbar_sax20*et_sax20,'--sr','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot((0:length(ft_sax50)-1)*deltat,tbar_sax50*et_sax50,':*b','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_sax = plot((0:length(ft_sax80)-1)*deltat,tbar_sax80*et_sax80,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('saxton'); ylabel('e(t)'); xlabel('residence time'); legend('20 rpm ','50rpm','80 rpm'); xlim([0 2100]); figure hold on 78

82 plot(t_single20,tbar_single20*et_single20,'--rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r','MarkerEdgeColor','r'); plot(t_maddox20,tbar_maddox20*et_maddox20,':*b','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','b','MarkerEdgeColor','b'); h_all_dl = plot(t_sax20,tbar_sax20*et_sax20,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k','MarkerEdgeColor','k'); title('all at 20 rpm'); ylabel('e(t)'); xlabel('dimentionless residence time'); legend('singleflight ','Maddox','Saxton'); xlim([0 4.2]); figure hold on plot([0:length(et_single20)-1]*deltat,tbar_single20*et_single20,'-- rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot([0:length(et_maddox20)- 1]*deltat,tbar_maddox20*Et_maddox20,':*b','LineWidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_all_d = plot([0:length(et_sax20)-1]*deltat,tbar_sax20*et_sax20,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('all at 20 rpm'); ylabel('e(t)'); xlabel('residence time(sec)'); legend('singleflight ','Maddox','Saxton'); xlim([0 2100]); %% figure hold on plot((0:length(ft_single20)-1)*deltat,tbar_single20*ft_single20,'-- rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot((0:length(ft_single50)- 1)*deltat,tbar_single50*Ft_single50,':*b','LineWidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_single_ft = plot((0:length(ft_single80)- 1)*deltat,tbar_single80*Ft_single80,'-.og','LineWidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('singleflight'); ylabel('f(t)'); xlabel('cumulative residence time'); legend('20 rpm ','50rpm','80 rpm'); xlim([0 2100]); figure hold on plot((0:length(ft_sax20)-1)*deltat,tbar_sax20*ft_sax20,'--rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot((0:length(ft_sax50)-1)*deltat,tbar_sax50*ft_sax50,':*b','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_sax_ft = plot((0:length(ft_sax80)-1)*deltat,tbar_sax80*ft_sax80,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('saxton'); ylabel('f(t)'); xlabel('cumulative residence time'); legend('20 rpm ','50rpm','80 rpm'); 79

83 xlim([0 2100]); figure hold on plot(t_single20,tbar_single20*ft_single20,'--rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot(t_maddox20,tbar_maddox20*ft_maddox20,':*b','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_all_dl_ft = plot(t_sax20,tbar_sax20*ft_sax20,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('all at 20 rpm'); ylabel('f(t)'); xlabel('dimentionless Cumulative residence time'); legend('singleflight ','Maddox','Saxton'); xlim([0 4.2]); figure hold on plot((0:length(ft_single20)-1)*deltat,tbar_single20*ft_single20,'-- rs','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','r'); plot((0:length(ft_maddox20)- 1)*deltat,tbar_maddox20*Ft_maddox20,':*b','LineWidth',2, 'MarkerSize',8,'MarkerEdgeColor','b'); h_all_d_ft = plot((0:length(ft_sax20)-1)*deltat,tbar_sax20*ft_sax20,'-.ok','linewidth',2, 'MarkerSize',8,'MarkerEdgeColor','k'); title('all at 20 rpm'); ylabel('f(t)'); xlabel('cumulative Residence time(sec)'); legend('singleflight ','Maddox','Saxton'); xlim([0 2100]); %% saveas(h_single,'single','tif') saveas(h_sax,'saxton','tif') saveas(h_all_dl,'all_dimless','tif') saveas(h_all_d,'all_dim','tif') saveas(h_single_ft,'single_ft','tif') saveas(h_sax_ft,'saxton_ft','tif') saveas(h_all_dl_ft,'all_dimless_ft','tif') saveas(h_all_d_ft,'all_dim_ft','tif') variance_single20 =sum((((0:length(et_single20)-1)- tbar_single20).^2).*et_single20'*deltat); variance_single50 =sum((((0:length(et_single50)-1)- tbar_single50).^2).*et_single50'*deltat); variance_single80 =sum((((0:length(et_single80)-1)- tbar_single80).^2).*et_single80'*deltat); variance_maddox20 =sum((((0:length(et_maddox20)-1)- tbar_maddox20).^2).*et_maddox20'*deltat); variance_sax20 =sum((((0:length(et_sax20)-1)- tbar_sax20).^2).*et_sax20'*deltat); variance_sax50 =sum((((0:length(et_sax50)-1)- tbar_sax20).^2).*et_sax50'*deltat); variance_sax80 =sum((((0:length(et_sax80)-1)- tbar_sax20).^2).*et_sax80'*deltat); 80

84 disp(' table ') disp('screw Configuration Screw Speed Tbar(min) Variance') disp([' Single flight 20 ' num2str(tbar_single20/60,2) ' ' num2str(variance_single20,2)]); disp([' Single flight 50 ' num2str(tbar_single50/60,2) ' ' num2str(variance_single50,2)]); disp([' Single flight 80 ' num2str(tbar_single80/60,2) ' ' num2str(variance_single80,2)]); disp([' Maddox 20 ' num2str(tbar_maddox20/60,2) ' ' num2str(variance_maddox20,2)]); disp([' Saxton 20 ' num2str(tbar_sax20/60,2) ' ' num2str(variance_sax20,2)]); disp([' Saxton 50 ' num2str(tbar_sax50/60,2) ' ' num2str(variance_sax50,2)]); disp([' Saxton 80 ' num2str(tbar_sax80/60,2) ' ' num2str(variance_sax80,2)]); save processed_data 81

85 Sample Images Mixing section Screw speed (rpm) Mixing degree % Dowlex Mixing degree % Eraclene Mixing degree % Mix1 Mixing degree % Mix2 20 Single flight Maddock Saxton 80 82