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In-line imaging measurements of particle size, velocity and concentration in a particulate two-phase flow
Particuology 13 (2014) 106–113
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In-line imaging measurements of particle size, velocity and concentration in a particulate two-phase flow Xiaozhen Chen, Wu Zhou ∗ , Xiaoshu Cai, Mingxu Su, Hailong Liu Institute of Particle and Two-phase Flow Measurement, University of Shanghai for Science and Technology, Shanghai 200093, China
a r t i c l e
i n f o
Article history: Received 18 October 2012 Received in revised form 9 March 2013 Accepted 29 March 2013 Keywords: In-line measurements Particle trajectory Image processing Multi-parameters
a b s t r a c t A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens. In this work, the image pre-processing steps include stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. A top-hat transformation is found to be very effective for the binarization of images with non-uniform background illumination. Particle trajectories measured within a certain exposure time are used to directly obtain particle size and velocity. The particle concentration is calculated by using the statistics of recognized particles within the field of view. We validate our method by analyzing experiments in a gas-droplet cyclone separator. This in-line image processing method can significantly reduce the measurement cost and avoid the data inversion process involved in the light scattering method. © 2013 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
1. Introduction Particulate two-phase flow is very popular in industries such as energy, environmental, processing, and power engineering. Parameter characterization of the particle phase is important but difficult because of its complexity and randomness (Shi, 2004). The simultaneous determination of multiple particle parameters is one of the hotspots in measurement technique research for two-phase flow, especially for in-line measurements. Methods for in-line particle measurement in a dispersed twophase flow mainly include the optical, ultrasonic, and electric methods. The laser-based particle size analyzer is widely used in particle size measurement (Black, McQuay & Bonin, 1996), but is rarely used to measure the particle velocity and spatial distribution of particle concentration (Inaba & Matsumoto, 1999). In addition, determination of the particle refractive index is still a difficult problem (Kinoshita, 2001), and the laser-based particle size analyzer is not optimized to measure the two-phase flow at very low particle concentrations. The phase Doppler particle analyzer (PDPA) (Doudou, 2005; Du, Yao, & Lin, 2005; Han, Wang, & Ma, 2010; van den Moortel, Azario, Santini, & Tadrist, 1998) can simultaneously measure particle size, velocity, and concentration, but its
∗ Corresponding author. Tel.: +86 021 55277764. E-mail address: usst [email protected] (W. Zhou).
pointwise measurement method makes an assumption of spherical particles, which is not always true in actual processes. Particle image velocimetry (PIV) (Kumara, Elseth, Halvorsen, & Melaaen, 2010) is a flow-field visualization technology that permits measurement of the instantaneous velocity and its related properties in fluids seeded with tracer particles, but does not provide any information about the concentration and size of the actual particles (Kashyap, Chalermsinsuwan, & Gidaspow, 2011). In the last few years, ultrasonic attenuation spectroscopy (UAS) and focused beam reflectance measurements (FBRM) have emerged in industrial applications for measuring the concentration and particle size distribution (PSD) in dense two-phase flows (Sarkar, Doan, Ying, & Srinivasan, 2009). In addition, the particle concentration in terms of the particle volume fraction has been measured using a microchannel with 12 multi-layered electrodes, which bases its measurements on the cross-sectional capacitances of the microparticles in the flow (Othman et al., 2013). To our knowledge, however, there is no in-line and in situ measurement technique that can simultaneously measure the size, velocity, and concentration of particles in an extremely diluted two-phase flow. It is well known that image-based methods offer the potential to extract both qualitative and quantitative PSD information based on direct visualization of the process. Image processing also provides a deeper understanding of the process by providing more realistic and credible two-dimensional information on the particle shape and size (Li et al., 2006; Scott et al., 2001). The
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http://dx.doi.org/10.1016/j.partic.2013.03.005
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Fig. 1. The trace of moving particles with a longer exposure time: (a) image of moving particles and (b) related parameters.
rapid progress in high-speed, online digital imaging sensors (both charge-coupled device (CCD)- and complementary metal-oxide semiconductor (CMOS)-based) has inspired wide industrial application of this technique for in-line detection of various parameters. Image-based analysis provides a promising option for real-time measurements of particle shape and size distribution, and is the basis of instruments such as the particle vision and measurement (PVM) probe from Mettler-Toledo International Inc. (Switzerland), the particle image analyzer (PIA) from Mess Technik Schwartz Gmbh (Germany), the in situ particle viewer (ISPV) from Perdix Components Ltd. (UK), and online microscopy from Glaxo Smith Kline (UK) (Barrett & Glennon, 2002; Qu, Louhi-Kultanen, & Kallas, 2006; Wang, Roberts, & Ma, 2008). Digital imaging techniques have been widely applied in power engineering for such measurements as particle size distribution (Gao, Yan, Lu, & Carter, 2012), the investigation of interparticle collision, and velocity measurements of pneumatically conveyed particles (Song, Peng, Lu, Yang, & Yan, 2009). The imaging technique was even integrated with electrostatic sensing to simultaneously measure size distribution and velocity (Carter, Yan, & Cameron, 2005). Recently, the concept of ‘laser sheet image analysis’ was introduced into a non-intrusive image-based technique and used to measure particle concentration fields in solid–liquid systems. This technique, however, was developed for cases with high particle concentration (Tamburini, Cipollina, Micale, & Brucato, 2013). To be effective for in-line process control, image analysis needs to be accurate, fast, robust, and tolerant of in-line image quality. In this paper, we propose a method based on trajectory image analysis for the in situ and in-line measurement of particle size, velocity, and concentration in dilute particulate two-phase flow. The concise design of our in-line measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens, and it has the advantage of being low in cost, fast in response, simple in structure, and easy to install. We also developed a simple and dependable image processing program to extract the particle information. The system can be operated in a complicated industrial environment and is found to be stable for in-line measurements. 2. Measurement principles In a two-phase flow, the particles that need to be measured move quite fast. To measure the size of moving particles with conventional image-based methods, a very short exposure time is usually required to freeze the moving particles and capture clear
images. Therefore, a high-power pulsed laser must be employed as the light source to provide both a very short exposure time and adequate illumination. Furthermore, to measure the velocity of moving particles, it is necessary to acquire two images of the moving particles in a very short time interval and then use a cross-correlation technique to process the two images. To accomplish this, one must use a powerful, double-pulsed laser as the light source. Such a system is complex and not conducive for in-line measurements. If the exposure time is not short enough, the resulting image of the moving particle will be shown as a trace of the particle motion. An image of moving particles with a long exposure time is shown in Fig. 1(a) and (b) shows the length, S, and width, D, of the trace, which are related to the velocity and the diameter of the moving particle, respectively. In fact, we assume the diameter of the particle and D to be equivalent. By controlling the exposure time within a certain value , the velocity, , and the diameter, D, of the moving particle may be calculated as follows:
v=˛
L S−D n − nw =˛ = ˛w l ,
(1)
and D = ˛wnw ,
(2)
where ˛ is the amplification ratio of the camera, w is the pixel dimension, and nl and nw are the number of pixels in the length and width of the trace, respectively. A micrometer is used to calibrate the number of pixels in the length direction and get the amplification ratio, ˛. The relationship between one pixel and the actual length, as measured on the micrometer, is shown in Fig. 2. The pixel size and the amplification ratio, ˛, directly influence the accuracy and resolution of the measurement technique. A smaller pixel size and a larger ˛ can get a higher measurement accuracy and resolution, but a larger ˛ corresponds to a smaller view area and, therefore, an optimized ˛ should be selected. The appropriate exposure time needs to be controlled because with too short of an exposure the particle movement will be too small, increasing the error in the measurement results. On the other hand, if the exposure time is too long, the particle can move out of the field of view, leaving an incomplete particle trace. As shown in Fig. 1(a), moving particles can leave traces with clear boundaries or fuzzy boundaries. The trace images of particles with clear boundaries mean those particles are within the depth of field, while the fuzzy particle images are of moving particles that are out of the depth of field. During image processing, only those traces with a clear boundary are effective for processing to determine the size, velocity, and concentration of particles.
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Fig. 3. The flow chart of the image processing used in this work.
Fig. 2. The image of the micrometer calibration.
Therefore, the measurement volume of the system should be defined. Assuming the view area is WH, and ıl is the depth of field, then the measurement volume V is: V = WHıl .
(3)
By counting and processing all trace images with a clear boundary for a large number of image frames, we can statistically obtain the concentration of the particles as well as their size and velocity. If Z is the total number of counted image frames and D(n) is the size distribution of the measured particles, then the particle volume concentration, C, can be calculated as:
C=
N D3 (n)/6 n=1
(ZV )
,
(4)
where N is the total number of counted particles.
3. Image pre-processing For most on-site conditions, the image contrast ratios are not high enough for direct particle recognition and characterization. Especially for water droplet images, light scattering produces illumination but reduces the contrast ratio. In addition, a non-uniform background illumination also plays a role in low image contrast ratios. Therefore, before the particle recognition and information extraction processes mentioned in Section 2 can be implemented, pre-processing is essential to enhance the image contrast. Image pre-processing steps that we employ in this work are stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. The image processing flow chart we use is shown in Fig. 3.
3.1. Image reading and stitching To achieve a true in-line measurement, it is necessary to process images as fast as possible. To increase the processing speed, we implement our first pre-processing step of image stitching in which nine continuously obtained images are read in and stitched together into one new 3 × 3 array image.
3.2. Illumination correction and image binarization Image binarization converts an image of up to 256 gray levels into a black and white image, which is an easier format for object recognition. Otsu’s method (Otsu, 1979) and the maximum entropy method (Pun, 1981) are the most common algorithms for image thresholding, though for images with non-uniform background illumination, like those shown in Fig. 4(a), the changes in illumination across the image may cause some parts to be brighter or darker with no relationship to the presence of particles. Fig. 4(b) and (c) show that it is impossible to extract particles directly from an original image with only one global threshold, either using Otsu’s method (Fig. 4(b)) or the maximum entropy method (Fig. 4(c)). Therefore, it is crucial to implement illumination correction or background equalization prior to image binarization. The top-hat transform (Bai, Zhou, & Xue, 2012) is an operation that removes small elements and details from given images, thereby extracting the background illumination. There are two types of top-hat transforms, namely the white top-hat transform and the black top-hat transform. The latter is also called the bottom-hat transform, and is the transform we employ because it is appropriate for images with dark objects on a bright background (Fig. 4(a)). First, a closing operation (Roerdink, 2000) using a disk structuring element with a radius of 50 pixels is applied to Fig. 4(a) to isolate the non-uniform background, which is shown in Fig. 4(d). The 50-pixel radius value is chosen to ensure that the structuring element is much larger than any individual particle in the image. Second, the bottom-hat transform is completed and Fig. 4(e) is obtained by comparing the background Fig. 4(d) and the original image Fig. 4(a). Although the background illumination of the initial image is quite non-uniform, the obtained image after the bottom-hat transform provides a high enough contrast ratio to extract the bright particles. At this point, image thresholding can be implemented, and binarization results of image Fig. 4(e) using Otsu’s method and the maximum entropy method are shown in Fig. 4(f) and (g), respectively. The above-mentioned results underscore the necessity and superiority of top/bottom-hat transforms for non-uniform illumination typical in images. Also, we can see that after illumination correction, the two general binarization methods lead to similar results. In this work, therefore, we use the bottom-hat transform and Otsu’s threshold for binarization. The automatic processing in this step lays the foundation for in-line measurements.
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Fig. 4. Processes of illumination correction and binarization: (a) original image with non-uniform illumination, (b) binarization of original image with Otsu’s method, (c) binarization of original image with the maximum entropy method, (d) background of image in (a) obtained by a closing operation, (e) processed image after bottom-hat transformation, (f) binarization of image in (e) with Otsu’s method, and (g) binarization of image in (e) with the maximum entropy method.
3.3. Denoising and elimination of incomplete particles Information noise is inevitable during image formation, transmission, and recording. As shown in Fig. 4(f) and (g), a large amount of image noise appears as small spots, which can easily be misread as particles in data processing. Therefore, an opening operation (Roerdink, 2000) was applied to suppress these small noise spots. In addition, particles moving across the image edges during the exposure period have incomplete trajectories and need to be removed before statistical analysis can be done. The image results after removing noise and incomplete particles are shown in Fig. 5(a) and (b). It should be mentioned that the particle concentration after
these elimination operations may be slightly lower than the actual concentration. 3.4. Elimination of defocused particles As described in Section 2, the images with defocused fuzzy boundaries should be removed during concentration measurements since these images represent particles out of the depth of field. After labeling the recognized particles in the binary image in Fig. 5(b), gradients of the original image were correspondingly calculated, as shown in Fig. 6(a). An empirical value for the gradient threshold is used, and Fig. 6(b) shows the image
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Fig. 5. Processes of denoising and eliminating incomplete particles: (a) elimination of noise spots in Fig. 4(f) and (b) removal of bright areas on the edges of the image in Fig. 5(a).
result after removing the defocused particles. Compared with Figs. 5(b) and 6(b) presents fewer particles, which means that the particles out of focus are successfully removed and only particles in the depth of field are retained. After the series of image pre-processing steps outlined here, particle traces in Fig. 6(b) are treated as ellipses whose minor axis length is taken as the particle size, D, and the major axis length corresponds to S in Fig. 1(b). At this point, the methods mentioned in Section 2 are used to determine the particle velocity and concentration. In this work, all processes can be automatically performed with a typical processing time of ∼5 s by a computer with 2.00 GB of RAM and a duo core CPU running at 2.00 GHz. 4. Experiments
Fig. 6. Process of removal of defocused particles: (a) gradient of the original image and (b) after elimination of defocused particles in Fig. 5(b).
rotational effects and gravity. The experimental system is shown in Fig. 7. Water was supplied to the nozzle by the dose pump, while an air compressor provided compressed air as the carrying gas. A buffer tank was used to maintain a steady gas-droplet flow into the cyclone, and different working conditions were achieved by adjusting the pump pressure.‘ 4.2. Measurement system The in-line measurement system using an image-based method consists of an image acquisition component, a camera stepping device, and an image processing component, as shown in Fig. 8. The image acquisition components were composed of an industrial digital CCD camera, a telecentric lens, and a high-power LED
4.1. Experimental device Cyclone separators are applied widely in industries dealing in petroleum, chemicals, building materials, and environmental protection. Compared with other separation devices such as filtration and settlement, the cyclone separator has the advantage of having no moving parts, a simple structure, a stable operation, low power consumption, and a high separation efficiency. The separation efficiency is strongly related to the particle size, velocity, and concentration in the cyclone inlet. Accurate measurement of these parameters is therefore helpful to understand the separation characteristics. To evaluate the proposed measuring method, experiments were carried out on the inlet of a gas-droplet cyclone separator, which removes the fine droplets of liquid from the gaseous stream using
Fig. 7. Schematic diagram of experimental setup.
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Fig. 8. In-line measurement system.
light. For this work we chose a CCD camera with a frame exposure mode to ensure that we could capture images of moving particles as particle traces. The maximum resolution of the camera was 1392 pixels × 1040 pixels and the minimum exposure time was 22 s. Since it was unnecessary to have an extremely short exposure period, a high-power LED (3 W) rather than a laser was chosen as the light source. The LED light and the CCD camera were located at two sides of the measuring tube and along the optical axis of the telecentric lens. The light and camera were coupled together to ensure stability of the viewing field, even in the event of vibration. The positions of measured particles are random in the viewing field depth, and imaging with an ordinary lens generally leads to a certain degree of distortion, thereby making distal particles smaller. Therefore, the image particle size can be the result of two factors; the real particle size and the distance between the particle and the camera. The usual way to solve this confusion is to limit the depth of field to ensure that the distal position of the measured particles is essentially constant. However, this will also severely reduce the number of particles per measurement, leading to the necessity of more frame acquisitions and a longer measurement time to improve the statistics. However, if the aperture of the lens is fixed in the object focal plane, such problems can be solved. A lens designed according to this principle is called a telecentric lens (Gharib, Pereira, Dabiri, Hove, & Modarress, 2002), and it ensures that the particle size is independent of the distance between particle and the camera. The particle size can be determined definitely by corresponding the pixel size in the image with a certain calibrated magnification of the telecentric lens. The magnification of our lens was initially calibrated by a micrometer, as introduced in Section 2 and shown in Fig. 2. A stepping system was designed to move the camera along the optical axis direction of the lens to measure the particle along the radial position of the inlet tube. The system mainly consisted of an optical measurement device, a stepper motor and an equipment control box. The stepper motor had a spatial resolution of 1.6 micrometers and was controlled by the equipment control box, which was connected to the computer via USB port. The communication between the CCD and computer was performed via gigabit Ethernet with a transfer rate of 1000 Mbps. The coaxial cable can support a maximum distance of 100 m so that remote control of the camera can be achieved in an industrial site. A photo of the test site is shown in Fig. 9.
Fig. 9. A photo of the test site.
indicates that the image traces of moving droplets are equivalent to ellipses, as introduced in Sections 2 and 3, and the minor axis can be taken as the particle size. In the experiments, the flow rate of water was changed gradually to achieve four different conditions, and Fig. 11 shows the frequency and cumulative distributions of the particle sizes under these four conditions. It can be seen that the particle sizes are widely distributed but mainly concentrated in the range of 5–10 m, and there is only a small number of larger droplets under all experimental conditions. Table 1 illustrates the average diameters and standard deviations, as well as the corresponding water concentrations in the four different conditions. These concentrations are too low to be
5. Results and discussions Fig. 10 shows a clear image of water droplets with low velocities. We are able to assume that the measured droplets are spheroids and the ratio between the minor axis and major axis is ∼1. This
Fig. 10. Clear image of water droplets.
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Fig. 11. Particle size distributions under four different conditions.
measured by other methods such as laser diffraction. Although the water flow rate is not quantitatively determined, we qualitatively show that the water flow rate increased from condition 1 to condition 4. Although the concentration gradually increased, the average particle diameter did not change much because of the experimental control and working conditions. The use of the buffer tank can potentially be another reason. Table 1 shows that the number of particles with the median diameter, DN50 , is relatively stable, while in each condition a different number of large droplets occurs, which significantly affects the Sauter mean diameter. Therefore, the DN50 was approximately a constant value except in condition 4 (Fig. 11 and Table 1). These stable and reasonable data attest to the capability and superiority of the in-line image method proposed in this paper. The corresponding velocity field of the measured water droplets in Fig. 4(a) is illustrated in Fig. 12(a), while Fig. 12(b) exemplifies a typical velocity distribution for our experiments. Because the air flow from the piston air compressor is unstable, the particle velocities change significantly during the experiments, with the change occurring in a range of 0.0–2.5 m/s. It should be mentioned that the measured velocities are projections of real velocities on the perpendicular direction of the optic axis. If this were not so, errors can occur and influence the accuracy of the PSD because of the change in the trajectories of particles. In this work, the measurement section is the inlet of the cyclone, where the flow of droplets can be treated as plug flow. Therefore, the assumption that particles are stationary in the direction of the optic axis is reasonable. Nevertheless, a 3-D flow-field measurement is the focus of a future study of ours.
Other errors that will appear in the statistics of the particle concentration are those due to the presence of defocused particles and overlapping particles, which can be compensated in the software to a certain extent by estimating the particle concentration and pretesting. The method outlined in this work is mainly optimized for
Table 1 Measured parameters for the four conditions. Condition
1
2
3
4
Sauter mean diameter (m) DN50 (m) Standard deviation (m) Water concentration (ppm)
16.65 6.72 5.62 13.31
17.22 6.60 5.76 15.33
17.73 6.56 5.53 20.63
15.77 7.35 5.22 18.98
Fig. 12. Velocity distribution of particles at a particular instant in time: (a) velocity field of Fig. 4(a) and (b) typical velocity distribution.
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applications in a relatively dilute two-phase flow. As the particle concentration increases, it is conceivable that the overlap effect of the particles will gradually dominate the process. An experimental system based on the 3-D structure can be used to improve the spatial resolution and weaken the overlap effect in the future. 6. Conclusions A novel in-line measurement method for particle size, velocity, and concentration is developed and validated on a gas-droplet cyclone separator. Based on a blurred image processing method, the measuring system was made up of a common industrial CCD camera, an inexpensive LED light, and a telecentric lens. Because we avoided using a high-power laser for a light source, the measurement cost is greatly decreased. The long-distance communication made possible by the coaxial cable between the CCD camera and the computer made in situ measurements practicable. In image pre-processing, the adaptive binarization threshold can correct for the non-uniform background illumination and obtain much better results than by using a simple global threshold. The particle size can be definitely determined using the telecentric lens, and the particle velocity and concentration are also calculated simultaneously. In the gas-droplet cyclone separator, we are able to show that as the water flow rate changes, the particle concentration shows the same trend as the particle size, but the average particle diameter does not significantly change. In conclusion, this method proposes a new direction for in-line and non-intrusive measurements of moving particles in an extremely dilute particulate two-phase flow. Acknowledgments The authors gratefully acknowledge support from the National Natural Science Foundation of China (51206112, 51076106, 51176128) and the Science and Technology Support Program in Shanghai (10540501000). References Bai, X., Zhou, F., & Xue, B. (2012). Image enhancement using multi scale image features extracted by top-hat transform. Optics & Laser Technology, 44(2), 328–336. Barrett, P., & Glennon, B. (2002). Characterizing the metastable zone width and solubility curve using lasentec FBRM and PVM. Chemical Engineering Research and Design, 80, 799–805. Black, D. L., McQuay, M. Q., & Bonin, M. P. (1996). Laser-based techniques for particlesize measurement: A review of sizing methods and their industrial applications. Progress in Energy and Combustion Science, 22, 267–306. Carter, R. M., Yan, Y., & Cameron, S. D. (2005). On-line measurement of particle size distribution and mass flow rate of particles in a pneumatic suspension using
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In-line imaging measurements of particle size, velocity and concentration in a particulate two-phase flow Xiaozhen Chen, Wu Zhou ∗ , Xiaoshu Cai, Mingxu Su, Hailong Liu Institute of Particle and Two-phase Flow Measurement, University of Shanghai for Science and Technology, Shanghai 200093, China
a r t i c l e
i n f o
Article history: Received 18 October 2012 Received in revised form 9 March 2013 Accepted 29 March 2013 Keywords: In-line measurements Particle trajectory Image processing Multi-parameters
a b s t r a c t A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens. In this work, the image pre-processing steps include stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. A top-hat transformation is found to be very effective for the binarization of images with non-uniform background illumination. Particle trajectories measured within a certain exposure time are used to directly obtain particle size and velocity. The particle concentration is calculated by using the statistics of recognized particles within the field of view. We validate our method by analyzing experiments in a gas-droplet cyclone separator. This in-line image processing method can significantly reduce the measurement cost and avoid the data inversion process involved in the light scattering method. © 2013 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
1. Introduction Particulate two-phase flow is very popular in industries such as energy, environmental, processing, and power engineering. Parameter characterization of the particle phase is important but difficult because of its complexity and randomness (Shi, 2004). The simultaneous determination of multiple particle parameters is one of the hotspots in measurement technique research for two-phase flow, especially for in-line measurements. Methods for in-line particle measurement in a dispersed twophase flow mainly include the optical, ultrasonic, and electric methods. The laser-based particle size analyzer is widely used in particle size measurement (Black, McQuay & Bonin, 1996), but is rarely used to measure the particle velocity and spatial distribution of particle concentration (Inaba & Matsumoto, 1999). In addition, determination of the particle refractive index is still a difficult problem (Kinoshita, 2001), and the laser-based particle size analyzer is not optimized to measure the two-phase flow at very low particle concentrations. The phase Doppler particle analyzer (PDPA) (Doudou, 2005; Du, Yao, & Lin, 2005; Han, Wang, & Ma, 2010; van den Moortel, Azario, Santini, & Tadrist, 1998) can simultaneously measure particle size, velocity, and concentration, but its
∗ Corresponding author. Tel.: +86 021 55277764. E-mail address: usst [email protected] (W. Zhou).
pointwise measurement method makes an assumption of spherical particles, which is not always true in actual processes. Particle image velocimetry (PIV) (Kumara, Elseth, Halvorsen, & Melaaen, 2010) is a flow-field visualization technology that permits measurement of the instantaneous velocity and its related properties in fluids seeded with tracer particles, but does not provide any information about the concentration and size of the actual particles (Kashyap, Chalermsinsuwan, & Gidaspow, 2011). In the last few years, ultrasonic attenuation spectroscopy (UAS) and focused beam reflectance measurements (FBRM) have emerged in industrial applications for measuring the concentration and particle size distribution (PSD) in dense two-phase flows (Sarkar, Doan, Ying, & Srinivasan, 2009). In addition, the particle concentration in terms of the particle volume fraction has been measured using a microchannel with 12 multi-layered electrodes, which bases its measurements on the cross-sectional capacitances of the microparticles in the flow (Othman et al., 2013). To our knowledge, however, there is no in-line and in situ measurement technique that can simultaneously measure the size, velocity, and concentration of particles in an extremely diluted two-phase flow. It is well known that image-based methods offer the potential to extract both qualitative and quantitative PSD information based on direct visualization of the process. Image processing also provides a deeper understanding of the process by providing more realistic and credible two-dimensional information on the particle shape and size (Li et al., 2006; Scott et al., 2001). The
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http://dx.doi.org/10.1016/j.partic.2013.03.005
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Fig. 1. The trace of moving particles with a longer exposure time: (a) image of moving particles and (b) related parameters.
rapid progress in high-speed, online digital imaging sensors (both charge-coupled device (CCD)- and complementary metal-oxide semiconductor (CMOS)-based) has inspired wide industrial application of this technique for in-line detection of various parameters. Image-based analysis provides a promising option for real-time measurements of particle shape and size distribution, and is the basis of instruments such as the particle vision and measurement (PVM) probe from Mettler-Toledo International Inc. (Switzerland), the particle image analyzer (PIA) from Mess Technik Schwartz Gmbh (Germany), the in situ particle viewer (ISPV) from Perdix Components Ltd. (UK), and online microscopy from Glaxo Smith Kline (UK) (Barrett & Glennon, 2002; Qu, Louhi-Kultanen, & Kallas, 2006; Wang, Roberts, & Ma, 2008). Digital imaging techniques have been widely applied in power engineering for such measurements as particle size distribution (Gao, Yan, Lu, & Carter, 2012), the investigation of interparticle collision, and velocity measurements of pneumatically conveyed particles (Song, Peng, Lu, Yang, & Yan, 2009). The imaging technique was even integrated with electrostatic sensing to simultaneously measure size distribution and velocity (Carter, Yan, & Cameron, 2005). Recently, the concept of ‘laser sheet image analysis’ was introduced into a non-intrusive image-based technique and used to measure particle concentration fields in solid–liquid systems. This technique, however, was developed for cases with high particle concentration (Tamburini, Cipollina, Micale, & Brucato, 2013). To be effective for in-line process control, image analysis needs to be accurate, fast, robust, and tolerant of in-line image quality. In this paper, we propose a method based on trajectory image analysis for the in situ and in-line measurement of particle size, velocity, and concentration in dilute particulate two-phase flow. The concise design of our in-line measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens, and it has the advantage of being low in cost, fast in response, simple in structure, and easy to install. We also developed a simple and dependable image processing program to extract the particle information. The system can be operated in a complicated industrial environment and is found to be stable for in-line measurements. 2. Measurement principles In a two-phase flow, the particles that need to be measured move quite fast. To measure the size of moving particles with conventional image-based methods, a very short exposure time is usually required to freeze the moving particles and capture clear
images. Therefore, a high-power pulsed laser must be employed as the light source to provide both a very short exposure time and adequate illumination. Furthermore, to measure the velocity of moving particles, it is necessary to acquire two images of the moving particles in a very short time interval and then use a cross-correlation technique to process the two images. To accomplish this, one must use a powerful, double-pulsed laser as the light source. Such a system is complex and not conducive for in-line measurements. If the exposure time is not short enough, the resulting image of the moving particle will be shown as a trace of the particle motion. An image of moving particles with a long exposure time is shown in Fig. 1(a) and (b) shows the length, S, and width, D, of the trace, which are related to the velocity and the diameter of the moving particle, respectively. In fact, we assume the diameter of the particle and D to be equivalent. By controlling the exposure time within a certain value , the velocity, , and the diameter, D, of the moving particle may be calculated as follows:
v=˛
L S−D n − nw =˛ = ˛w l ,
(1)
and D = ˛wnw ,
(2)
where ˛ is the amplification ratio of the camera, w is the pixel dimension, and nl and nw are the number of pixels in the length and width of the trace, respectively. A micrometer is used to calibrate the number of pixels in the length direction and get the amplification ratio, ˛. The relationship between one pixel and the actual length, as measured on the micrometer, is shown in Fig. 2. The pixel size and the amplification ratio, ˛, directly influence the accuracy and resolution of the measurement technique. A smaller pixel size and a larger ˛ can get a higher measurement accuracy and resolution, but a larger ˛ corresponds to a smaller view area and, therefore, an optimized ˛ should be selected. The appropriate exposure time needs to be controlled because with too short of an exposure the particle movement will be too small, increasing the error in the measurement results. On the other hand, if the exposure time is too long, the particle can move out of the field of view, leaving an incomplete particle trace. As shown in Fig. 1(a), moving particles can leave traces with clear boundaries or fuzzy boundaries. The trace images of particles with clear boundaries mean those particles are within the depth of field, while the fuzzy particle images are of moving particles that are out of the depth of field. During image processing, only those traces with a clear boundary are effective for processing to determine the size, velocity, and concentration of particles.
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Fig. 3. The flow chart of the image processing used in this work.
Fig. 2. The image of the micrometer calibration.
Therefore, the measurement volume of the system should be defined. Assuming the view area is WH, and ıl is the depth of field, then the measurement volume V is: V = WHıl .
(3)
By counting and processing all trace images with a clear boundary for a large number of image frames, we can statistically obtain the concentration of the particles as well as their size and velocity. If Z is the total number of counted image frames and D(n) is the size distribution of the measured particles, then the particle volume concentration, C, can be calculated as:
C=
N D3 (n)/6 n=1
(ZV )
,
(4)
where N is the total number of counted particles.
3. Image pre-processing For most on-site conditions, the image contrast ratios are not high enough for direct particle recognition and characterization. Especially for water droplet images, light scattering produces illumination but reduces the contrast ratio. In addition, a non-uniform background illumination also plays a role in low image contrast ratios. Therefore, before the particle recognition and information extraction processes mentioned in Section 2 can be implemented, pre-processing is essential to enhance the image contrast. Image pre-processing steps that we employ in this work are stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. The image processing flow chart we use is shown in Fig. 3.
3.1. Image reading and stitching To achieve a true in-line measurement, it is necessary to process images as fast as possible. To increase the processing speed, we implement our first pre-processing step of image stitching in which nine continuously obtained images are read in and stitched together into one new 3 × 3 array image.
3.2. Illumination correction and image binarization Image binarization converts an image of up to 256 gray levels into a black and white image, which is an easier format for object recognition. Otsu’s method (Otsu, 1979) and the maximum entropy method (Pun, 1981) are the most common algorithms for image thresholding, though for images with non-uniform background illumination, like those shown in Fig. 4(a), the changes in illumination across the image may cause some parts to be brighter or darker with no relationship to the presence of particles. Fig. 4(b) and (c) show that it is impossible to extract particles directly from an original image with only one global threshold, either using Otsu’s method (Fig. 4(b)) or the maximum entropy method (Fig. 4(c)). Therefore, it is crucial to implement illumination correction or background equalization prior to image binarization. The top-hat transform (Bai, Zhou, & Xue, 2012) is an operation that removes small elements and details from given images, thereby extracting the background illumination. There are two types of top-hat transforms, namely the white top-hat transform and the black top-hat transform. The latter is also called the bottom-hat transform, and is the transform we employ because it is appropriate for images with dark objects on a bright background (Fig. 4(a)). First, a closing operation (Roerdink, 2000) using a disk structuring element with a radius of 50 pixels is applied to Fig. 4(a) to isolate the non-uniform background, which is shown in Fig. 4(d). The 50-pixel radius value is chosen to ensure that the structuring element is much larger than any individual particle in the image. Second, the bottom-hat transform is completed and Fig. 4(e) is obtained by comparing the background Fig. 4(d) and the original image Fig. 4(a). Although the background illumination of the initial image is quite non-uniform, the obtained image after the bottom-hat transform provides a high enough contrast ratio to extract the bright particles. At this point, image thresholding can be implemented, and binarization results of image Fig. 4(e) using Otsu’s method and the maximum entropy method are shown in Fig. 4(f) and (g), respectively. The above-mentioned results underscore the necessity and superiority of top/bottom-hat transforms for non-uniform illumination typical in images. Also, we can see that after illumination correction, the two general binarization methods lead to similar results. In this work, therefore, we use the bottom-hat transform and Otsu’s threshold for binarization. The automatic processing in this step lays the foundation for in-line measurements.
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Fig. 4. Processes of illumination correction and binarization: (a) original image with non-uniform illumination, (b) binarization of original image with Otsu’s method, (c) binarization of original image with the maximum entropy method, (d) background of image in (a) obtained by a closing operation, (e) processed image after bottom-hat transformation, (f) binarization of image in (e) with Otsu’s method, and (g) binarization of image in (e) with the maximum entropy method.
3.3. Denoising and elimination of incomplete particles Information noise is inevitable during image formation, transmission, and recording. As shown in Fig. 4(f) and (g), a large amount of image noise appears as small spots, which can easily be misread as particles in data processing. Therefore, an opening operation (Roerdink, 2000) was applied to suppress these small noise spots. In addition, particles moving across the image edges during the exposure period have incomplete trajectories and need to be removed before statistical analysis can be done. The image results after removing noise and incomplete particles are shown in Fig. 5(a) and (b). It should be mentioned that the particle concentration after
these elimination operations may be slightly lower than the actual concentration. 3.4. Elimination of defocused particles As described in Section 2, the images with defocused fuzzy boundaries should be removed during concentration measurements since these images represent particles out of the depth of field. After labeling the recognized particles in the binary image in Fig. 5(b), gradients of the original image were correspondingly calculated, as shown in Fig. 6(a). An empirical value for the gradient threshold is used, and Fig. 6(b) shows the image
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Fig. 5. Processes of denoising and eliminating incomplete particles: (a) elimination of noise spots in Fig. 4(f) and (b) removal of bright areas on the edges of the image in Fig. 5(a).
result after removing the defocused particles. Compared with Figs. 5(b) and 6(b) presents fewer particles, which means that the particles out of focus are successfully removed and only particles in the depth of field are retained. After the series of image pre-processing steps outlined here, particle traces in Fig. 6(b) are treated as ellipses whose minor axis length is taken as the particle size, D, and the major axis length corresponds to S in Fig. 1(b). At this point, the methods mentioned in Section 2 are used to determine the particle velocity and concentration. In this work, all processes can be automatically performed with a typical processing time of ∼5 s by a computer with 2.00 GB of RAM and a duo core CPU running at 2.00 GHz. 4. Experiments
Fig. 6. Process of removal of defocused particles: (a) gradient of the original image and (b) after elimination of defocused particles in Fig. 5(b).
rotational effects and gravity. The experimental system is shown in Fig. 7. Water was supplied to the nozzle by the dose pump, while an air compressor provided compressed air as the carrying gas. A buffer tank was used to maintain a steady gas-droplet flow into the cyclone, and different working conditions were achieved by adjusting the pump pressure.‘ 4.2. Measurement system The in-line measurement system using an image-based method consists of an image acquisition component, a camera stepping device, and an image processing component, as shown in Fig. 8. The image acquisition components were composed of an industrial digital CCD camera, a telecentric lens, and a high-power LED
4.1. Experimental device Cyclone separators are applied widely in industries dealing in petroleum, chemicals, building materials, and environmental protection. Compared with other separation devices such as filtration and settlement, the cyclone separator has the advantage of having no moving parts, a simple structure, a stable operation, low power consumption, and a high separation efficiency. The separation efficiency is strongly related to the particle size, velocity, and concentration in the cyclone inlet. Accurate measurement of these parameters is therefore helpful to understand the separation characteristics. To evaluate the proposed measuring method, experiments were carried out on the inlet of a gas-droplet cyclone separator, which removes the fine droplets of liquid from the gaseous stream using
Fig. 7. Schematic diagram of experimental setup.
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Fig. 8. In-line measurement system.
light. For this work we chose a CCD camera with a frame exposure mode to ensure that we could capture images of moving particles as particle traces. The maximum resolution of the camera was 1392 pixels × 1040 pixels and the minimum exposure time was 22 s. Since it was unnecessary to have an extremely short exposure period, a high-power LED (3 W) rather than a laser was chosen as the light source. The LED light and the CCD camera were located at two sides of the measuring tube and along the optical axis of the telecentric lens. The light and camera were coupled together to ensure stability of the viewing field, even in the event of vibration. The positions of measured particles are random in the viewing field depth, and imaging with an ordinary lens generally leads to a certain degree of distortion, thereby making distal particles smaller. Therefore, the image particle size can be the result of two factors; the real particle size and the distance between the particle and the camera. The usual way to solve this confusion is to limit the depth of field to ensure that the distal position of the measured particles is essentially constant. However, this will also severely reduce the number of particles per measurement, leading to the necessity of more frame acquisitions and a longer measurement time to improve the statistics. However, if the aperture of the lens is fixed in the object focal plane, such problems can be solved. A lens designed according to this principle is called a telecentric lens (Gharib, Pereira, Dabiri, Hove, & Modarress, 2002), and it ensures that the particle size is independent of the distance between particle and the camera. The particle size can be determined definitely by corresponding the pixel size in the image with a certain calibrated magnification of the telecentric lens. The magnification of our lens was initially calibrated by a micrometer, as introduced in Section 2 and shown in Fig. 2. A stepping system was designed to move the camera along the optical axis direction of the lens to measure the particle along the radial position of the inlet tube. The system mainly consisted of an optical measurement device, a stepper motor and an equipment control box. The stepper motor had a spatial resolution of 1.6 micrometers and was controlled by the equipment control box, which was connected to the computer via USB port. The communication between the CCD and computer was performed via gigabit Ethernet with a transfer rate of 1000 Mbps. The coaxial cable can support a maximum distance of 100 m so that remote control of the camera can be achieved in an industrial site. A photo of the test site is shown in Fig. 9.
Fig. 9. A photo of the test site.
indicates that the image traces of moving droplets are equivalent to ellipses, as introduced in Sections 2 and 3, and the minor axis can be taken as the particle size. In the experiments, the flow rate of water was changed gradually to achieve four different conditions, and Fig. 11 shows the frequency and cumulative distributions of the particle sizes under these four conditions. It can be seen that the particle sizes are widely distributed but mainly concentrated in the range of 5–10 m, and there is only a small number of larger droplets under all experimental conditions. Table 1 illustrates the average diameters and standard deviations, as well as the corresponding water concentrations in the four different conditions. These concentrations are too low to be
5. Results and discussions Fig. 10 shows a clear image of water droplets with low velocities. We are able to assume that the measured droplets are spheroids and the ratio between the minor axis and major axis is ∼1. This
Fig. 10. Clear image of water droplets.
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Fig. 11. Particle size distributions under four different conditions.
measured by other methods such as laser diffraction. Although the water flow rate is not quantitatively determined, we qualitatively show that the water flow rate increased from condition 1 to condition 4. Although the concentration gradually increased, the average particle diameter did not change much because of the experimental control and working conditions. The use of the buffer tank can potentially be another reason. Table 1 shows that the number of particles with the median diameter, DN50 , is relatively stable, while in each condition a different number of large droplets occurs, which significantly affects the Sauter mean diameter. Therefore, the DN50 was approximately a constant value except in condition 4 (Fig. 11 and Table 1). These stable and reasonable data attest to the capability and superiority of the in-line image method proposed in this paper. The corresponding velocity field of the measured water droplets in Fig. 4(a) is illustrated in Fig. 12(a), while Fig. 12(b) exemplifies a typical velocity distribution for our experiments. Because the air flow from the piston air compressor is unstable, the particle velocities change significantly during the experiments, with the change occurring in a range of 0.0–2.5 m/s. It should be mentioned that the measured velocities are projections of real velocities on the perpendicular direction of the optic axis. If this were not so, errors can occur and influence the accuracy of the PSD because of the change in the trajectories of particles. In this work, the measurement section is the inlet of the cyclone, where the flow of droplets can be treated as plug flow. Therefore, the assumption that particles are stationary in the direction of the optic axis is reasonable. Nevertheless, a 3-D flow-field measurement is the focus of a future study of ours.
Other errors that will appear in the statistics of the particle concentration are those due to the presence of defocused particles and overlapping particles, which can be compensated in the software to a certain extent by estimating the particle concentration and pretesting. The method outlined in this work is mainly optimized for
Table 1 Measured parameters for the four conditions. Condition
1
2
3
4
Sauter mean diameter (m) DN50 (m) Standard deviation (m) Water concentration (ppm)
16.65 6.72 5.62 13.31
17.22 6.60 5.76 15.33
17.73 6.56 5.53 20.63
15.77 7.35 5.22 18.98
Fig. 12. Velocity distribution of particles at a particular instant in time: (a) velocity field of Fig. 4(a) and (b) typical velocity distribution.
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applications in a relatively dilute two-phase flow. As the particle concentration increases, it is conceivable that the overlap effect of the particles will gradually dominate the process. An experimental system based on the 3-D structure can be used to improve the spatial resolution and weaken the overlap effect in the future. 6. Conclusions A novel in-line measurement method for particle size, velocity, and concentration is developed and validated on a gas-droplet cyclone separator. Based on a blurred image processing method, the measuring system was made up of a common industrial CCD camera, an inexpensive LED light, and a telecentric lens. Because we avoided using a high-power laser for a light source, the measurement cost is greatly decreased. The long-distance communication made possible by the coaxial cable between the CCD camera and the computer made in situ measurements practicable. In image pre-processing, the adaptive binarization threshold can correct for the non-uniform background illumination and obtain much better results than by using a simple global threshold. The particle size can be definitely determined using the telecentric lens, and the particle velocity and concentration are also calculated simultaneously. In the gas-droplet cyclone separator, we are able to show that as the water flow rate changes, the particle concentration shows the same trend as the particle size, but the average particle diameter does not significantly change. In conclusion, this method proposes a new direction for in-line and non-intrusive measurements of moving particles in an extremely dilute particulate two-phase flow. Acknowledgments The authors gratefully acknowledge support from the National Natural Science Foundation of China (51206112, 51076106, 51176128) and the Science and Technology Support Program in Shanghai (10540501000). References Bai, X., Zhou, F., & Xue, B. (2012). Image enhancement using multi scale image features extracted by top-hat transform. Optics & Laser Technology, 44(2), 328–336. Barrett, P., & Glennon, B. (2002). Characterizing the metastable zone width and solubility curve using lasentec FBRM and PVM. Chemical Engineering Research and Design, 80, 799–805. Black, D. L., McQuay, M. Q., & Bonin, M. P. (1996). Laser-based techniques for particlesize measurement: A review of sizing methods and their industrial applications. Progress in Energy and Combustion Science, 22, 267–306. Carter, R. M., Yan, Y., & Cameron, S. D. (2005). On-line measurement of particle size distribution and mass flow rate of particles in a pneumatic suspension using
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