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Online measurement of the bubble size distribution in medium-consistency oxygen delignification HEIKKI MUTIKAINEN1*, NATALIYA STROKINA2, TUOMAS EEROLA2, LASSE LENSU2, HEIKKI KÄLVIÄINEN2 and JARI KÄYHKÖ1 FiberLaboratory Mikkeli University of Applied Sciences Vipusenkatu 10, FI-57200 Savonlinna, Finland 2 Machine Vision and Pattern Recognition Laboratory (MVPR) School of Engineering Science Lappeenranta University of Technology (LUT) P.O. Box 20, FI-53851 Lappeenranta, Finland * Corresponding author (
[email protected]) 1
SUMMARY Progress of a new imaging-based measurement of oxygen bubble size was continued by developing an automated method for bubble detection from image data. The imaging application was found to be able to measure oxygen bubble size in mill conditions when using imaging apparatus connected to the sampling valve after the first oxygen stage mixer in a Finnish softwood kraft pulp mill. This appears to be the first time that bubble size distribution has been measured in a mill-based oxygen delignification process. Mixer rotor speed had a clearly measurable effect on the oxygen bubble size distribution in the pulp suspension at the window of the imaging assembly. The volumetric average bubble size with mixer rotor speed 890 rpm was 0.064 mm while with mixer rotor speed 1380 rpm it was 0.037 mm. The automated method estimated bubble volumes reasonably well, enabling further development of the online measurement application.
KEYWORDS Dispersion, oxygen delignification, mixing, bubble detection, MC-pulp, chemical pulp, image processing, machine vision
INTRODUCTION
In terms of process engineering, the quality of oxygen dispersion strongly affects the total efficiency of the oxygen delignification stage of a pulp line. Because mixing oxygen into mediumconsistency pulp is a very energy-intensive unit operation, mixer design and operation parameters should ideally be used to optimise the characteristics of the gas dispersion and control energy consumption. Until recently, this has not been possible due to harsh imaging conditions that have made it difficult to characterise this dispersion. However, recent progress in camera and illumination technologies has encouraged interest in developing imaging-based solutions that can provide completely new possibilities in measuring and understanding the phenomena connected to the mixing of gas into mediumconsistency pulp suspension.
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A new imaging-based oxygen dispersion bubble size measurement method was recently presented by Mutikainen et. al. (1). The method was found to be applicable for the characterisation of oxygen bubble size in medium-consistency brown-stock pulp suspensions in laboratory conditions. It appears that this might be the first such automated measurement method for bubble detection and gas dispersion state estimation. Due to low visibility in the suspension, the method was not capable of measuring through the body of suspension, so only the dispersion/bubble size near the measurement window at the surface of the chamber could be clearly measured. However, as the mixer machinery supplier states that gas is dispersed homogeneously into the suspension and oxygen dispersion is relatively stable, an external measurement method is an option to be considered in this particular case. Information about the gas is retrieved from the bubbles contained in the pulp suspension images. In earlier works (1-4), the bubbles were marked manually by an expert. This, however, is laborious and time-consuming, which motivates the development of automatic methods for bubble detection. In this work, we tackle this problem by applying automatic image processing based on the bubble detection method by Strokina et al. (5). The method utilises a geometry-based approach that adapts to the appearance changes of bubbles caused by, for example, illumination changes. The method has been shown to reliably predict the gas volume (5).
and artefacts to the be seen, difficult process conditions cause various distortions such asasnoise, noise, clutter and artefacts to to thethe 2. seen, the difficult difficult process processconditions conditionscause causevarious variousdistortions artefacts to the 2. As As can can be seen, the distortionssuch suchas noise,clutter clutter and artefacts loop are less noisy than those images, making making bubble detection challenging task. The images from the pilot mixing loop areare less noisy than those images, making bubble detection detection aaachallenging challengingtask. task.The Theimages imagesfrom fromthe thepilot pilotmixing mixing loop less noisy than those noisy than those by fibre However, variation ininbubble bubble appearance high and the images arearecluttered cluttered byby fibre from industrial process. However, However, variation variationin bubbleappearance appearanceisisishigh cluttered by fibre from the the industrial industrial process. highand andthe theimages imagesare cluttered fibre segments. At the industrial scale, gas manifests as a foam rather than separate bubbles, which complicates bubble segments. At the industrial scale, gas manifests as a foam rather than separate bubbles, which complicates bubble segments. At complicates bubble bubble industrial scale, gas manifests as a foam rather than separate bubbles, which complicates detection. between Moreover, the the higher the rotor speed, the more difficult is,is,even even for human, totodistinguish distinguish between detection. Moreover, higher the the rotor rotor speed, speed,the themore moredifficult difficultitititis, distinguish between detection. Moreover, the higher evenfor foraa ahuman, human,to distinguish between separate separate bubbles. bubbles. separate bubbles.
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Fig. pulp suspension images from Fig. 1. 1. Examples Examples of of pulp suspension images from Fig. 1. Examples of pulp pulp suspension images from Fig. 1. Examples of Fig. Examples ofloop. pulp suspension images from the pilot mixing loop. the pilot mixing the pilot mixing loop. the pilot pilot mixing mixing loop. loop. the
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Fig. 3. Pilot plant medium consistency loop (4).
Fig. 3. Pilot plant medium consistency loop (4).
The presented together with experimental and The paper paper is is organised organised as as follows: follows: (i) (i) the the imaging imaging equipment equipment is isTo presented together with the the experimental procedure andsuspens simulate the foaming found in typicalprocedure brown-stock The paper is organised organised as follows:were (i) the imaging equipment is presented together with the and experimental procedure and The paper is procedure and Experimental procedure machinery In this present work, experiments carried out in two machinery, is the detection machinery, (ii) (ii) the the image image analysis analysis method method for for bubble bubble detection detectionconcentration is described, described,of(iii) (iii) the results results of of bubble bubble detection in the the surface-active agent was added into in the bleached Pilot-scale mixing loop machinery, (ii) the the image is described, (iii) the results of bubble detection in the different environments: 1) aanalysis pilot-scale medium-consistency machinery, (ii) method for bubble detection detection in pilot (iv) finally, are and bars and the volumetric gas content was 20%. Rotameters were u pilot mixing mixing loop loop and and at at the the industrial industrial scale scale are are presented presented and and discussed, discussed, (iv) finally, conclusions conclusions are drawn drawn and future futurethe mixing loop, and 2) a mill environment using several mixer rotor Pilot-scale experiments were carried out using bleached pilot mixing loop and at the industrial scale are presented and discussed, (iv) finally, conclusions are drawn and future pilot mixing loop drawn and future The mixer rotor speeds evaluated were 1200, 1500 and 1800 rpm setup development setup development considered. speeds. The computedconsidered. bubble size properties made it possible to softwood kraft pulp in a mixing loop consisting of a MC pump, setup development considered. setup development corresponding to velocities of 0.45, 0.90 and 1.35 m/s in the r study the state of dispersion. Examples of the obtained images
an Andritz pilot-scale fluidising Proto-mixer, a reactor and a Imaging was undertaken from three locations/assemblies: the first dropping tower (Fig. 3). thesimulate rotor 70 the mm foaming before the outlet; second onto the pipe 0.55 m To found in the typical brown-stock artefacts to the images, making bubble detection a challenging suspensions after the mixer, the feeding point of the reactor. treated near in oxygen delignification, a specified Imaging equipment Imaging equipment task. The images from the pilot mixing loop are less noisy concentration of surface-active agent was added into the Mill environment Imaging equipment Imaging than those from the industrial process. However, variation in bleached pulp suspension. Pressure in the mixing loop was 1.5 Experiments in the mill environment were performed using a custo Experiments were using imaging apparatus the development progress has been Experiments wereis carried carried outimages using are imaging apparatus for which the development progress hasRotameters been previously previously bubble appearance high and out the cluttered by fibre for barswhich and the volumetric gas content was 20%. were 4). a sampling valve after the first oxygen stage mixer (Fig. Experiments were carried out using imaging apparatus for to which the development has been previously Experiments were carried out imaging apparatus for which the development progress has of been previously segments. At industrial scale, gasusing manifests a foam rather used described by Rantala (2), Mutikainen (3) Kumpulainen (4). The apparatus aa FOculus described bythe Rantala (2), Mutikainen (3)as and and Kumpulainen (4). The imaging apparatus consisted ofThe FOculus adjust theimaging gas content beforeprogress theconsisted experiments. mixer described by Rantala (2), Mutikainen (3) and Kumpulainen (4). The imaging apparatus consisted of a FOculus than separate bubbles, which complicates bubble detection. rotor speeds evaluated were 1200, 1500 and 1800 rpm and the described by Rantala (2), Mutikainen (3) and Kumpulainen (4). The imaging apparatus consisted of a FOculus FO531TB FO531TB camera, camera, aa Richard Richard Wolf Wolf 51 51 camera camera adapter adapter (4.85261.382) (4.85261.382) and and aa Richard Richard Wolf Wolf borescope borescope (6.05045.00). (6.05045.00). Moreover, the higher the rotor speed, the more difficult it is, volumetric pulp flows were 10, 20 or 30 L/s corresponding FO531TB camera, a Richard Wolf 51 camera adapter (4.85261.382) and a Richard Wolf borescope (6.05045.00). FO531TB camera, a Richard Wolf two 51 (4.85261.382) and aRecherche Richard Wolf borescope Illumination was out ST-15E stroboscopes by Industrie in pilot loop Illumination was carried carried out using using twocamera ST-15Eadapter stroboscopes by Contrôle Contrôle Recherche Industrie in the the (6.05045.00). pilot to loop even for a human, to distinguish between separate bubbles. velocities of 0.45, 0.90 and 1.35 m/s in the recirculation piping Illumination was carried out using two ST-15E stroboscopes by Contrôle Recherche Industrie in the pilot experiments and aa Cavitar Cavilux Smart diode laser Illumination was carried out using twopulsed ST-15E stroboscopes by experiments. Contrôle Recherche Industrie in the pilot loop loop experiments Cavitar Cavilux pulsed diode laser in in the the mill mill experiments. The paper isand organised as follows: (i)Smart the imaging equipment with a 0.168 m inner diameter. Imaging was undertaken from experiments and a Cavitar Cavilux Smart pulsed diode laser in the mill experiments. experiments and a Cavitar Cavilux Smart pulsed diode laser in the mill experiments. is presented together with the experimental procedure and three locations/assemblies: the first was placed onto the mixer,at Experimental procedure and machinery, (ii) the image analysis method for bubble detection is the interaction area of the rotor 70 mm before the outlet; the Experimental procedure and machinery machinery described, (iii) the results of bubble detection in the pilot mixing second onto the pipe 0.55 m after the mixer and the third onto the Experimental procedure and machinery Experimental procedure and machinery loop and at the industrial scale are presented and discussed, (iv) pipe 2.45 m after the mixer, near the feeding point of the reactor. Pilot-scale Pilot-scale mixing mixing loop loop finally, conclusions are drawn and future setup development Pilot-scale loop were Pilot-scale experiments Pilot-scale mixing experiments were carried carried out out using using bleached bleached softwood softwood kraft kraft pulp pulp in in aa mixing mixing loop loop consisting consisting of of aa MC MC Pilot-scale considered. mixing loop Mill environment are shown in Figures 1 and 2. As can be seen, the difficult process EXPERIMENTS EXPERIMENTS EXPERIMENTS conditions cause various distortions such as noise, clutter and EXPERIMENTS
Pilot-scale experiments were carried Proto-mixer, out using using bleached bleached softwood kraft pulp aa mixing loop pump, an pilot-scale fluidising aa reactor and (Fig. 3Fig. pump, an Andritz Andritz pilot-scale fluidising Proto-mixer, reactorsoftwood and aa dropping dropping towerin (Fig. 3Fig. 3). 3). Pilot-scale experiments were carried out kraft tower pulp in mixing loop consisting consisting of of aa MC MC Experiments in the mill environment were performed using a pump, an Andritz pilot-scale fluidising Proto-mixer, a reactor and a dropping tower (Fig. 3Fig. 3). pump, an Andritz pilot-scale fluidising Proto-mixer, a reactor and a dropping tower (Fig. 3Fig. 3).
EXPERIMENTS
Imaging equipment Experiments were carried out using imaging apparatus for which the development progress has been previously described by Rantala (2), Mutikainen (3) and Kumpulainen (4). The imaging apparatus consisted of a FOculus FO531TB camera, a Richard Wolf 51 camera adapter (4.85261.382) and a Richard Wolf borescope (6.05045.00). Illumination was carried out using two ST-15E stroboscopes by Contrôle Recherche Industrie in the pilot loop experiments and a Cavitar Cavilux Smart pulsed diode laser in the mill experiments.
custom-made sampling apparatus which was connected to a sampling valve after the first oxygen stage mixer (Fig. 4). Pulp was led from the sampling valve through the tube and to the discard channel, maintaining constant motion in the plug 4. sample. SamplingThe apparatus connected to fibre line. flowFig. pulp pressure in the sampling tube was 7.5 bars and the softwood brown-stock pulp consistency was 10.2%. Image data in the pilot loop and mill environment experiments was led from the and sampling throughrandomly the tube and to the werePulp collected continuously imagesvalve were selected plug flow pulp sample. The pressure in the sampling tube was 7.5 for analysis.
was 10.2%. Image data in the pilot loop and mill environment e Image method for bubble wereanalysis selected randomly for analysis. detection The bubbles appear as circular sets of pixels in the suspension image. Thus, digital image processing and analysis are applied to automatically detect bubbles in the images. The method Vol 68 No 2 April - June 2015
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The bubbles appear as circular sets of pixels in the suspension image. Thus, digital image processing and analysis are Air removal applied to automatically detect bubbles in the images. The method for bubble detection has been previously described (5), where the method’s performance on the pilot loop images was demonstrated. A more detailed description of the Mixer method,formethod parameter and parameter sensitivity analysis has also been described (6). The bubble bubble detection has selection been previously described (5), where RESULTS AND DISCUSSION detection formulated as detection of concentric circular arrangements (CCA). The CCA illustrated in Fig. the problem method’sis performance on a the pilot loop images was MC-Pump In order to develop an automated image analysis method, it A more detailed of within the method, 5Figuredemonstrated. 5 is a set of concentric circulardescription arcs located an annulus of a certain width. is necessary to obtain reference data, i.e., the ground truth.
method parameter selection and parameter sensitivity analysis Therefore, in both experiments in order to train the automated has also been described (6). The bubble detection problem is image analysis method and validate its detections, the bubbles formulated as a detection of concentric circular arrangements in the images were also marked manually. The ground truth (CCA). The CCA illustrated in Figure 5 is a set of concentric Steam line generation method has been reported by Mutikainen et al. (1). circular arcs located within an annulus of a certain width. Fig. 3. Pilot plant medium consistency loop (4). The bubbles are found using a hypothesise-optimise-verify Pilot medium-consistency mixing loop framework as shown in Figure 6. Since bubbles manifest themselves as objects with ridge edges, the solution for ridge Before the industrial-scale measurement, experiments were using atreated pilot mixing setup. Twenty-four microscopic To simulate the by foaming found typicalThe brown-stock suspensions in oxygen delignification, a specified detection described Lindeberg (7) isinadopted. images performed images with physical resolution of 2.65 by 2.01 mm (1600 by are filtered by a second derivative zero-mean Gaussian filter in concentration of surface-active agent was added into the bleached pulp suspension. Pressure in the mixing loop was 1.5 pixels) were taken using the setup and the bubbles were eight directions. For further gas processing, absolute of 1200 barsmodel. and the volumetric contentthewas 20%.value Rotameters were used to adjust the gas content before the experiments. Fig. 5. CCA the responses is taken, to capture both bright and dark edges. manually annotated. Example images are presented in Figure 7. Thedominant mixer rotor speedsofevaluated were 1200, and the pulp flows 10, 20 or 30 L/s Therpm volume of avolumetric bubble with radius R were was calculated The orientation the edge normal in each1500 point and is 1800 3 3 that with the bubbles of an = 4 � R , assumingpiping corresponding to velocities 0.45, and 1.35 recirculation a 0.168 are m inner diameter. computed as the maximum of theof eight filter0.90 responses. Non-m/sasinVthe approximately spherical shape. The data was divided into a area of The bubbles are found using a hypothesise-optimise-verify framework as shown in Figure 6. Since bubbles maximum suppression along the edge normal is applied to Imaging was undertaken from three locations/assemblies: the first was placed onto the mixer, at the interaction training set containing 397 randomly selected bubbles and a test manifest themselves as objects with ridge edges, the solution for ridge detection described by Lindeberg (7) is adopted. thin the edges. Finally, the CCA hypotheses are generated the rotor 70 mm before the outlet; the second onto the pipe 0.55 m after the mixer and the third onto the pipe 2.45 m containing the remainder of the bubbles. The parameters of by sampling at random from edge pixels followed by Gaussian CCA setfilter The images are filtered by a second derivative zero-mean in eight directions. For further processing, the after the mixer, near the feeding point of the reactor. the method were learnt using the training data. The results of parameter estimation. Bubbleisparameter optimisation is carried absolute value of the responses taken, to capture both bright and dark edges. The dominant orientation of the edge Millbyenvironment out minimising novel cost-function using the simplex the volume detection are presented in Figure 8. As can be seen, normalmethod in each(8).point is asprocessing the maximum of outputs the eight filter Non-maximum suppression along the the estimated volume distributionapparatus closely follows thewas manually Experiments in computed the mill environment were performed aresponses. custom-made sampling which connected to The proposed image methods a using determined volume distribution. The largest problem occurs edge normal is applied to thin the edges. Finally, the CCA hypotheses are generated by sampling at random from edge set of circles (detected bubbles) parameterised by their centre a sampling valve after the first oxygen stage mixer (Fig. 4). with small bubbles, whereout there the greatest novel deviation from pixels followed CCA is carried byisminimising costcoordinatesbyand radii.parameter estimation. Bubble parameter optimisation
Image analysis method for bubble detection
The bubbles appear as circular sets of pixels in the suspension image. Thus, digita applied to automatically detect bubbles in the images. The method for bubble dete (5), where the method’s performance on the pilot loop images was demonstrated. method, method parameter selection and parameter sensitivity analysis has als detection problem is formulated as a detection of concentric circular arrangements 5Figure 5 is a set of concentric circular arcs located within an annulus of a certain w
function using the simplex method (8). The proposed image processing methods outputs a set of circles (detected bubbles) parameterised by their centre coordinates and radii.
RESULTS AND DISCUSSION In order to develop an automated image analysis method, it is necessary to obtain reference data, i.e., the ground truth. Therefore, in both experiments in order to train the automated image analysis method and validate its detections, the bubbles in the images were also marked manually. The ground truth generation method has been reported by Mutikainen et al. (1). Pilot medium-consistency mixing loop Before the industrial-scale measurement, experiments were performed using a pilot mixing setup. Twenty-four microscopic images with physical resolution of 2.65 by 2.01 mm (1600 by 1200 pixels) were taken using the setup and Fig. 5.Sampling CCA model. Fig. 5. CCA model. Fig. apparatus connected to fibre line. Fig.4.were 4. Sampling apparatus connected to fibre line. are presented in Figure 7. the bubbles manually annotated. Example images The volume of a bubble with radius R was calculated as
, assuming that the bubbles are of an
Pulp was led from the sampling valve through the tube and to the discard channel, maintaining constant motion in the plug flow sample. Theare pressure in the using sampling atube was 7.5 bars and the softwood brown-stock pulp consistency Thepulpbubbles found hypothesise-optimise-verify framework as was 10.2%. Image data in the pilot loop and mill environment experiments were collected continuously and images manifest themselves as objects with ridge edges, the solution for ridge detection des were selected randomly for analysis.
The images are filtered by a second derivative zero-mean Gaussian filter in eight di absolute value of the responses is taken, to capture both bright and dark edges. Th normal in each point is computed as the maximum of the eight filter responses. N Fig. 6. Framework for bubble detection. (5) edge normal is applied to thin the edges. Finally, the CCA hypotheses are generate Fig. 6. Framework for bubble detection. (5) pixels followed by CCA parameter estimation. Bubble parameter optimisation is ca function 161 Appita using the simplex method (8). The proposed image processing methods bubbles) parameterised by their centre coordinates and radii. Technology • Innovation • Manufacturing • Environment
The The method method demonstrated demonstrated aa good good performance, performance, achie achie with a precision of 74%. The detection takes about 3 seconds with a precision of 74%. The detection takes about 3 seconds process control control point point of of view. view. Examples Examples of of the the bubble bubble detect detect process PEER REVIEW positives, positives, red red circles circles false false negatives, negatives, and and yellow yellow circles circles false false p p the ground truth volume. This is mainly due to the fact that small blob-like bubbles do not have the ridge edges expected by the model. This problem is, however, avoidable by increasing the resolution of the images. The method demonstrated a good performance, achieving a mean relative error of volume estimation of 28% with a precision of 74%. The detection takes about 3 seconds per image, which can be considered as real-time from the process control point of view. Examples of the bubble detection results are presented in Figure 7. Blue circles are true positives, red circles false negatives, and yellow circles false positives.
Mill environment The experiments with the pilot mixing loop suggest that the method for the automated detection of bubbles can be applied to the task of gas volume distribution estimation and provide satisfactory results. To bring the approach towards practical applications, further experiments were carried out in a real mill environment. Similar to the pilot loop experiment, image data from the mill environment were manually annotated by an expert. The annotations were used to study the bubble size distributions, as well as, to train and evaluate the automatic bubble detection algorithm. Figure 9 presents both the manually determined and automatically estimated volumetric cumulative bubble-size distributions when the mixer rotor speed was varied from 890 rpm to 1400 rpm. Figure 9 (a) shows that the bubble size distribution is highly dependent on the mixer rotor speed. The curvature of the distribution reveals that some considerably larger bubbles were detected from images. Curves of 890 rpm and 1000 rpmFig. reveal Fig. 7. 7. that with too-low mixer rotor speeds, bubble size distribution is too wide for manual image data analysis due to its limitations in terms of the number of analysed images.
Fig. 7. Examples of bubble detection in the images from the
pilot mixingof loop. Examples bubble Examples of bubble detection detection in in the the images images from from the the pilot pilot mi mi
Fig. 8. Automated estimation of the gas volume distribution in the pilot mixing loop.
Fig. 8. Automated estimation of the gas volume distribution in the pilot mixing loop.
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to theloop pilotexperiment, loop experiment, image datathefrom mill environment were manually annotated Similar Similar to the pilot image data from millthe environment were manually annotated by an by an expert. The annotations were used to study the bubble size distributions, as well as, to train and evaluate the automatic expert. The annotations were used to study the bubble size distributions, as well as, to train and evaluate the automatic bubble detection algorithm. Figure 9 presents both the manually determined and automatically estimated volumetric bubble detection algorithm. Figure 9 presents both the manually determined and automatically estimated volumetric cumulative bubble-size distributions when therotor mixer rotorwas speed wasfrom varied from rpm to 1400 rpm. cumulative bubble-size distributions when the mixer speed varied 890 rpm890 to 1400 rpm.
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Fig. 9. Volumetric cumulative bubble size distributions ofin oxygen 10.2% brown-stock suspension defined by (a) method manual method Fig. 9. Volumetric cumulative bubble size distributions of oxygen 10.2%inbrown-stock suspension defined by (a) manual and (b) automated method. and (b) automated method. Fig. 9. Volumetric cumulative bubble size distributions of oxygen in 10.2% brown-stock suspension defined by
(a)size manual method and (b) automated method. 9 (a)that shows the size bubble distribution highly dependent on therotor mixer rotor The speed. The curvature Figure 9Figure (a) shows the that bubble distribution is highlyis dependent on the mixer speed. curvature of the of the distribution reveals that some considerably larger bubbles were detected from images. Curves of 890 rpm and 1000 rpm distribution some were considerably larger 10 bubbles were detected from images. Curves 890 defined rpm and by 1000 as a function of mixer rotor of speed therpm manual and The smallest reveals detectedthat bubbles slightly under µm. From reveal that with too-low mixer rotor speeds, bubble size distribution is too wide for manual image data analysis reveal that withdistributions too-low mixer rotor speeds, is too wide for manual data that analysis due to due toof methods. Figureimage 10 shows the performance the volumetric in Figure 9 (a) it bubble can be size seendistribution that automated its limitations in terms of the number of analysed images. its limitations in terms of thedisappear number of analysed images. remarkably bigger bubbles from the image data at the automated bubble detection improves when the mixer rotor The smallest detected wereThis slightly µm.the the volumetric distributions 9remarkably (a) it can The between smallest detected bubbles were rpm. slightly under 10under µm. 10 From volumetric distributions in Figure 9Figure (a)that it can speed isFrom increased. This is connected to theinfact some point 1200 rpm and bubbles 1300 may be seen remarkably bigger bubbles disappear the image data atpoint somebetween pointfrom between 1200 and 1300 be seen be that remarkably bigger virtually bubbles disappear fromoxygen thefrom image data atbubbles some 1200 andrpm 1300 bigger disappear therpm image data atrpm. some rpm. point concluded to be that an area where homogenous This be concluded toarea be an area where virtually oxygen can be achieved the is lowest This may be may concluded to with be anthe where virtually homogenous oxygen1200 dispersion can 1300 be achieved thewith lowest between rpmdispersion and rpm. Awith human expert more dispersion can be achieved lowest mixer speeds underhomogenous mixer under speeds underThe current process conditions. The bubble size with arotor mixer rotorof speed of 1300 likely toaverage mark all of the large bubbles and may miss some of the current process conditions. volumetric average bubble sizevolumetric mixer speeds current process conditions. The volumetric average bubble size with a mixer speed 1300 with a mixer rotor speed of 1300 rpm was 0.040 mm, with the smaller ones. The automatic system, on the other hand, does not smallest measured bubble diameter 0.008 mm and the largest favour bubbles with a large size but instead may miss them due 0.136 mm. The overlapping of the 1300 and 1400 rpm curves to their larger amount of distortions in appearance compared indicates that an increase of the mixer rotor speed after 1300 to the smaller bubbles. Missing even just a few very large rpm might not have a clear measurable effect on bubble size bubbles has a huge effect on the estimated average size. This explains why the automatic method underestimates the mean distribution. From Figure 9 (b), it can be seen that there is a difference size, especially with lower rotor speeds where large bubbles are between the shape of the manually determined size distribution common. However, it should be noted that the trend is correct, and the corresponding automatically estimated distribution, i.e., large average bubble size causes a large estimated value especially at low rotor speeds. This is due to the low quality of and vice versa. The error seems to be systematic and can be the images and a very high number of bubbles, which causes the corrected if necessary. Figure 11 shows the scatter plot between manually automatic method to fail to detect some of the larger bubbles. However, it should be noted that the main trend, i.e., there are determined average bubble diameters and average bubble more large bubbles and the bubble homogeneity is lower with diameters determined by the automatic method. The R²-value low rotor speed, is also evident in the distributions computed with the polynomial fit is close to 0.95, meaning that the using the automatic method. This can also be seen in Figure 10, detection results using CCA can be considered to correlate well rpm was 0.040 mm, with the smallest measured bubble diameter 0.008 mm and the largest 0.136 mm. The overlapping which presents the volumetric average diameters of the bubbles with the manually determined average bubble diameters. of the 1300 and 1400 rpm curves indicates that an increase of the mixer rotor speed after 1300 rpm might not have a clear measurable effect on bubble size distribution.
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Fig.From10. Volumetric average diameter of bubbles as a function of Figure 9 (b), it can be seen that there is a difference between the shape of the manually determined size distribution and the corresponding automatically estimated distribution, especially at low rotor speeds. This is due to the mixer rotor speed thewhich CCA method and low quality of the images andcomputed a very high number by of bubbles, causes the automatic automatically method to fail to detect some of the larger bubbles. However, it should be noted that the main trend, i.e., there are more large bubbles and the bubble manually homogeneitydetermined is lower with low rotorground speed, is alsotruth evident inresults. the distributions computed using the automatic method. This can also be seen in Figure 10, which presents the volumetric average diameters of the bubbles as a function of mixer rotor speed defined by the manual and automated methods. Figure 10 shows that the performance of the automated bubble detection improves when the mixer rotor speed is increased. This is connected to the fact that remarkably bigger bubbles disappear from the image data at some point between 1200 rpm and 1300 rpm. A human expert is more likely to mark all of the large bubbles and may miss some of Technology • Innovation • Manufacturing • Environment the smaller ones. The automatic system, on the other hand, does not favour bubbles with a large size but instead may miss them due to their larger amount of distortions in appearance compared to the smaller bubbles. Missing even just a few very large bubbles has a huge effect on the estimated average size. This explains why the automatic method underestimates the mean size, especially with lower rotor speeds where large bubbles are common. However, it should be noted that the trend is correct, i.e., large average bubble size causes a large estimated value and vice versa. The error
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Fig. 11. Manually detected average bubble diameters versus CONCLUSIONS average bubble diameters determined by the automated method. An automated bubble detection method for an online measurement application for the characterisation of bubble size distribution in medium consistency pulp was studied. The imaging setup developed in the laboratory and at the pilot scale was found to be applicable to the mill scale using the imaging apparatus connected to the sampling valve after the first oxygen stage mixer in a Finnish softwood kraft pulp mill. The state of dispersion behind an imaging assembly window seemed similar to the earlier laboratory experiments. The mixer rotor speed had a clear measurable effect on the bubble size distribution of oxygen in a pulp suspension behind an imaging assembly window. The volumetric bubble size distributions revealed that considerably bigger bubbles vanish from the image data at some point between 1200 and 1300 rpm. The CCA method seemed to estimate bubble volumes reasonably well, with a computation time of 3 seconds per image, making it a potential approach to the characterisation of gas dispersion in the pulp-making process. However, it should be noted that the detection method has problems with small blob-like bubbles, leading to a slightly shifted size distribution. Moreover, with low quality images, some of the large bubbles remained undetected, causing average bubble size to be underestimated. Therefore, further method development is required.
PEER REVIEW CONCLUSIONS
An automated bubble detection method for an online measurement application for the characterisation of bubble size distribution in medium consistency pulp was studied. The imaging setup developed in the laboratory and at the pilot scale was found to be applicable to the mill scale using the imaging apparatus connected to the sampling valve after the first oxygen stage mixer in a Finnish softwood kraft pulp mill. The state of dispersion behind an imaging assembly window seemed similar to the earlier laboratory experiments. The mixer rotor speed had a clear measurable effect on the bubble size distribution of oxygen in a pulp suspension behind an imaging assembly window. The volumetric bubble size distributions revealed that considerably bigger bubbles vanish from the image data at some point between 1200 and 1300 rpm. The CCA method seemed to estimate bubble volumes reasonably well, with a computation time of 3 seconds per image, making it a potential approach to the characterisation of gas dispersion in the pulp-making process. However, it should be noted that the detection method has problems with small blob-like bubbles, leading to a slightly shifted size distribution. Moreover, with low quality images, some of the large bubbles remained undetected, causing average bubble size to be underestimated. Therefore, further method development is required.
ACKNOWLEDGEMENTS
The research was carried out in the PulpVision project (LUT projects nos. 70010/10 and 70040/11, MUAS project nos. 2294/31/2009 and 2293/31/2009), funded by Tekes (the Finnish Funding Agency for Technology and Innovation), the European Union and the participating companies.
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Original manuscript received 10 July 2014, revision accepted 14 November 2014
Vol 68 No 2 April - June 2015
164
