particle technology

A Review of Image Processing Technique in Particle Mixing Analysis

Norazah Abdul Rahman, Syaidatul Akma Mohd Zuki, Faculty of Chemical Engineering

Universiti Teknologi Mara Shah Alam, MALAYSIA

[email protected], [email protected]

Ihsan Mohd Yassin Faculty of Electrical Engineering

Universiti Teknologi Mara Shah Alam, MALAYSIA [email protected]

AbstractMany industries, such as chemical, mining, food, pharmaceutical and agriculture require mixing as part of the production process. Several industries such as pharmaceutical and mining, mixing is a critical process that plays a significant role towards the quality of the final product. The mixing process, therefore, must be scrutinized to ensure homogeneity of mixtures. The characteristics of a mixing process can be analysed by either invasive or non-invasive method. Because of disadvantages of invasive methods, non-invasive methods such as Positron Emission Particle Tracking (PEPT), Discrete Element Model (DEM) Simulation and Magnetic Resonance Imaging (MRI) have been used to analyze the mixing process. However, these non-invasive methods used high and advanced technology in their applications. Moreover, they are expensive techniques and require stringent safety procedures. Therefore, image processing has become an interesting new method for studying particle property and a necessary means for particles analysis. This paper presents a review of image processing applications in mixing. The paper begins with a review of invasive and non-invasive methods for evaluating mixing characteristics, followed by a review of recent application of image processing techniques for mixing process.

Keywords-image processing; mixing; homogeneity; non-invasive method

INTRODUCTION Mixing (or blending) is a process that combines two or

more materials to form one substance or mass. It is an important process in many industrial applications that involve the blending of large quantities of granular materials such as pharmaceutical, chemical, agricultural, mining, building materials, explosives, and food industries [1]. In some industrial applications, blending and packaging constitute the entire production process, while in others, blending or mixing is a minor step in a series of long and complex process flow. Several mixing types exists, namely single-phase liquid, liquid-liquid, solid-solid, solid-liquid and gas-liquid mixing [2].

There are three principal mechanism by which mixing occurs, namely convection, diffusion and shear [3]. All mixers operate based on one of these three mechanisms. Convective mixing occurs by motions of particles within the

mixture, which create contact area between the different components. Diffusion mixing occurs when particles roll over each other on a sloping surface of powder [2]. In shear mixing, shear stresses give rise to slip zone and mixing takes place by interchange of particles between layers within the zone [1]. In free-flowing powders, diffusive and shear mixing give rise to size segregation. Therefore for such powders, convection is the major mixing mechanism [4].

This paper presents a review of image processing applications in mixing. The paper begins with a review of invasive and non-invasive methods for evaluating mixing characteristics, followed by a review of recent application of image processing techniques for mixing process.

REMARKS ON PARTICLE MIXING Some applications of mixing require simple combination

of particles, while others demand a high degree of homogeneity. In most industries, uniform particles mixing are desired for best quality of the final product. For example, in the pharmaceutical industry, it is important to achieve a high degree of homogeneity because of safety implications related to its products [5]. It is essential that the same amount of the active ingredient be present in each individual tablet or capsule. In the case of highly energetic and explosive mixtures, a fixed ratio of coarse-to-fine particles and the maximum packing density are both highly desirable to increase the products range and power.

Based on the above remarks, it is evident that the quality of mixture is important in industrial applications. Therefore, it is necessary to identify the quality of mixing during the mixing process.

Mixing process quality is identified by either invasive or non-invasive method. This review article focuses on quality determination in solids or particle mixtures. Although particle mixing is a simple process, there is possibility for segregation to occur. This is because of differences in properties of solids (particle size, density, shape and resilience) [6]. From these four factors, particle size is the main contributor for segregation.

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INVASIVE METHODS FOR MIXING QUALITY DETERMINATION

Invasive methods for mixing quality determination involve continuous sampling and evaluation of the mixture to determine its mixing quality. The sampling rules that should be followed are : (1) sample from moving stream powder, and (2) sample the whole stream for equal period of time [1]. In invasive quality determination, it is necessary to collect a manageable amount of powders that is representative of the batch as a whole. Invasive methods are very tedious as the mixture quality needs to be determined based on samples. The method also generates waste because sampling removes significant amounts of the mixture in order to determine its quality.

Furthermore, invasive sampling is inaccurate because of unreliable data collection methods. This is because in invasive assessment, the mixing process has to be stopped for sample collection at certain time intervals. The sampling process is also subject to many errors and great care must be taken to produce accurate and representative sample from the mixture powders as a whole.

NON-INVASIVE METHODS FOR MIXING QUALITY DETERMINATION

Non-invasive methods for mixing quality determination are preferable to invasive methods because of the weaknesses presented in Section III. Non-invasive methods do not disrupt the mixing process to determine its mixing quality [7]. Because of this, these methods present more accurate data and analysis on the mixing process[8]. Non-invasive method use advanced and expensive techniques such as Discrete Element Model (DEM) Simulation [16] and Magnetic Resonance Imaging (MRI) [9]. Many researchs have been done on the mixing analysis by using non-invasive method [6]-[21].

Magnetic Resonance Imaging (MRI) MRI is a non-invasive method that has been used in

medicine and pharmaceutical industries recently. This method is based on using magnetic field gradients to encode the nuclear magnetic resonance (NMR) signal with spatial information [9]. The NMR signal will be produced by certain nuclei such as 1H, 13C and 31P. In medical applications, MRI is required for obtaining high-resolution images of various organs within the human body by mapping the distribution of 1H, hydrogen nuclei [5].

Recently, there are several researchs that have been done by using MRI to investigate mixing quality. Hardy et al., 2007 [10] used MRI to measure the mixing state of fine powders. Besides, the MRI also has been used by Porion et al., 2004 [11] and Sommier et al., 2001 [12] to investigate the segregation and mixing process in a pharmaceutical blender. Both researchers used sugar beads and Turbular mixers. According to Mller et al., 2008 [13], there are many new techniques for upgrading the imaging speed and detailed information for MRI applications. MRI can reveal useful

informations such as voidage map and motion of bubble formation and bubble rise.

As already discussed before, the main advantage of MRI is non-invasive method and for medical purpose, it is a technique that do not involved radiation process which save to people who will have bad effects of radiation such as pregnant woman and babies [14]. The disadvantages of MRI method are expensive, noisy and require stringent safety procedures [14]. MRI equipment is also costly in maintenance and operation. MRI also requires a expensive magnetic resonance scanner [15].

Discrete Element Model (DEM) Simulation DEM is a technique that be widely used as an effective

method to investigate the engineering problem in granular and discontinuous materials such as in granular and powder flows. The DEM simulations have been applied in chemical, civil, pharmaceutical and mining industries [16]. Renzo [17] stated that the DEM simulation process requires integration of the Newton-Euler equation of motion for each particle and the full solution of the continuity and Navier-Stokes equations throughout the bed.

Discrete Element Model (DEM) simulation has been used to investigate the quality of mixing. There are several of them such as Rhodes et al., 2001 [10], Chaikittisilp et al., 2006 [18], Renzo et al., 2008 [11], Nakamura et al., 2007 [19] and Norouzi et al., 2011[20] have studied the mixing process in fluidized bed using DEM simulation. Research by Kaneko et al., 2000 [21] and Xu et al., 2010 [22] used DEM to analyze the characteristics of mixing process in single helical ribbon agitator and rotating drum respectively. Xu et al., 2010 [16] used 2D DEM simulation to studied mixing process have recommended 3D DEM simulation for their future research.

The advantage of DEM simulation is it can be used to simulate a wide range in size, type and moisture of granular flow. Beside that it is also allows a more detailed study of the microdynamics of powder flow than is often possible using physical experiments. However, DEM simulation has limitation for maximum number of particles and duration of a virtual simulation because they are limited by computational power.

With the advancement of technology and the reduction in cost of image acquisition equipments, image processing has become an interesting new method for studying particle property and a necessary means for particles analysis. The applications of image processing for non-invasive mixing quality determination are discussed in Section IV-C.

Table 1 shows the previous researches on studying the particles mixing process using different analysis method and reactor. The non-invasive methods, Discrete Element Model (DEM) and Magnetic Resonance Imaging (MRI) were used by the researchers in their studies.

Table 1 : Previous research on studying mixing process Authors Reactor Analysis Mixing


467

Method Analysis Gui and Fan, 2011 [23]

Fluidized bed

Discrete Element Model (DEM)

The effect of bubble on particle mixing characteristics

Hassanpour et al., 2011 [24]

Paddle mixer

Discrete Element Model (DEM)

Powder flow characteristic

Hardy et al., 2007 [6]

Simple chamber

Magnetic Resonance Imaging (MRI)

Mixing state

Porion et al., 2004 [7]

Turbular blender

Magnetic Resonance Imaging (MRI)

Influences for mixing and segregation

Digital Image Processing-based Methods for Non-invasive Mixing Quality Determination

Digital Image Processing (DIP) is a form of signal

processing that produces a digital image or a set of characteristics or parameters related to the image as its output. Interest in digital image processing methods stems from two principal application areas, namely: (1) improvement of pictorial information for human interpretation, and (2) processing of image data storage, transmission and representation for autonomous machine perception [25].

Due to cheap and readily available computational and image acquisition equipments, many researchers have done studies on the application of image processing on the mixing process. A study has been conducted by Daumann et al., 2009 [4] on determination of mixing time in discontinuous powder mixer by using image analysis on a horizontal twin-shaft paddle mixer with a spiral mixing tool. The method was tested on cement and ultramarine blue powders. The mixing efficiency was determined using image processing technique. The individual digital image, taken during the mixing process was analyzed via Matlab Image Processing Toolbox and the mixing efficiency was determined from the output. Thresholding was done by using Adobe Photoshop CS2. The experimental results are evaluated on the basis of known statistical interrelationship by means of Fokker-Planck equation for powder mixing.

Berthiaux et al., 2006 [26] studied the application of Principal Component Analysis (PCA) for characterizing homogeneity in powder mixing using image processing techniques. Test was performed on semolina powders with different colours. Images were taken using Charge-Coupled Device (CCD) camera. The results indicated that the homogeneity criterion was dependant on the scale of scrutiny chosen for the images used.

Another study by Le Coent et al., 2005 [27] used image processing for obtaining the mixing time in a glass stirred

vessel. The digital images were analyzed using box-counting method which is employed to perform the fractal image analysis of binary images. The images obtained were processed with the Scion Image Software. The results indicated that the proposed method allows the determination of the mixing time and quantification of the degree of homogeneity of the blend.

Muerza et. al. [28] has used image analysis to define and characterize homogeneity of mixing particles. Six continuous Sulzer static mixers were used in this research. The technique was tested on mixture of aspirin and semolina powders. An image analysis technique was developed as well as systemic model of the powder flow. The procedure starts by introducing the aspirin and semolina powder in the static mixer and the mixing process will be taken by a camera. Then, the recorded film is analyzed by using Visilog software.

CONCLUSIONS In this overview, an alternative non-invasive method to

study mixing and segregation of particles by using image processing has shown great potential to be studied further. Hence, a study on the suitability of low-cost cameras coupled with image processing has been proposed.

The proposed approach begins with the acquisition of an image of the mixing process. After preprocessing, the image will be analyzed using artificial intelligence techniques, namely artificial neural network to determine the level of homogeneity of mixture. All experiments will be done in MATLAB software.

The proposed method has the advantage of economy (as the image acquisition device is a standard web-camera), simplicity (as only images need to be used as inputs for the neural network) and non-invasiveness (eliminating the need for manual sampling and testing). Furthermore, this method will help researchers to gain deeper understanding of microstructure, property of powder particles and analyzing the powders mixture.

ACKNOWLEDGMENT The authors gratefully acknowledge the financial support

from Research Management Institute (RMI) from Universiti Teknologi Mara.

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