Many 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.
Particle size plays a major role in segregation phenomena in mixing process. In pharmaceutical industry, it is essential to have a proper analysis to make sure that the good product is well mixed and to prevent segregation. In this study, Digital Image processing has been used as an alternative method to thief probe, to study the effect of particle size on mixing and segregation process. The experiment started with execution of image acquisition process of mixing process. The images taken were further analyzed using Image Processing Tool in MATLAB software. The experiments were done with two different arrangement of particle in the bed using air velocity of 0.94 ms −1 . The mixture was considered to be well mixed when the intensity of red of colour histogram is approximately constant. For the layered position, the good mixing time for set 1, 2, 3 and 4 were achieved at 62, 84, 86 and 74 s respectively. Then, for random position, homogeneous mixture were achieved at 62, 76, 82 and 72 s for set 1, 2, 3 and 4 respectively. This finding has been quantified by using Lacey Mixing Index where the value obtained was nearly to 1.0 which showed uniform mixing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.