Characterization of fast-growing foams in bottling processes by endoscopic imaging and convolutional neural networks

Regardless of whether the occurrence of foams in industrial processes is desirable or not, the knowledge about the characteristics of their formation and morphology is crucial. This study addresses the measuring of characteristics in foam and the trailing bubbly liquid that result from air bubble entrainment by a plunging jet in the environment of industry-like bottling process es of non-carbonated beverages. Typically encountered during the bottling of fruit juices, this process configuration is characterized by very fast filling speeds with high dynamic system parameter changes. Especially in multiphase systems with a sensitive disperse phase like gas bubbles, the task of its measurement turns out to be difficult. The aim of the study is to develop and employ an image processing capability in real geometries under realistic industrial conditions, e.g. as opposed to a narrow measurement chamber. Therefore, a typically sized test bottle was only slightly modified to adapt an endoscopic measurement technique and to acquire image data in a minimally invasive way. Two convolutional neural networks (CNNs) were employed to analyze irregular non-overlapping bubbles and circular overlapping bubbles. CNNs provide a robust object recognition for varying image qualities and therefore can cover a broad range of process conditions at the cost of a time-consuming training process. The obtained single bubble and population measurements allow approximation, correlation and interpretation of the bubble size and shape distributions within the foam and in the bubbly liquid. The classification of the measured foam morphologies and the influence of operating conditions are presented. The applicability to the described test case as an industrial multiphase process reveals high potential for a huge field of operations for particle size and shape measurement by the introduced method

An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection

This article presents an efficient and economical automatic image analysis technique for the droplet characterization in a liquid–liquid dispersion. The methodology employs a combination of the Satoshi Suzuki's [Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process. 1985;30:32–46] find contours algorithm and the method of minimal enclosing circle identification, proposed by Emo Welzl [Smallest enclosing disks (balls and ellipsoids). Berlin, Heidelberg: Springer; 1991. p. 359–370. chapter 24], to achieve the objectives. The round object detection algorithm has been designed for the identification and verification of correct droplets in the mixture which helped to increase the accuracy of automatic detection. Tests have been performed on various sets of images obtained during several emulsification processes and contain examples of droplets which differ in size, density, volume and appearance etc. An effective communication between the two methodologies and newly introduced algorithms demonstrated an accuracy of 90% or above in the measurement of droplet size distribution and Sauter mean diameters through an automatic vision-based system

Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 mu m microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6 x 10(-5) using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes

Proper Orthogonal Decomposition as a technique for identifying two-phase flow pattern based on electrical impedance tomography

Collecting of very large amount of data from experimental measurement is a common practice in almost every scientific domains. There is a great need to have specific techniques capable of extracting synthetic information, which is essential for understanding and modelling the specific phenomena. The Proper Orthogonal Decomposition (POD) is one of the most powerful data analysis methods for multivariate and nonlinear phenomena. Generally, POD is a procedure that takes a given collection of input experimental or numerical data and creates an orthogonal basis constituted by functions estimated as the solutions of an integral eigenvalue problem known as a Fredholm equation. This paper proposes a novel approach by utilising POD to identify flow structure in horizontal pipeline, specially, for slag, plug and wavy stratified air-water flow regimes, , in which POD technique extends the current evaluation procedure of electrical impedance tomography [31]. This capability is extended by the implementation of POD as an identifier for typical horizontal two phase flow regimes. Direct POD method introduced by Lumley and Snapshot POD method introduced by Sirovich are applied The POD snapshot matrices are reconstructed from electrical tomography measurement under specific flow conditions. It is expected that this study may provide new knowledge on two phase flow dynamics in a horizontal pipeline and useful information for further prediction of multiphase flow regime

System Integration - A Major Step toward Lab on a Chip

Microfluidics holds great promise to revolutionize various areas of biological engineering, such as single cell analysis, environmental monitoring, regenerative medicine, and point-of-care diagnostics. Despite the fact that intensive efforts have been devoted into the field in the past decades, microfluidics has not yet been adopted widely. It is increasingly realized that an effective system integration strategy that is low cost and broadly applicable to various biological engineering situations is required to fully realize the potential of microfluidics. In this article, we review several promising system integration approaches for microfluidics and discuss their advantages, limitations, and applications. Future advancements of these microfluidic strategies will lead toward translational lab-on-a-chip systems for a wide spectrum of biological engineering applications