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Particle aggregation is essential in many industrial processes, spanning the pharmaceutical and food industries, polymer production, and the environment, among others. However, aggregation can also occur, in some processes, as a non-desired side effect. Thus, to be able to monitor aggregation in industrial processes is of high importance to guarantee that the final, required product characteristics are obtained. In this paper, we present an extensive review of the different techniques available for monitoring particle characteristics in industrial processes involving particulate materials, with special emphasis on aggregation processes. These methods include both off-line and on-line techniques, based either on image acquisition techniques or different radiation scattering techniques (light-scattering and ultrasound spectroscopy). The principles behind each technique are addressed, together with their relevant applications, advantages, and disadvantages.

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

An innovative method for bubble detection and characterization in multiphase flows using advanced computer vision and neural network algorithms is introduced. Building on the research group’s previous findings, this study combines high-speed video capture with advanced deep learning techniques to enhance bubble detection accuracy and dynamic analysis. In order to further develop a robust framework for detecting and analyzing bubble properties in multiphase flows, enabling accurate estimation of essential mass transfer parameters, a YOLOv9-based neural network was implemented for bubble segmentation and trajectory analysis, achieving high accuracy. Key contributions include the development of an averaged mass transfer model integrating experimental data, neural network outputs, and scaling algorithms, as well as validation of the proposed methodology through experimental studies, including high-speed video imaging and comparisons with mass transfer coefficients obtained via the sulfite method. By precisely characterizing critical parameters, the algorithm enables accurate gas transfer rate calculations, ensuring optimal conditions in various industrial applications. The neural network-based algorithm serves as a non-invasive platform for detailed characterization of bubble media, demonstrating high accuracy in experimental validation and significantly outperforming traditional techniques. This approach provides a robust tool for real-time monitoring and modeling of bubble flows, laying the foundation for novel, non-invasive methods to measure multiphase media properties.

Due to their many advantages over their counterparts, such as Z-source inverters (ZSIs), split-source inverters (SSIs) have recently received much attention as single-stage boost inverters. This paper discusses a multiphase version of the SSI topology for the first time. Among multiphase systems undergoing a revolution in the research area, five-phase motor drives are a relatively practical selection in industrial applications. Therefore, this paper focuses on a five-phase SSI as an example. The topology, operating principles, modulation techniques, and performance analysis of the analyzed topology are introduced. A modified space-vector modulation (MSVM) scheme is developed to eliminate low-frequency ripples in the input current. There is also a detailed analysis and graphical evaluation of the output currents ripples using the space-vector approach. It is evident that multiphase SSI is suitable for motor drives, especially when a high-output voltage gain is required. In addition to having a nearly identical ripple in output current to a conventional VSI, it has the benefit of performing the boosting action in a single stage with fewer passive components and a low ripple in input current. Finally, the simulation and experimental results have been conducted to demonstrate the viability of the multiphase SSI studied in the theoretical study and analysis.

The structure of this work in the field of queuing theory consists of two stages. The first stage presents Little’s Law in Multiphase Systems (MSs). To obtain this result, the Strong Law of Large Numbers (SLLN)-type theorems for the most important MS probability characteristics (i.e., queue length of jobs and virtual waiting time of a job) are proven. The next stage of the work is to verify the result obtained in the first stage.

This article presents three variants of the Pulse Width Modulation (PWM) for the Double Square Multiphase type Conventional Matrix Converters (DSM-CMC) supplying loads with the open-end winding. The first variant of PWM offers the ability to obtain zero value of the common-mode voltage at the load’s terminals and applies only six switches within the modulation period. The second proposal archives for less Total Harmonic Distortion (THD) of the generated load voltage. The third variant of modulation concerns maximizing the voltage transfer ratio, minimizing the number of switching, and the common-mode voltage cancellation. The discussed modulations are based on the concept of sinusoidal voltage quadrature signals, which can be an effective alternative to the classic space-vector approach. In the proposed approach, the geometrical arrangement of basic vectors needed to synthesize output voltages is built from the less number of vectors, which is equal to the number of the matrix converter’s terminals. The PWM duty cycle computation is performed using only a second-order determinant of the voltages coordinate matrix without using trigonometric functions. A new approach to the PWM duty cycles computing and the load voltage synthesis by 5 × 5 and 12 × 12 topologies has been verified using the PSIM simulation software.

Applying multiphase systems in microreactors leads to an intensification of heat and mass transport. Critical aspects of the well-studied segmented slug-flow, such as bubble generation and pump control, can be automated, provided a robust sensor for the reliable determination of velocity, phase lengths, and phase ratio(s) is available. In this work, a fast and low-priced sensor is presented, based on two optical transmission sensors detecting flow characteristics noninvasively together with a microcontroller. The resulting signal is mainly due to refraction of the bubble-specific geometries as shown by a simulation of light paths. The high performance of the processing procedure, utilizing the derivative of the signal, is demonstrated for a bi- and triphasic slug flow. The error of

The thermodynamically constrained averaging theory (TCAT) is a comprehensive theory used to formulate hierarchies of multiphase, multiscale models that are closed based upon the second law of thermodynamics. The rate of entropy production is posed in terms of the product of fluxes and forces of dissipative processes. The attractive features of TCAT include consistency across disparate length scales; thermodynamic consistency across scales; the inclusion of interfaces and common curves as well as phases; the development of kinematic equations to provide closure relations for geometric extent measures; and a structured approach to model building. The elements of the TCAT approach are shown; the ways in which each of these attractive features emerge from the TCAT approach are illustrated; and a review of the hierarchies of models that have been formulated is provided. Because the TCAT approach is mathematically involved, we illustrate how this approach can be applied by leveraging existing components of the theory that can be applied to a wide range of applications. This can result in a substantial reduction in formulation effort compared to a complete derivation while yielding identical results. Lastly, we note the previous neglect of the deviation kinetic energy, which is not important in slow porous media flows, formulate the required equations to extend the theory, and comment on applications for which the new components would be especially useful. This work should serve to make TCAT more accessible for applications, thereby enabling higher fidelity models for applications such as turbulent multiphase flows.