Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.

The development of energy-efficient solutions for large-scale fermenters demands a deep and comprehensive understanding of hydrodynamic and heat and mass transfer processes. Despite a wide variety of research dedicated to measurements of mass transfer intensity in bubble flows, this research subject faces new challenges due to the topical development of new innovative bioreactor designs. In order to understand the fluid dynamics of the gas–liquid medium, researchers need to develop verified CFD models describing flows in the bioreactor loop using a progressive physical and mathematical apparatus. In the current paper, we represent the results of evaluating the key performance indicator of the bioreactor, namely the volumetric mass transfer coefficient (${\mathrm{k}}_{\mathrm{L}}\mathrm{a}$) known as a parameter of dominant importance for the design, operation, scale-up, and optimization of bioreactors, using the developed thermometry method. The thermometry method under consideration was examined within a series of experiments, and a comparative analysis was provided for a number of various regimes also being matched with the classical approaches. The methodology, experiment results, and data verification are given, which allow the evaluation of the effectiveness and prediction of the fluid flows dynamics in bioreactors circuits and ultimately the operational capabilities of the fermenter line.

This mainly theoretical work is devoted to the study of the contribution of the cell-free layer (CFL) near the vessel wall to hemodynamics in a large coronary artery with stenosis to assess the relevance of CFL modeling to the needs of interventional cardiology. An Euler–Euler model considering blood as a two-component fluid with a discrete phase of erythrocytes and a liquid plasma phase was applied to a simple 2d vessel with 65% stenosis. It was found that both the CFL thickness and the local contribution of the CFL thickness to hemodynamics are inhomogeneous along the vessel. The effects of CFL on the velocity profiles, vortex formation, hematocrit, viscosity, and wall shear stresses in the area of stenosis were determined. To demonstrate the significance of CFL modeling for prognostic purposes, the same hemodynamic conditions, analyzed using a one-component model, were also considered. A comparison analysis showed that the existence of CFL resulted in a significant overestimation (up to over 100%) of the main hemodynamic characteristics of the flow obtained using the model based on the Carreau equation.

A computer vision algorithm to determine the parameters of a two-phase turbulent jet of a water-gas mixture traveling at a velocity in the range of 5–10 m/s was developed in order to evaluate the hydrodynamic efficiency of mass exchange apparatuses in real time, as well as to predict the gas exchange rate. The algorithm is based on threshold segmentation, the active contours method, the regression of principal components method, and the comparison of feature overlays, which allows the stable determination of jet boundaries and is a more efficient method when working with low-quality data than traditional implementations of the Canny method. Based on high-speed video recordings of jets, the proposed algorithm allows the calculation of key characteristics of jets: the velocity, angle of incidence, structural density, etc. Both the algorithm’s description and a test application based on video recordings of a real jet created on an experimental prototype of a jet bioreactor are discussed. The results are compared with computational fluid dynamics modeling and theoretical predictions, and good agreement is demonstrated. The presented algorithm itself represents the basis for a real-time control system for aerator operation in jet bioreactors, as well as being used in laboratory jet stream installations for the accumulation of big data on the structure and dynamic properties of jets.

Multiphase biophysical media are known to be complex structures with continuous high demand to the scientific community for understanding the relationships and ratios between factors affecting the type, dynamics and nature of its structural changes on their impact degree on the media itself. Among the plentiful list of such factors the following do worth mentioning: the lifetime of a particle, turbulence factors and a number of others, each requiring careful analysis, assessment of the contribution degree and, importantly, correct accounting. The present study is focused on such a factor affecting mass transfer intensity change as surface tension through its relationship with the interfacial area: the latter is the site of mass exchange between the gas and liquid phases, and modifications in surface tension values can significantly impact the characteristics of this area, hence altering the rate of mass transfer. By controlling surface tension, one can effectively modulate the size and stability of particles, namely bubbles or droplets, which in turn changes the interfacial area available for mass transfer. The total interfacial area, which is the cumulative surface area of all bubbles, serves as the site for mass transfer. The impact of the surface tension coefficient variation into gas–liquid mass transfer characteristics is analyzed both for the case of water and model liquid. The latter means the potential contribution of surface-active substances was a part of research scope since it was applied to recreate conditions similar to the cultural liquid when microorganisms that produce surfactants are grown. The proposed new methodology assumes calculating interfacial area through the segmentation of images captured by a high-speed camera, thus we can gain a profoundly enhanced understanding of the relationship between surface tension and mass transfer. The precise visual data and subsequent computation of the interfacial area provide deeper insights into the dynamics of bubble formation and the effects of surface tension on bubble size and distribution. As a result, this method has significantly improved our capacity to investigate and optimize mass transfer processes in multiphase biophysical systems. Both analytical approach and results interpretation not only influence affirmatively on deep understanding of natural mechanisms in biophysical media, but also might serve their best for potential application, e.g. in the context of the development of biotechnological industries based on fermentation processes for protein production.

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.