Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

• Published in: Algorithms Vol. 16; no. 3; p. 125
• Main Authors: Nizovtseva, Irina, Palmin, Vladimir, Simkin, Ivan, Starodumov, Ilya, Mikushin, Pavel, Nozik, Alexander, Hamitov, Timur, Ivanov, Sergey, Vikharev, Sergey, Zinovev, Alexei, Svitich, Vladislav, Mogilev, Matvey, Nikishina, Margarita, Kraev, Simon, Yurchenko, Stanislav, Mityashin, Timofey, Chernushkin, Dmitrii, Kalyuzhnaya, Anna, Blyakhman, Felix
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Algorithms; Artificial neural networks; bioreactor; Bioreactors; Biosynthesis; bubble detection; Bubbles; Clustering; Coefficients; Comparative analysis; Computer vision; Design; Efficiency; Fermentation; Flow control; Fluid dynamics; Gas flow; Gases; gas—liquid flows; Image segmentation; jet stream; Liquid flow; Machine vision; Mass transfer; Methods; Neural networks; phase contact area; Physiological aspects; Process parameters; Productivity; Russia
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16030125

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.