Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms
|
|
| Автор |
Nizovtseva, I.
Palmin, V. Simkin, I. Starodumov, I. Mikushin, P. Nozik, A. Hamitov, T. Ivanov, S. Vikharev, S. Zinovev, A. Svitich, V. Mogilev, M. Nikishina, M. Kraev, S. Yurchenko, S. Mityashin, T. Chernushkin, D. Kalyuzhnaya, A. Blyakhman, F. |
|
| Тематика |
ALGORITHMS
APPROXIMATION BIOREACTOR BUBBLE DETECTION CANNY EDGE DETECTOR CLUSTERING ALGORITHMS COMPUTER VISION DATA MARKUP EDGE DETECTION FRANGI FILTER ACCURACY GAS—LIQUID FLOWS IMAGE PROCESSING IMAGE QUALITY IMPROVEMENT JET STREAM KLA MASS TRANSFER COEFFICIENT NEURAL NETWORKS PHASE CONTACT AREA STARDIST MODEL VESSEL SEGMENTATION APPROXIMATION ALGORITHMS BIOCONVERSION BIOREACTORS COMPUTER VISION DEEP NEURAL NETWORKS EDGE DETECTION ENERGY EFFICIENCY IMAGE ENHANCEMENT IMAGE SEGMENTATION MASS TRANSFER APPROXIMATION BUBBLE DETECTION CANNY EDGE DETECTORS CONTACT AREAS DATA MARKUP FRANGI FILTER ACCURACY GAS LIQUID FLOWS IMAGE QUALITY IMPROVEMENTS IMAGES PROCESSING JET STREAMS KLUM MASS-TRANSFER COEFFICIENT NEURAL-NETWORKS PHASE CONTACT PHASE CONTACT AREA STARDIST MODEL VESSEL SEGMENTATION CLUSTERING ALGORITHMS |
|
| Описание |
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. © 2023 by the authors.
Ministry of Education and Science of the Russian Federation, Minobrnauka The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged. |
|
| Дата |
2024-04-05T16:18:10Z
2024-04-05T16:18:10Z 2023 |
|
| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
|
| Идентификатор |
Nizovtseva, I, Palmin, V, Simkin, I, Starodumov, I, Mikushin, P, Nozik, A, Hamitov, T, Ivanov, S, Vikharev, S, Zinovev, A, Svitich, V, Mogilev, M, Nikishina, M, Kraev, S, Yurchenko, S, Mityashin, T, Chernushkin, D, Kalyuzhnaya, A & Blyakhman, F 2023, 'Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms', Algorithms, Том. 16, № 3, 125. https://doi.org/10.3390/a16030125
Nizovtseva, I., Palmin, V., Simkin, I., Starodumov, I., Mikushin, P., Nozik, A., Hamitov, T., Ivanov, S., Vikharev, S., Zinovev, A., Svitich, V., Mogilev, M., Nikishina, M., Kraev, S., Yurchenko, S., Mityashin, T., Chernushkin, D., Kalyuzhnaya, A., & Blyakhman, F. (2023). Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms. Algorithms, 16(3), [125]. https://doi.org/10.3390/a16030125 1999-4893 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151097376&doi=10.3390%2fa16030125&partnerID=40&md5=584a5ef1e324da31328b490bec768dad https://www.mdpi.com/1999-4893/16/3/125/pdf?version=1679639197 http://elar.urfu.ru/handle/10995/130302 10.3390/a16030125 85151097376 000952975000001 |
|
| Язык |
en
|
|
| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
|
| Формат |
application/pdf
|
|
| Издатель |
MDPI
|
|
| Источник |
Algorithms
Algorithms |
|