Year: 2024
Type: article
Abstract: \nThe use of machine learning has the potential to improve the control of bioreactors. The aim of this research was to use self-organising Kohonen maps based on algorithms built from the composition of the taxonomic structure of the bioreactor. By adjusting the weights of the map neurons, we can infer internal unobservable dependencies in the input data structures based on the results. Using our chosen model, we will gain a deeper understanding of the taxonomic composition of the bacterial community, which will allow us to better manage fermentation processes in bioreactors.\n
Authors: Sergey Miroshnikov, Vitaliy Ryazanov, Dmitry Proskurin, Elena Sheida, Ivan Miroshnikov, Maxim Ovechkin, Galimzhan Duskaev
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Russian Science Foundation
Award: G5332104047
OpenAlex ID: W6922072075
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