Neural networks for bioreactor control solutions - Miroshnikov Sergey; Ryazanov Vitaliy; Proskurin Dmitry; Sheida Elena; Miroshnikov Ivan; Ovechkin Maxim; Duskaev Galimzhan (E3S Web of Conferences)
The 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.