EXPERIMENTAL STUDY OF NEURAL NETWORK SOFTWARE SENSORS IN YEAST AND BACTERIA FED-BATCH PROCESSES

Nowadays, on-line bioprocess monitoring is still a delicate task due to the lack of on-line measurements of the key components of a culture. In this study the use of artificial neural networks (NNs) as a basis to develop software sensors is investigated. Particularly attention is focused on the use of standard signals, such as those coming from pH or oxygen regulation, to infer information on the evolution of biomass or products of yeast and bacteria fed-batch cultures. The selection of informative signals is achieved through principal component analysis (PCA). Radial basis function (RBF) NNs are then used to estimate the component concentrations of interest. This work is based on extensive experimental studies, considering different cell strains and bioreactor scales. The results of our tests demonstrate the flexibility of NN software sensors in industrial environments.