Comparison of biomass estimation techniques for a Bacillus thuringiensis fed-batch culture

In this work, the ability of artificial neural nets was investigated for the on-line biomass prediction of the simulated growth of a strain of Bacillus thuringiensis in fed-batch mode. For this purpose, multilayered backpropagation nets with sigmoid nodes were trained. The patterns were composed of input data on current values of biomass concentration, limiting substrate concentration and dilution rate, and output data on prediction of biomass concentration for the following step. The dilution rate was disturbed by a PRBS input, and simulations were conducted using a phenomenological experimentally validated model. The nets were able to predict the biomass concentration for different feeding techniques, and they were also compared with the variable estimation technique using the extended Kalman filter.