Neural network predictive control by MIMS monitored 2,3-butanediol fermentation by Klebsiella oxytoca

With the aid of a membrane introduction mass spectrometer (MIMS), the major product 2,3-butanediol (2,3-BDL) as well as the other metabolites from the fermentation carried by Klebsiella oxytoca can be measured on-line simultaneously. A backpropagation neural network (BPN) being recognized with superior mapping ability was applied to this control study. This neural network adaptive control differs from those conventional controls for fermentation systems in which the measurements of cell mass and glucose are not included in the network model. It is only the measured product concentrations from the MIMS that are involved. Oxygen composition was chosen to be the control variable for this fermentation system. Oxygen composition was directly correlated to the measured product concentrations in the controller model. A two-dimensional (number of input nodes by number of data sets) moving window for on-line, dynamic learning of this fermentation system was applied. The input nodes of the network were also properly selected. Number of the training data sets for obtaining better control results was also determined empirically. Two control structures for this 2,3-BDL fermentation are discussed and compared in this work. The effect from adding time delay element to the network controller was also investigated.