Sequential adaptive networks: An ensemble of neural networks for feed forward control of L-methionine production

Intracellular methionine synthesis is strictly regulated and apparently, results in highly nonlinear concentration-time profiles observed in fed-batch production of this amino acid. For controlling methionine concentration along a predefined trajectory, a control strategy was developed using a sequential adaptive network (SAN) in conjunction with a mechanistic feedforward control law. SAN is an assembly of chronologically ordered networks, with one sub network assigned to each sampling interval, so that feature memory is distributed. Data for training SAN was obtained using a model whose parameters were calculated from experimental data. A range of different operating regimes was simulated using the model to create process scenarios for evaluating the performance of the SAN-feedforward controller (SAN-FFC). The adaptation of the weights of SAN is driven by the error between predicted and measured values of dissolved oxygen concentration at each sampling interval. Under simulated conditions, the feedforward control law uses the values of state variables predicted by SAN and measured values to determine a control action that is in tune with process evolution. The SAN-feedforward controller is robust and exhibits stable tracking of the methionine concentration trajectory in the presence of measurement noise and parametric uncertainty. It is perceived that the online implementation of SAN-FFC for a general bioprocess is practicable.