Estimation of optimal feeding strategies for fed-batch bioprocesses.
A generic methodology for feeding strategy optimization is presented. This approach uses a genetic algorithm to search for optimal feeding profiles represented by means of artificial neural networks (ANN). Exemplified on a fed-batch hybridoma cell cultivation, the approach has proven to be able to cope with complex optimization tasks handling intricate constraints and objective functions. Furthermore, the performance of the method is compared with other previously reported standard techniques like: (1) optimal control theory, (2) first order conjugate gradient, (3) dynamical programming, (4) extended evolutionary strategies. The methodology presents no restrictions concerning the number or complexity of the state variables and therefore constitutes a remarkable alternative for process development and optimization.
Franco-Lara
Ezequiel
E
Biochemical Engineering, Munich University of Technology, Boltzmannstr. 15, 85748, Garching, Germany.
Weuster-Botz
Dirk
D
D000465:Algorithms; D019149:Bioreactors / Q000382:microbiology; D003198:Computer Simulation; D008962:Models, Theoretical