Adaptive optimization of a continuous culture with the use of a bilevel forgetting factor.
An adaptive optimization algorithm using a dynamic identification scheme with a bilevel forgetting factor (BFF) has been developed. The simulation results show superiority of this method to other methods when applied to maximize the cellular productivity of a continuous culture of baker's yeast, Saccharomyces cerievisiae. Within the limited ranges of tuning parameters tested the BFF algorithm is found to be superior in terms of initial optimization speed and accuracy and reoptimization speed and accuracy when there is an external change and long term stability (removal of "blowing up" phenomena). Algorithms tested include those based on a constant forgetting factor, an adaptive variable forgetting factor (VFF) and moving window (MW) identification.
Chang
Y K
YK
School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907.
Pyun
Y R
YR
Lim
H C
HC