Abstract
Traditionally, fed-batch biochemical process optimization and control uses complicated off-line optimizers, with no online model adaptation or re-optimization. This study demonstrates the applicability of a class of adaptive critic designs for online re-optimization and control of an aerobic fed-batch fermentor. Specifically, the performance of an entire class of adaptive critic designs, viz., heuristic dynamic programming, dual heuristic programming and generalized dual heuristic programming, was demonstrated to be superior to that of a heuristic random optimizer, on optimization of a fed-batch fermentor operation producing monoclonal antibodies
Recommended Citation
D. C. Wunsch and M. S. Iyer, "Dynamic Re-Optimization of a Fed-Batch Fermentor using Adaptive Critic Designs," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2001.
The definitive version is available at https://doi.org/10.1109/72.963778
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Adaptive Critic Designs; Batch Processing (Industrial); Biochemical Process; Dual Heuristic Programming; Dynamic Programming; Fed-Batch Fermentor; Feedforward Neural Nets; Fermentation; Heuristic Dynamic Programming; Monoclonal Antibodies; Process Control; Random Optimizer
International Standard Serial Number (ISSN)
1045-9227
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2001 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2001
