Traditionally fed-batch biochemical process optimization and control uses complicated theoretical off-line optimizers, with no online model adaptation or re-optimization. This study demonstrates the applicability, effectiveness, and economic potential of a simple phenomenological model for modeling, and an adaptive critic design, generalized dual heuristic programming, for online re-optimization and control of an aerobic fed-batch fermentor. The results are compared with those obtained using a heuristic random optimizer
D. C. Wunsch and M. S. Iyer, "Fed-Batch Dynamic Optimization using Generalized Dual Heuristic Programming," Proceedings of the International Joint Conference on Neural Networks, 1999. IJCNN '99, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/IJCNN.1999.836190
International Joint Conference on Neural Networks, 1999. IJCNN '99
Electrical and Computer Engineering
Keywords and Phrases
Action Learning; Adaptive Critic Design; Batch Processing (Industrial); Dual Heuristic Programming; Dynamic Optimization; Fed-Batch Biochemical Process; Feedforward Neural Nets; Feedforward Neural Networks; Fermentation; Learning (Artificial Intelligence); Neurocontrollers; Optimization; Phenomenological Model; Process Control; Real-Time Systems
Article - Conference proceedings
© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.