Abstract

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

Meeting Name

International Joint Conference on Neural Networks, 1999. IJCNN '99

Department(s)

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

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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