Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming

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In this brief, we extend the simulation-based approximate dynamic programming (ADP) method to optimal feedback control of fed-batch reactors. We consider a free-end problem, wherein the batch time is considered in finding the optimal feeding strategy in addition to the final time productivity. In ADP, the optimal solution is parameterized in the form of profit-to-go function. The original definition of profit-to-go is modified to include the decision of batch termination. Simulations from heuristic feeding policies generate the initial profit-to-go versus state data. An artificial neural network then approximates profit-to-go as a function of process state. Iterations of the Bellman equation are used to improve the profit-to-go function approximator. The profit-to-go function approximator thus obtained, is then implemented in an online controller. This method is applied to cloned invertase expression in Saccharomyces cerevisiae in a fed-batch bioreactor.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2005-09
Language
English
Article Type
Article
Keywords

NONLINEAR PROCESSES; OPTIMIZATION; REACTOR

Citation

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, v.13, no.5, pp.786 - 790

ISSN
1063-6536
DOI
10.1109/TCST.2005.852105
URI
http://hdl.handle.net/10203/92817
Appears in Collection
CBE-Journal Papers(저널논문)
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