We use simulation-based approach to find the optimal feeding strategy for cloned invertase expression in Saccharomyces cerevisiae in a fed-batch bioreactor. The optimal strategy maximizes the productivity and minimize...
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We use simulation-based approach to find the optimal feeding strategy for cloned invertase expression in Saccharomyces cerevisiae in a fed-batch bioreactor. The optimal strategy maximizes the productivity and minimizes the fermentation time. This procedure is motivated from neurodynamicprogramming (NDP) literature. wherein the optimal solution is parameterized in the form of a cost-to-go or profit-to-go functions. The proposed approach uses simulations from a heuristic feeding policy as a starting point to generate the profit-to-go vs state data. An artificial neural network is used to obtain profit-to-go as a function of system state. Iterations of Bellman equation are used to improve the profit function . The profit-to-go function thus obtained, is then implemented in an online controller, which essentially converts infinite horizon problem into an equivalent one-step-ahead problem.
This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm ...
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This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.
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