Optimization of a Fed-batch Bioreactor Using Simulation-based Approach

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 Neuro Dynamic Programming (NDP) literature, wherein the optimal solution is parameterized in the form of a cost-to-go or profit-togo 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.