Hybrid optimization-simulation based approach for the optimal development of biotechnological processes

This work introduces a systematic method for the optimization of biotechnological processes that relies on the combined use of simulation packages and optimization tools. The proposed algorithm iterates between two types of sub-problems: a nonlinear programming (NLP) sub-problem that optimizes the operating conditions and equipment sizes of a given flowsheet and a specially customized master problem that decides on the number of equipments in parallel. The NLP sub-problems are solved by integrating the process simulator SuperPro Designer ® with an external NLP solver implemented in Matlab ® , whereas the master sub-problems include a heuristic-based debottlenecking strategy. The capabilities of the proposed methodology are illustrated through its application to the production of L-lysine.