Multi-objective particle swarm optimization (MOPSO) of lipid accumulation in Fed-batch cultures

Dynamic optimization of fermentation processes could demand the use of multiple criteria to attain certain objectives, which in most cases are conflicting to each other. The use of Pareto optimal sets supplies the necessary information to take decisions about the trade-offs between objectives. In this work, a multi-objective optimization algorithm based on particle swarm optimization (MOPSO) is used to optimize lipid contents in fermentations with Yarrowia lipolytica. A reduced model was developed to shorten the computation time of MOPSO. A pattern search algorithm was sequentially coupled to MOPSO to execute a dynamic optimization handling physical constraints. Three cases are analyzed to emphasize the response of our control strategy. Simulation results showed that MOPSO - pattern search algorithm achieved high lipid fraction and productivity.