Model predictive control on fed-batch penicillin fermentation process - Chew, Li Mei (2009)
In this research study the development of optimization strategies for a fed-batch penicillin fermentation process using model predictive controller was simulated using MATLAB 7.1 software. To facilitate the study, model predictive control (MPC) based on unstructured model for penicillin production in a fed-batch fermentor has been developed. A mathematical model of the system is derived based on published materials, the data is generated using PENSIM, dynamic response is analyzed, transfer function is developed and finally the MPC is implemented into the fermentation process. MPC offers an adaptive and optimizing control strategy which deals with multiple goals and constraints. The results of a study of the applicability of Model Predictive Control (MPC) in the process were obtainable. In order to obtain best optimization result for the fed-batch penicillin fermentation process, two optimization algorithms were selected. First, dynamic optimization using direct shooting method and second is implementation single step ahead Dynamic Matrix Control (DMC). Comparison of these two different approaches shows that DMC algorithm showed the best result with an optimization procedure.