Model predictive control of a nonlinear fed-batch fermentation process

Bioprocesses, which are involved in producing different antibiotics and other pharmaceutical products, may be conveniently classified according to the mode chosen for the process: either batch, fed-batch or continuous. From the control engineer's viewpoint it is the fed-batch processes, however, which present the greatest challenge to get a pure product with a high concentration. To achieve this goal, control of the following parameters has significant importance dealing with these processes: temperature, pH, dissolved oxygen (DO2). Bioprocesses have complicated dynamics. Hence, their control is a delicate task; Nonlinearity and non-stationarity, which make modeling and parameter estimation particularly difficult perturbs such processes. Moreover, the scarcity of on-line measurements of the component concentrations (essential substrates, biomass and products of interest) makes this task more sophisticated. In this paper, Model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor has been developed. MPC is performed via determining the control signal by minimizing a cost function in each step. The results of this controller to maximize penicillin concentration have been displayed and also compared with the results of auto-tuned PID controller used in previous works.