Dynamic Reoptimisation of Fed-batch Bioreactors Using Genetic Algorithms

摘要

On-line reoptimisation of a bioprocess is a difficult task due to its complex,time-varying,nonlinear nature.The task is further complicated by the lack of suitable on-line sensors.Frequently,model errors due to the complex nature of bioprocesses require that kinetic model used in optimisation be reparameterised or Updated on-line for effective control of fermentation processes.An on-line reoptimisation procedure based on an improved genetic algorithm approach in combination with state and parameter estimation is developed for the determination of substrate feed profiles for the optimal operation of fed-batch bioreactors.The problem specific knowledge generated through the rigorous application of the optimal control theory is used to formulate the set of decision variables representing the qualitative and quantitative aspects of the feed rate profile.An Extended Kalman Filter (EKF) is used to provide optimal estimates of the relevant state variables from readily available on-line process measurements and the model parameters are dynamically adjusted using a nonlinear programming approach over a moving window.The EKF also keeps track of the specific growth parameter to detect any change in the process.The efficiency of the proposed algorithm is demonstrated on a cell mass production problem under fed-batch operation.