Evolutionary Strategy for Feeding Trajectory Optimization of Fed-batch Reactors

Safe and optimal operation of complex production processes is one of the most important research and development problems in process engineering. This problem is the most relevant at the design of the optimal feeding profile of fed-batch chemical reactors due to the nonlinear and unstable dynamical behavior of the processes. This paper shows that how the optimal feeding policy can be determined in fed-batch reactors by sequential quadratic programming, classical evolutionary strategy (ES) and the advanced version of ES that is based on covariance matrix adaptation. A multi-objective function was created and the search space was constrained in case of all of the three applied algorithms. The switching times between states in the feeding trajectory and the feed rates in each state were manipulated to find the global minima of the objective function. To obtain the optimal feeding policy the first-principle model of a pilot fed-batch reactor was implemented in MATLAB and applied as a dynamic simulator of the process. Off-line optimization process was carried out in case of different dosing time distribution. As the results show a significant improvement can be achieved in process performance applying advanced ES based optimization algorithms to generate feeding trajectories.