This work discusses the optimization of the biopolymer PHAs production by Ralstonia eutropha, in a bioreactor carried out under fed-batch mode. Although the optimization of fed-batch fermentations involves the manipulation of the substrate feed rate, which generates a singular optimal control problem, the optimal trajectory can be also set by adjusting small segments by non-linear programming. The cybernetic structured mathematical model used here in is described by a system of 12 differential equations; the strategy involves the maximization/minimization of an Objective Function considering the model as a set of implicit constraints and the discretization of the manipulated variables (substrate feed rates). The sequential quadratic program method is used to solve the optimization problem. PHAs productivity is taken as the objective function and its results are compared to those documented in the literature.

The integration of artificial intelligence techniques introduces fresh perspectives in the implementation of these methods. This paper presents the combination of neural networks and evolutionary strategies to create what is known as evolutionary artificial neural networks (EANNs). In the process, the excitation function of neurons was modified to allow asexual reproduction. As a result, neurons evolved and developed significantly. The technique of a batch polymerization reactor temperature controller to produce polymethylmethacrylate (PMMA) by free radicals was compared with two different controls, such as PID and GMC, demonstrating that artificial intelligence-based controllers can be applied. These controllers provide better results than conventional controllers without creating transfer functions to the control process represented.