Parameter estimation of bioprocesses via parallel particle swarm optimization

Modeling of biotechnological systems is an important research area. The most challenging approach is to build non-linear state space models for these systems. In this work the parameters of a bacterial growth bioprocess are estimated using prediction error method. Prediction error methods are widely used in parameter estimation both for linear and nonlinear models and consist in minimization of the distance between measured and modeled data in a suitable norm. Because these problems are solved using numerical algorithms that are time consuming, in this paper a parallel particle swarm optimization technique is used in order to numerically solve the minimization problem. The algorithm is implemented on a multicore processor and the performances of this approach are presented by numerical simulations.