Modeling of biological processes using self-cycling fermentation and genetic algorithms

Abstract

Self-cycling fermentation (SCF) was coupled with a genetic algorithm (GA) to provide a simple system for evaluating biological models. The SCF provided the necessary system excitation and data "richness" required to completely define the fitted biological models. The solution scheme based on the GA avoided the computational difficulties often associated with calculusbased nonlinear regression techniques, resulting in rapid and accurate convergence. After validating the mathematical approach, data from the SCF obtained under denitrifying conditions were fitted successfully to an established model using the GA. Finally, data obtained in the SCF for the removal of phenol were used to compare multiple models. This work suggests that the SCF, in conjunction with the GA, provides a coherent system that can facilitate the characterization of biological systems.

Continuous hybridoma bioreactor: sensitivity analysis and optimal control

“…Introducing eqn (21) into eqn (20), we obtain the entropic density of any trajectory f {x[u(t)],u(t),t}:…”

“…A piecewise approximation of the optimal control trajectory was sought, either constant or linear over a time interval, and the GA technique was applied to find it. GA is a method extensively used especially in the last two decades, taking advantage of the progress in computational capacities [13,21–23].…”