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

Self-cycling fermentation (SCF) was coupled with a genetic algorithm (GA) t o provide a simple system for evaluating biological models. The SCF provide d the necessary system excitation and data "richness" required to completel y define the fitted biological models. The solution scheme based on the GA avoided the computational difficulties often associated with calculus-based nonlinear regression techniques, resulting in rapid and accurate convergen ce. After validating the mathematical approach, data from the SCF obtained under denitrifying conditions were fitted successfully to an established mo del using the GA. Finally, data obtained in the SCF for the removal of phen ol were used to compare multiple models. This work suggests that the SCF, i n conjunction with the GA, provides a coherent system that can facilitate t he characterization of biological systems. (C) 2000 John Wiley & Sons, Inc.