Kinetic modelling of acetic fermentation in an industrial process by genetic algorithms with a desirability function
The basic tool to simulate the evolution of a bioprocess, such as the industrial process of acetic fermentation, is the kinetic model. The model must be simple and have a high predictive ability to give results capable of explaining the real behaviour. The difficulties in the kinetic modelling of biological processes are mainly related to the description of the bacterial growth. In this paper a genetic algorithm is designed to obtain a set of kinetic parameters for the specific growth rate that enables the model to explain the industrial fermentation. Only acetic concentration data from industrial fermentators are required. A four-factor desirability function works properly as the response to maximize. The model optimized can explain not only the behaviour of the industrial batch process studied, but also the experimental results obtained with a pilot fermentator working continuously with and without cell recycling. Copyright (C) 2003 John Wiley Sons, Ltd.
kinetic modelling acetic fermentation genetic algorithms desirability function simulation process