Empirical modeling of antibiotic fermentation process using neural networks and genetic algorithms

Abstract

Empirical modeling of the industrial antibiotic fed-batch fermentation process is discussed in this paper. Several methods including neural networks, genetic algorithms and feature selection are combined with prior knowledge in the research methodology. A linear model, a radial basis function neural network and a hybrid linear–neural network model are applied for the model formation. Two approaches to modeling of antibiotic fermentation process are presented: a dynamic modeling of the process and a modeling in the fermentation sample space. The first approach is focused on the current state of the fermentation process and forecasts the future product concentration. The second approach treats the fermentation batch as one sample which is characterized by the set of extracted features. Based on these features, the fermentation efficiency is predicted. Modeling in the fermentation sample space integrates the prior knowledge of experts with empirical information and can represent a basis for the control of the fermentation process.