ON DATA-BASED MODELING TECHNIQUES FOR FERMENTATION PROCESSES

Six different modelling techniques were considered for the recombinant Escherichia coli fermentation process. These are Multiple Linear Regr ession (MLR), Principal Component Regression (PCR), Partial Least Squa res (PLS), Auto-Regressive Moving Average with eXogeneous inputs (ARMA X), Non-linear ARMAX (NARMAX) and Artificial Neural Networks. The mode ls use industrial on-line data from the process as input variables in order to forecast the concentrations of biomass and recombinant protei n normally only available from off-line laboratory analysis. The model s' performances are compared by prediction error and graphical fit usi ng results obtained from a common testing set of fermentation data.