On data-based modelling techniques for fermentation processes

Abstract

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