Abstract
Four different modelling techniques are considered for the Escherichia coli fermentation process. These are Multiple Linear Regression (MLR), Principal Component Regression (PCR), Non-linear Auto-Regressive Moving Average with eXogeneous inputs (NARMAX) and Artificial Neural Networks (ANNs). The models use industrial on-line data from the process as inputs 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 accuracy and graphical fit using results obtained from a common set of fermentation data.
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