Intensified design of experiments for upstream bioreactors.

Abstract

Statistical Design of Experiments (DoE) is a widely adopted methodology in upstream bioprocess development (and generally across industries) to obtain experimental data from which the impact of independent variables (factors) on the process response can be inferred. In this work, a method is proposed that reduces the total number of experiments suggested by a traditional DoE. The method allows the evaluation of several DoE combinations to be compressed into a reduced number of experiments, which is referred to as intensified Design of Experiments (iDoE). In this paper, the iDoE is used to develop a dynamic hybrid model (consisting of differential equations and a feedforward artificial neural network) for data generated from a simulated Escherichia coli fermentation. For the case study presented, the results suggest that the total number of experiments could be reduced by about 40% when compared to traditional DoE. An additional benefit is the simultaneous development of an appropriate dynamic model which can be used in both, process optimization and control studies.