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Intensified design of experiments for upstream bioreactors

Lookup NU author(s): Dr Moritz von Stosch, Dr Mark Willis



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley - VCH Verlag GmbH & Co. KGaA, 2017.

For re-use rights please refer to the publisher's terms and conditions.


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 E. 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.

Publication metadata

Author(s): von Stosch M, Willis M

Publication type: Article

Publication status: Published

Journal: Engineering in Life Sciences

Year: 2017

Volume: 17

Issue: 11

Pages: 1173-1184

Print publication date: 01/11/2017

Online publication date: 14/10/2016

Acceptance date: 01/09/2016

Date deposited: 10/09/2016

ISSN (print): 1618-0240

ISSN (electronic): 1618-2863

Publisher: Wiley - VCH Verlag GmbH & Co. KGaA


DOI: 10.1002/elsc.201600037


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