Browse by author
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.
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
URL: http://dx.doi.org/10.1002/elsc.201600037
DOI: 10.1002/elsc.201600037
Altmetrics provided by Altmetric
