von Stosch Moritz, Willis Mark J
CEAM Faculty of Science, Agriculture and Engineering Newcastle University Newcastle upon Tyne UK.
Eng Life Sci. 2016 Oct 14;17(11):1173-1184. doi: 10.1002/elsc.201600037. eCollection 2017 Nov.
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 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.
实验统计设计（DoE）是上游生物工艺开发（以及一般在各个行业）中广泛采用的一种方法，用于获取实验数据，从中可以推断自变量（因素）对过程响应的影响。在这项工作中，提出了一种方法，该方法可以减少传统DoE建议的实验总数。该方法允许将几个DoE组合的评估压缩为数量减少的实验，这被称为强化实验设计（iDoE）。在本文中，iDoE用于为模拟发酵产生的数据开发一个动态混合模型（由微分方程和前馈人工神经网络组成）。对于所呈现的案例研究，结果表明，与传统DoE相比，实验总数可以减少约40%。另一个好处是可以同时开发一个合适的动态模型，该模型可用于过程优化和控制研究。
