Hybride Modellbildung zur Prozessführung von Fed-Batch-Kultivierungen mit Aspergillus niger

Due to its high secretion capability, the filamentous growing fungus Aspergillus niger is a promising candidate for the industrial production of homologous and heterologous proteins. In this work, the growth and product formation of A.niger in fed-batch cultivations is modelled by using the enzyme glucoamylase as an example product. Since classical modeling of bioprocesses involves a great deal of effort, a hybrid modeling approach is being pursued that combines biologically motivated modeling with data-based methods and requires fewer biological details to be captured. In the first part, a data-based methodology for the rapid development of initial model structures is proposed, in which a pseudo-stoichiometry is estimated via singular value decomposition. Then, a linear transformation is used to convert the measured data into reaction-variant and -invariant states with which the kinetics of the model can be identified. The kinetics are described by a combination of a mechanistic model part and a multi-layer perceptron (MLP) as black box part. In the second part, a structured biological model for A.niger is derived, which also includes online measurement data and can therefore be used for process control. In this model, the product formation kinetics are replaced by an MLP, and different structures and strategies for training the MLP are investigated. A relationship between the glucose concentration in the reactor and the formation of glucoamylase can be revealed. The last part deals with an implementation of a sigma-point Kalman filter based on the derived model for the fed-batch process. In addition, a process optimization is performed to maximize the glucoamylase activity at the end of the cultivation.

Author dc:creator

• Pößel, Till Alexander

Advisor dc:contributor.advisor

• King, Rudibert

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

de

Identifier URI

https://doi.org/10.14279/depositonce-21560

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/22750