Lencastre Fernandes, Rita1; Bodla, Vijaya Krishna1; Carlquist, Magnus8; Heins, Anna-Lena1; Eliasson Lantz, Anna1; Sin, Gürkan7; Gernaey, Krist V1
1 Department of Chemical and Biochemical Engineering, Technical University of Denmark2 Center for Process Engineering and Technology, Department of Chemical and Biochemical Engineering, Technical University of Denmark3 Department of Systems Biology, Technical University of Denmark4 Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark5 Computer Aided Process Engineering Center, Department of Chemical and Biochemical Engineering, Technical University of Denmark6 Lund University7 Centre for oil and gas – DTU, Center, Technical University of Denmark8 Lund University
The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Käppeli (Biotechnol Bioeng 28:927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well.
Advances in Biochemical Engineering/biotechnology, 2013
Fermentation; Identifiability; Modeling; Monte Carlo; PAT; Saccharomyces cerevisiae; Sensitivity; Uncertainty
Main Research Area:
Advances in Biochemical Engineering. Biotechnology