Microreactors have been used for acquiring process data while consuming significantly lower amounts of expensive reagents. In this article, the combination of microreactor technology and computational fluid dynamics (CFD) is shown to contribute significantly towards understanding the diffusional properties of the substrate and the product of a biocatalytic reaction. Such knowledge is then applied to design reactor configurations. It has been demonstrated that this kind of knowledge is crucial for the choice and design of reactors. In the discussion, it is highlighted how microreactor-based platforms with similar dimensions to the ones tested here can be used as a screening tool for screening biocatalyst and process alternatives.
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.
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