Modelling and experimental studies of biocatalytic reactor heterogeneity at lab and pilot scale

The current global population poses unique challenges in meeting demand with the available resources. This large population strains our resources, making it difficult to meet the growing demand for goods such as food, pharmaceuticals, and
chemicals. To balance the planet’s resources with the demands of this population, industry and academia in concert need to provide innovative solutions. Biotechnology, in particular, holds promise as it can help increase food supply without
requiring more land and can shift the production of chemicals used for pharmaceuticals and chemicals from chemical catalysis to biocatalysis.

The transition from chemical catalysis to biocatalysis in the pharmaceutical industry is an exciting development, gaining momentum. Despite the high cost of enzymes and the challenges related to their longterm application, the significant market value of the resulting products ensures positive profitability. This situation also presents a unique opportunity for innovation in the fine (and bulk) chemical industries, where costeffective enzyme applications are needed. By overcoming
these challenges, the scientific community can pave the way for a more sustainable future in chemical production.

Scaling biocatalytic reactions from lab scale to pilot scale, or beyond, presents exciting opportunities, particularly for addressing gradient formation. Such gradients occur when substances must be continuously added to the reactor, which can affect the enzyme’s performance and production efficiency. This thesis aims to devise an innovative mathematical model to predict biocatalytic reactions at a 200 L scale while shedding light on gradient formation. By identifying and understanding the challenges associated with biocatalysis at this scale, the model aims to significantly enhance process efficiency and pave the way for more effective biocatalytic solutions.

To create such a model, the ester hydrolysis reaction catalysed by Candida antarctica lipase B was chosen, and the enzyme kinetics associated with it were measured. A clustered grid compartment model (CGCM) was then constructed, incorporating the enzyme kinetics, pHdetermining equations, and the nonenzymatic chemical reaction. A key advantage of this model is that it not only includes reaction kinetics but also the hydrodynamics of the 200 L reactor. Validation experiments demonstrated that the model’s predictions were accurate. The model was subsequently used to predict various scenarios and to potentially improve the production process by exploring different feeding strategies and the impact of engineered enzymes. 

In conclusion, this model has the potential to significantly enhance the application of biocatalysis, enabling the biocatalytic production of fine (and bulk) chemicals. The insights derived from the model will not only accelerate process development
but also provide valuable guidance for reactor design, feed strategies, and enzyme engineering.