A new digital twin for enzymatic hydrolysis processes applied to model-based process design - PhDData

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A new digital twin for enzymatic hydrolysis processes applied to model-based process design

The thesis was published by Appl, Christian Bernhard, in August 2023, UCL (University College London).

Abstract:

Renewable raw materials containing starch and proteins are split into their main components using enzymatic hydrolysis processes. However, even small changes in temperature, pH or pressure may strongly affect the enzyme activity and stability. At the same time, natural fluctuations may lead to changes in the substrate composition. These mutually influencing factors place enormous demands on the design and control of enzymatic hydrolysis processes.
Individual enzymatic hydrolysis processes have already been modelled, but models for the hydrolysis of potato starch by α-amylase and glucoamylase and the proteolysis of organic sunflower seed meal by endopeptidase and exopeptidase in a stirred tank reactor, or even digital twins, are unavailable. Therefore, a new mechanistic model for the combined starch hydrolysis and proteolysis was developed.
Sigmoidal and double sigmoidal functions were implemented to map the temperature and pH-dependent enzyme activity. The model can simulate the enzymatic hydrolysis processes with an agreement of more than 90%. The new model was integrated into an existing digital twin of a 20 L stirred tank reactor to create a new stand-alone digital twin for enzymatic hydrolysis processes.
Applying the new digital twin core model, a model-based process design strategy based on the open-loop-feedback-optimal and model-based design of experiment strategies was established. By applying the new strategy, the amount of α-amylase and glucoamylase required for starch hydrolysis could be reduced by more than 30%. In addition, the required amount of endopeptidase and exopeptidase for proteolysis could be reduced by more than 50%. Compared to the classic design of experiments approach, the number of experiments required for process optimisation could be reduced by more than 50%.
The strategies resulting from this work can soon be used for the optimisation of the industrial organic nutrient media production from regenerative substrates for the cultivation of microorganisms such as Saccharomyces cerevisiae.



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