Universal hybrid modeling of batch kinetics of aerobic carotenoid production using Saccharomyces Cerevisiae

Modeling a bio-fermentation process accurately is a difficult task given the complex interactions that occur within it. Usually, a first-principles approach is employed to build a model which captures its essential dynamics. But building an accurate model using this approach is time consuming and resource-intensive because it is quite challenging to mathematically quantify all the complex interactions that occur within the process. Therefore, hybrid model wherein a first-principles model is integrated with a data-driven model to achieve greater accuracy and robustness is an appealing alternative. In this manuscript, we develop a hybrid model using a physics-informed machine learning method called Universal Differential Equations (UDEs) for a bio-fermentation process. In this approach a deep neural network (DNN) is utilized to approximate the derivative of the unknown dynamics that occur within the process. The trained DNN is inserted in the ODEs that represent the first-principles model of the process, and the resultant hybrid model is solved using modern ODE solvers. This universal hybrid model gives greater accuracy compared to the original first-principles model.