Machine Learning‐Enhanced Hybrid Modeling for Phenotype Prediction and Bioreactor Optimization

This chapter explores the use of machine learning in bioprocesses, particularly in the context of modern bioprocess development. While mechanistic models offer interpretability grounded in first principles, they often fail to capture the complexity of biological systems due to oversimplifying assumptions, whereas data-driven models, such as machine learning techniques, can learn complex nonlinear behaviors, but require large datasets and lack physical insight. To overcome these challenges, hybrid modeling is employed, which combines mechanistic and machine learning approaches, leveraging the strengths of both. A detailed methodology is presented and applied to a case study on fed-batch microalgal lutein production, where a neural network is integrated into a kinetic model to estimate critical time-varying parameters to improve model fidelity. The resulting hybrid model demonstrates accuracy and is subsequently used to identify optimal feeding strategies, showcasing the practical value of hybrid modeling in bioprocess optimization under data availability constraints.