Hybrid Modeling for Bioprocesses: Architectures, Applications, and Perspectives

ABSTRACT Recent hardware developments within industrial digitalization make more evident the need of high‐fidelity models to exploit process system engineering potential in biomanufacturing. Hybrid models, which combine first‐principles (white‐box) and data‐driven (black‐box) approaches, aim to leverage the strengths of both methodologies to mitigate their individual limitations to provide high predictive power. This article explores the integration of hybrid modeling techniques in bioprocess engineering, emphasizing their potential to facilitate product development, accelerate experimental design, enhance process monitoring, drive optimization and catalyze the implementation of digitalization strategies to achieve a more robust and sustainable production in the framework of the goals set by the UN 2030 agenda. In this review, we analyze 270 publications from the early 1990s to 2024, extracting application domains, hybrid‐model architectures, implementation software, and nonparametric output structures. Our findings highlight marked improvements in predictive accuracy, and extrapolation capabilities while revealing persistent challenges in integrating mechanistic and empirical components. This review on hybrid modeling implementations shows their potential to enhance accuracy, calibration, and extrapolation capabilities, highly required for bioprocesses. This work underscores the importance of integrating mechanistic and data‐driven approaches to improve the flexibility and interpretability of models in complex engineering systems, offering significant implications for more sustainable and innovative bioprocesses.