Calibration‐free physics‐informed neural networks for adaptive state inference in bioprocesses

Abstract Accurate real‐time prediction of key quality indicators remains a major challenge in industrial bioprocessing, where complex, time‐varying kinetics and unmeasurable metabolic states hinder process optimization. Existing physics‐informed neural networks (PINNs) offer a promising hybrid paradigm by integrating data and mechanistic knowledge, yet their reliance on fully calibrated models limits robustness under parameter drift and unmeasurable state variables. To address these limitations, this study proposes a calibration‐free PINN framework that jointly estimates key quality indicators and critical, unmeasurable internal system states, enabling robust extrapolation under parameter drift and limited data. Validated by industrial penicillin fermentation (cross‐strain/process), simulations, and chemical cases, the framework demonstrates robust extrapolation under noise and parameter variability, accurately predicting key quality indicators. Empirically, the accuracy of unmeasurable state variable estimation is bounded by that of the physical model parameters. This affirms a high‐precision, strongly generalizable modeling paradigm for complex bioprocesses.