Batch-Aware Machine Learning for Early Warning and Soft Sensing in Industrial Fed-Batch Fermentation

Industrial fed-batch fermentation generates coupled, time-dependent process data that are difficult to interpret using isolated alarms. This study developed a retrospective, batch-aware machine learning framework integrating operational safety margins, anomaly characterization, 30 min critical event prediction, and 60 min product concentration forecasting. Complete fermentation batches were separated into training, validation, and locked test sets to prevent within-batch information leakage. Linear and ensemble classifiers and regressors were evaluated against temporal and feature ablation baselines. On the locked test set, gradient boosting was the most selective classifier (accuracy = 0.901; sensitivity = 0.556; false alarm rate = 3.1%), random forest prioritized sensitivity (accuracy = 0.479; sensitivity = 0.857; false alarm rate = 59.5%), and logistic regression showed an intermediate operating point (accuracy = 0.878; sensitivity = 0.571; false alarm rate = 6.2%). The ridge increment model achieved an RMSE = 1.173 g/L and R2 = 0.9984 versus RMSE = 4.072 g/L and R2 = 0.9807 for persistence; removing the current product concentration increased the RMSE to 10.078 g/L and reduced R2 to 0.8819. The archived process risk and CCP state formulas were recovered and verified against all source records, enabling a fully auditable rule-based retrospective scenario for alarm burden and downtime. The contribution is therefore workflow integration and traceable batch-level validation rather than a new learning algorithm. The framework is intended for operator-oriented decision support and requires prospective, cross-campaign and cross-reactor validation before deployment.