Data‐Augmented Deep Learning Algorithm for Accurate Control of Bioethanol Fermentation Using an Online Raman Analyzer

ABSTRACT Fed‐batch fermentation has become the preferred strategy in many industrial biomanufacturing processes. However, a key challenge remains in optimizing the feeding strategy to achieve stable maximum yields. In this study, we present an online Raman spectroscopy‐based monitoring and control system, using bioethanol production by Saccharomyces cerevisiae as a case study. To address the issue of limited labeled data, a pseudo‐labeling approach based on semi‐supervised learning was employed, expanding the available training data set by 100‐fold compared to conventional labeling methods. In addition, we developed a spectral‐temporal concatenation convolutional neural network (STC‐CNN) that incorporates sequential spectral features. Comparative evaluations with multiple machine learning algorithms demonstrated the superior performance of STC‐CNN, achieving a root mean square error (RMSE) of 3.63 g/L for glucose prediction. The system enabled rapid and automated glucose feeding to maintain various target concentrations. Notably, a glucose setpoint of 30 g/L yielded the highest ethanol concentration of 140.68 g/L—an increase of 3.85% over traditional Fed‐batch fermentation—while reducing glycerol by 6.67%. These results highlight the significant potential of Raman spectroscopy combined with deep learning for automated bioprocess optimization and discovery of optimal operating strategies.