Pressure-Guided LSTM Modeling for Fermentation Quantification Prediction

Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability and transparency of fermentation monitoring. A Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM) was developed to predict Fermentation Percent (FP) and cumulative Fermentation Quantification (FQ) using multivariate time-series data obtained from modular sensor units (PBSU, GBSU, and FQSU). Fermentation conditions were systematically varied under controlled environments, and all data were securely transmitted to a Fermentation-Blockchain-Cloud System (FBCS) to ensure data integrity and traceability. The LSTM models trained on AAG1-3 datasets demonstrated high predictive accuracy, with coefficients of determination (R²) between 0.8547 and 0.9437, and the estimated FQ values showed strong concordance with actual measurements. These results underscore the feasibility of integrating AI-driven prediction models with decentralized data infrastructure for robust and scalable bioprocess control.