SLU-DQN: A Model for Anticipatory Steam Detection for Steamer-Filling in Baijiu Intelligent Distillation Systems

Abstract

The true implementation of the Anticipatory Steam Detection for Steamer-Filling(ASDSF) process in baijiu intelligent distillation systems, which involves predicting and precisely spreading distillers’ grains before steam emerges, remains a critical unresolved challenge. In this study, we introduce the SLU model, which utilizes SwinLSTM as the core feature extraction module and adopts a U-shaped structure. This model achieves spatiotemporal feature extraction and dynamic change prediction. It is further enhanced by integrating a U-Net module for multi-scale feature fusion and optimized through a Deep Q-Network (DQN)-based decision-making process. The SLU-DQN model, specifically designed for anticipatory material spreading planning in the baijiu Steamer-Filling(SF) distillation system, predicts future steam emission areas. Finally, both quantitative and qualitative experimental results demonstrate the excellent performance of the SLU-DQN model in solving the ASDSF problem. The model achieved 91.1% reward accuracy, an F1-Score of 91% for material spreading point prediction, an MSE of 19.02, and an SSIM of 95.8%. These results not only highlight the model’s superior accuracy in predicting future steam emission areas but also provide a significant technical breakthrough for intelligent baijiu distillation systems, filling a crucial gap in the field.