Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process

• Published in: Journal of intelligent manufacturing Vol. 36; no. 6; pp. 3887 - 3922
• Main Authors: Wang, Tianyu, Zheng, Ruixiang, Li, Mian, Cai, Changbing, Zhu, Siqi, Lou, Yangbing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2025; Springer Nature B.V
• Subjects: Ablation; Adaptation; Assembly lines; Business and Management; Comparative studies; Control; Data transmission; Deep learning; Distance learning; Drying; Encoders-Decoders; Machines; Manufacturing; Manufacturing industry; Mechatronics; Neural networks; Prediction models; Predictive control; Process controls; Process variables; Processes; Production; Production lines; Recurrent neural networks; Regularization; Robotics; Time series; Continuous manufacturing processes; Rotary drying; Self-adaptation mechanism; Multiple operating conditions; Continual learning; Real-time prediction

Real-time prediction of future process outputs is critical for the model predictive control of continuous manufacturing processes. It helps identify when and how to adjust the process variables under the disturbances. A lot of recurrent neural network-based predictive models have been developed and...