Abstract
Optimizing twin-screw extruder (TSE) performance is critical in the plastics industry but is often resource-intensive. This study introduces a novel surrogate modeling approach using a Recurrent Deep Embedding Network (RDEN) that integrates deep autoencoders with recurrent neural networks to capture sequential dependencies and physical relationships in TSE processes. Leveraging Progressive Latin Hypercube Sampling (PLHS), the RDEN achieves robust predictions of key process variable, like mean residence time. Results demonstrate the model’s accuracy, generalization capabilities, and potential for automated screw design optimization.