Experimental Study on the Scalability of Planetary Roller Extruders

This contribution aims at developing scaling algorithms for planetary roller extruders (PREs). Laboratory‐ and production‐scale experiments were carried out, using thermoplastic polymers according to a statistical design of experiments (DOE). By comparing plant size, spindle configuration, operating parameters, and material properties, their influence on pressure build‐up capacity, process temperatures, and residence time distribution is analyzed. All data generated are used to train MATLAB‐based machine learning models. First indications hint at Gaussian processes and artificial neural networks, predicting operating parameters with high accuracy. Scaling algorithms for planetary roller extruders on laboratory and production scale are developed in this contribution. In systematic studies, different operating conditions are analyzed with regard to process temperatures, pressure build‐up capacity, and residence time distribution. Machine learning models are applied to make predictions of process variables with high accuracy.