Experimental Study on the Scalability of Planetary Roller Extruders

Abstract

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.

Understanding the Dynamics of Polymer Extrusion: Simulation of Thermoplastics Processing with Planetary Roller Extruders

“…For single- and twin-screw extruders, there is already a large number of investigations and models available to describe and optimize the processes, respectively [ [9] , [10] , [11] , [12] , [13] ]. Previous experimental work on PREs has focused on their compounding behavior [ [14] , [15] , [16] ] and the scale-up process [ 17 ], among other things. Mathematical PRE models, such as those developed by Rudloff et al, are based on experimental data and calculate process variables such as the degree of melting or the residence time along the cylinder of a PRE [ 18 , 19 ].…”