Control Strategies for Reactive Extrusion of Polypropylene by Peroxide Degradation A Brief Review and an Experimental Study

Abstract

Abstract Industry 4.0 and digitalization are widely argued for the future success of numerous industrial solutions. Big data management might lead to the assumption that every issue can be solved numerically without any physical background. To some extent, this strategy will help within the plastics industry in general and in the extrusion technology in particular. However, a deep process knowledge together with process-relevant sensors, as well as the right process arrangements within the processing chain combined with smart data mining methods will be still the key success of industry 4.0. This presentation illustrates, based on a brief review on existing control strategies (Part 1), including sensory and predictive control models for reactive extrusion applied at a real-life on-site best practice project (Part 2), possibilities in combination of process tasks with digitalization approaches for PP-Polymer production. Specifically, rheological research conducted with a novel, patented multi-point rheometer (part 3), will provide a deeper insight into dynamic processes such as reactive extrusion. With those results and derivations thereof, improvements in predictive process control in addition to artificial control systems are made and might even lead to further interesting opportunities.