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CHASE Process Digitalization


In our Area 1, we offer you expertise in Polymer Characterization, Polymer Processing, and Polymer Engineering to help you optimize and standardize your polymer manufacturing processes through monitoring and controlling, particularly in the field of Thermoplastic Composites, especially UD Tapes.


Our goal is to enable you to better know how to run your processes to get the desired results and to improve them technically, economically, and environmentally.

DI Dr. Christian Marschik, Area 1 Manager

Digitalization Polymer Processing

R&D Infrastructure Site Linz

+43 664 8568520


Dr. Karin Kloiber, BSc, Area 1 Manager

Digitalization Chemical Systems

Key Researcher Digitalization

+43 664 8481317

Karin Stoiber


Univ.-Prof. Dr. Zoltan Major, JKU Linz

Univ.-Prof. DI Dr. Georg Steinbichler, JKU Linz


Bilfinger, Covestro, Engel, EREMA, FACC, Festo, Greiner Perfoam, Leistritz, Renolit, Thermo Fisher


  • Establish (generic) workflows for the development of static, dynamic and adaptive digital twins for process analysis, optimization and control.

  • Application specific modelling approaches (mechanistic, data-driven, hybrid) required for established digital twins at different scales from a single processing step to the whole production system.

  • Useable digital twins for different processes: (bio-)chemical and polymer processing.

  • Dynamic evaluation and validation as well as demonstration of increased process and product quality stability by means of digital twins.


  • Application oriented bottom up design by consideration of industry and SME-requirements.

  • First principles modelling (mechanistic models) combined with data-driven approaches (like machine learning) yielding powerful hybrid approaches.

  • Comprehensive data collection and analytics integrating process / system wide data sources for knowledge discovery and model generation.

  • Novel machine learning approaches supporting mechanistic model generation as well as efficient model transfer between similar processes.

  • Leveraging and extending existing industrial IoT technologies provided by industrial partners.

  • Technology based smart living labs for evaluation and demonstration.


  • Improved quality assurance of processes and products enabled by digital twins.

  • Improved resource efficiency of processes by means of predictive maintenance enabled by digital twins.

  • Improved process efficiency. 

  • Prototypical implementation of digital twins for smart polymer production.

  • Experimental evaluation and validation of developed models within pilot plants.


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