EXCELLENT RESEARCH

Here we showcase selected COMET projects where CHASE research has delivered measurable results for industrial partners and scientific advancements in process digitalization, process optimization and circular process streaming:

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Adjustment of Component Color in the Injection Molding of Recycled Plastics ↗

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This project developed an inline color measurement and model‑based masterbatch control system for injection molding of recycled plastics, addressing color variation in heterogenous recyclate streams. A mathematical model calculates optimal masterbatch addition based on real‑time color sensor readings, enabling automated adjustment of component color under fluctuating input conditions. The solution reduces rejects and material usage while ensuring consistent product color quality, and has already been successfully tested on various industrial post‑industrial and post‑consumer waste streams in collaboration with project partners JKU Linz and ENGEL.

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Multiphysics Simulation Framework for Pyrolysis Optimization ↗

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This project developed a comprehensive multiphysics simulation framework to accurately capture key physical and chemical phenomena in industrial biomass pyrolysis, enabling detailed analysis of flow, reaction kinetics, and heat transfer under realistic operating conditions. Based on the open-source CFD software OpenFOAM®, the framework integrates complex multi-species transport and combustion modeling with kinetic pyrolysis effects, overcoming simplifications of traditional models and providing precise insights into system behavior for improved efficiency and energy recovery. The adaptable simulation tool is now being applied to auger reactor designs, enhancing performance prediction and optimization across diverse pyrolysis applications, in collaboration with Next Generation Elements GmbH and TU Wien.

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Hydrogenation of CO2 to Methanol over High Entropic Oxide Catalysts ↗

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This project focused on converting (bio-)CO₂ into methanol using high entropic oxide catalysts to enable a circular carbon economy by returning waste CO₂ to a closed-loop system. Researchers designed and optimized heterogeneous catalysts with earth-abundant materials to improve activity and stability, laying the groundwork for scalable, sustainable methanol synthesis. The outcomes support industrial adoption of green hydrogen and CO₂-based chemical production in collaboration with JKU Linz and OMV.

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Building a Digital Twin for Industrial Bioprocesses ↗

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In this project CHASE and its partners, including Festo SE & Co. KG and TU Wien, developed a digital twin prototype for real-time monitoring and control of bioreactor fermentation processes by integrating mechanistic models with soft sensors to estimate biomass, substrate uptake, and product formation. The solution incorporates a model predictive controller to guide process behavior based on real-time data, enhancing process understanding and enabling predictive decision making for advanced bioprocess operation. This approach lays a strong foundation for knowledge-based bioprocess optimization and online control in industrial settings.

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Modeling the Consolidation Process of Thermoplastic Composites ↗

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This project developed and validated a physics-based simulation approach to predict the consolidation behaviour of thermoplastic continuous fiber-reinforced composites, correlating critical process parameters with final product quality. By adapting an OpenFOAM® solver to model heat transfer and squeeze flow during the heating, pressing, and cooling stages, the team achieved accurate prediction of temperature distribution, dimensional change and bond strength across laboratory and industrial scales. The resulting simulation supports improved process design and optimization with positive effects on material, time and energy efficiency, in collaboration with partners including JKU Linz, Covestro, ENGEL and FACC.

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Closing the Loop in the Chemical Recovery of the Pulp and Paper Industry Using Raman Spectroscopy ↗

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This project demonstrated the implementation of Raman spectroscopy as a non-invasive, on-line process analytical technology (PAT) tool to monitor and optimize chemical recovery in the pulp and paper industry, addressing challenges in lignin-related process streams. By combining Raman spectroscopy with multivariate regression models, critical process parameters such as SO₂ concentrations were monitored in real time, enabling improved process control and reduced downtime from precipitation of insoluble salts. Successful tests at the Sappi Europe Gratkorn mill in collaboration with TU Wien highlight the potential for continuous industrial process monitoring and enhanced operational stability in harsh chemical environments.

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Holistic Design of Experiments for Biopharmaceutical Control Strategies ↗

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This project introduced a novel holistic design of experiments (hDoE) approach that uses integrated process models to optimize experimental runs across interconnected unit operations, enhancing process characterization efficiency and robustness in biopharmaceutical production. By combining multivariate regression with model-based analyses, the method significantly reduced experimental effort while improving predictions of out-of-specification events, ultimately supporting more robust process development and quality assurance. The innovative approach was validated through simulation studies and tested with partners including Körber Pharma Austria and TU Wien, demonstrating substantial improvements in experimental efficiency and process understanding.

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​From Waste to High-Value Applications - A Step towards a Circual Economy for Plastics ↗

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This project developed and implemented an advanced model-based control strategy for low-density polyethylene (LDPE) gas phase reactors to improve product quality stability and reduce off-specification events. By combining mechanistic reactor modeling with real-time data analytics and simulation, the team enhanced the prediction and adjustment of operating conditions, leading to tighter control over polymer properties and improved overall process performance. Successful industrial validation demonstrated significant improvements in consistency and reduced waste, supporting more efficient and sustainable LDPE production in collaboration with industrial partners.

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​Modeling the Pumping Capability of Single-Screw Extruders ↗

 

This project developed a hybrid modeling approach to predict the pumping capability of single-screw extruders by coupling analytical, numerical, and data-based models, addressing the complex flow behaviour of polymer melts under industrial conditions. The method produces fast, robust regression models that match the accuracy of detailed simulations while significantly reducing computation time, enabling efficient prediction for digital twins, assistance systems, and soft sensors. Validation across varied screw designs, materials, and processing conditions demonstrated high accuracy and practical utility, supporting improved extruder performance and process optimization in collaboration with partners JKU Linz, TU Wien, and SCCH.

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Modeling of the Devolatilization Process in an Extruder ↗

 

​This project developed and compared theoretical models to predict devolatilization performance in vented screw extruders, a key step in ensuring consistent quality during polymer recycling processes. Validated against experimental extrusion data, the models provide insight into mass transport behaviour in the degassing zone, enabling more accurate prediction of devolatilization and supporting improved extruder design and control strategies. The work was carried out in collaboration with JKU Linz and EREMA GmbH to enhance understanding of devolatilization phenomena under industrial conditions.

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​Quantum Cascade Laser Sensor for Sulfur Species Detection in Petrochemical Process Streams ↗

 

This project developed a highly sensitive quantum cascade laser (QCL) sensor platform for detecting sulfur compounds such as hydrogen sulfide (H₂S), carbonyl sulfide (COS), and methyl mercaptan in petrochemical process gas streams at sub‑parts‑per‑million levels. By integrating multiplexed continuous‑wave laser spectroscopy with a custom FPGA‑based signal processing system, the sensor achieves very low detection limits and robust operation under industrial safety standards. Validated in collaboration with industrial partner OMV and TU Wien, the prototype enables enhanced process control and quality assurance in harsh petrochemical environments.

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Controlling of Refolding Kinetics by Combining Pat and Modeling ↗

 

This project developed an automated characterization framework for polymer films using inline imaging combined with machine learning to rapidly assess quality attributes such as thickness uniformity and defect prevalence. By training predictive models on high‑resolution spectral and visual data, the system enables real‑time quality assessment during production, reducing manual inspection effort and improving consistency. Validated with industrial partners including Renolit SE and Festo SE & Co. KG, the prototype demonstrates significant potential for efficient, data‑driven quality control in polymer film manufacturing.

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Fundamental Investigations for the Optimization of Foaming Processes ↗

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This project advanced understanding of foam injection molding by developing novel measurement methods and simulations to capture the complex interactions of gas‑polymer mixtures and process parameters, a key step toward automated, self‑adjusting Industry 4.0 production. By introducing the Bulk Modulus Method for in‑line dynamic solubility measurement and designing specialized rheology dies to obtain process‑relevant data, the team enabled more accurate characterization of foaming behaviour with reduced energy consumption and improved sustainability. Ongoing work focuses on enhancing autonomous process control and expanding simulations for polymer‑gas interactions, in collaboration with partners ENGEL and JKU Linz.

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