Metals have enormous recycling potential—provided their exact composition is determined and impurities are removed. This is precisely where the AlloySort project at TH OWL comes in. Led by Professor Dr. Markus Lange-Hegermann, the team is developing an AI-based solution for the copper and aluminum industries. Until now, no methods have been available to non-destructively analyze heterogeneous recycled materials and determine the exact composition of mixed scrap.

Using PGNAA, AlloySort combines high-resolution sensor data with advanced AI algorithms, including convolutional neural networks, to enable precise material analysis despite high levels of noise. A sorting and conveyor belt demonstrator illustrates how the system can be integrated directly into industrial production processes. Initial results are impressive: the measurement time for identifying metal alloys has been reduced from two hours to only one second. The next project goal is not only to identify alloy types but also to determine their mixing ratios.

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Vaccines, therapeutic proteins, and monoclonal antibodies are increasingly produced using cell culture technologies. These biological production systems enable the manufacturing of highly effective and safe biopharmaceuticals. The market for such innovative therapeutics has become a major growth driver in the pharmaceutical industry. However, production conditions in bioreactors are highly complex and subject to strict regulatory quality requirements.

The biotechnology research conducted by Professor Björn Frahm and his team provides valuable support in addressing a key challenge in the manufacturing process: How can the seed train be designed to reliably generate the trillions of cells required for industrial-scale bioreactor production while ensuring quality, stability, and efficiency? 

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Cyberattacks on industrial production facilities are becoming increasingly common and pose a significant threat to companies. The effort required for security assessments continues to grow. Since industrial components and production systems are constantly evolving, risks must be reassessed on a regular basis. However, the analyses and certifications remain time- and resource-intensive and rely heavily on the expertise and experience of specialists. Through the project “SUSI – Software-based Support of Security Risk Assessments in Industry,” Professor Dr. Henning Trsek and his team, together with industry partners Weidmüller, Comma Soft, and rt-solutions, are developing intelligent software to support security risk assessments. The project also incorporates AI-based methods to enhance and streamline the assessment process. The goal is to reduce the time required for security risk assessments by up to 20 percent and enable cost savings of up to 50 percent. At the same time, the project aims to increase automation, ease the workload of specialists, and improve the quality and consistency of assessment results.

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