Evaluating and optimising catalytic filters using mathematical methods

Mario Ohlberger © WWU - Peter Leßmann Catalysts are commonly used to reduce pollutants released in car exhaust. In order to manufacture catalytic filters with even higher efficiency, industry is increasingly relying on innovative mathematical models and machine learning (ML). In the newly launched collaborative research project "ML-MORE", researchers at the Cluster of Excellence "Mathematics Münster" at the University of Münster are working closely with the Fraunhofer Institute for Industrial Mathematics ITWM, the University of Stuttgart, TU Darmstadt and Umicore AG & Co. KG, a materials technology and recycling company. The Federal Ministry of Education and Research (BMBF) is funding the project as part of "Mathematics for Innovations", a programme devoted to developing methods for handling large volumes of data. The BMBF has allocated approximately one million euros to finance the project over the next three years. According to project coordinator Prof. Mario Ohlberger from the Institute for Analysis and Numerics, "mathematics is making enormous gains in the areas of model reduction and machine learning which we and Umicore are now putting to use for industrial application." Due to the current situation, the project kicked off with a video conference.