Smarter Filtration Design through Digital Modeling and AI
Dr. Andreas Wiegmann
CEO
Math2Market GmbH, DE
Speaker Bio
Dr. Wiegmann is co-founder of Math2Market GmbH. Andreas Wiegmann holds a master’s degree in Mathematics from Karlsruhe Institute of Technology, and a Ph.D. in Mathematics from the University of Washington in Seattle, USA.
Before founding Math2Market, he had 2 years of experience as Postdoctoral Fellow at the University of California at Berkeley and Lawrence Berkeley National Lab, and 12 years experience as Deputy Head of Department in the Department "Flow and Material Simulation" at Fraunhofer ITWM. He initiated and led the development of the GeoDict software and built the team and the customer base that laid the foundations for Math2Market.
Presentation time
December 3, 2025
11:10 am - 11:25am EST
Abstract
The design and optimization of filters and filter media have traditionally relied on extensive prototyping and empirical testing. These approaches are both time-consuming and resource intensive. Still, digital modeling and simulation present an efficient alternative, enabling faster innovation, cost reduction, and deeper understanding of microstructure–performance relationships.
This presentation shows how advanced digital tools support a full digital workflow for filter development, starting from virtual 3D modeling of media microstructures to complete filter assembly simulations. Key properties such as pore size distribution, permeability, and pressure drop can be predicted, while particle transport, deposition, and clogging behavior can be analyzed and filter efficiency and filter lifetime can be quantified.
By virtually exploring key design parameters such as fiber diameter, porosity, pleat geometry, and housing effects, digital modeling streamlines development by reducing experimental iterations and accelerating product optimization. Insights from industrial case studies highlight the benefits of simulation in performance prediction, material selection, and lifetime assessment. Filtration R&D is shifting from empirical testing toward a largely digital, data-driven, simulation-based approach for the design of more efficient, durable, and sustainable filtration solutions.