Towards Practical Model-Based Decision Making
Building Trust in Optimal Experimental Design and Enabling Surrogate Model Reusability
Martin Bubel
Hrsg.: Fraunhofer ITWM
2026, 228 S., num., mostly col. illus. and tab, Softcover
Sprache: Englisch
Kaiserslautern, TU, Diss., 2026
Fraunhofer Verlag
ISBN 978-3-8396-2189-9

Inhalt
Model-based decision making is a cornerstone of modern chemical process engineering, essential for tasks ranging from process design to real-time optimization. However, its widespread application is often hindered by two major bottlenecks: the high cost of experimental data collection for model calibration and the computational burden of rigorous process simulations. To bridge the gap between theory and practice, this thesis advances two critical methodologies: Optimal Experimental Design (OED) and Surrogate Modeling.

Addressing the need for experimental efficiency, this work establishes trust in OED through a wet-lab benchmark study, demonstrating increased data efficiency over traditional factorial designs. It further introduces a novel cubature-based method for uncertainty quantification in nonlinear models. To tackle the lack of reusability in surrogate modeling, the thesis proposes a custom modelfluid representation. This enables the training of a distillation column surrogate that generalizes across chemical systems. Together, these contributions provide a synergistic framework for more efficient and scalable model-based engineering.

Verfügbare Formate

Softcover
EUR 69.00 (* inkl. MwSt.)
erscheint ca. Mai 2026