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October 15, 2025Open Access

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison

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Nonlinear model order reduction methods enhance the efficiency of computationally expensive dynamical systems.
Eight established methods, including POD-Galerkin and dynamic mode decomposition, were applied to an air separation process.
The review identifies similarities and differences among general-purpose and tailored approaches for process systems.
An extension of manifold-Galerkin approaches allows handling of inputs in constructing reduced state subspaces.

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This review evaluates nonlinear model order reduction methods in process engineering, highlighting their strengths and weaknesses.

Schulze et al. (2025) studied this question.

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