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[2506.12819] Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Summary
This article reviews and compares nonlinear model order reduction methods for dynamical systems in process engineering, highlighting their strengths and weaknesses.
Why It Matters
The ability to reduce complex dynamical models to simpler forms is crucial for real-time optimization and control in engineering applications. This review provides insights into various methods, helping researchers and practitioners choose appropriate techniques for their specific needs.
Key Takeaways
- Nonlinear model order reduction is essential for real-time applications in process engineering.
- The article compares eight established reduction methods, detailing their characteristics and applicability.
- An extension of manifold-Galerkin approaches is proposed to include inputs in reduced state subspace.
- Strengths and weaknesses of each method are discussed, aiding in informed decision-making.
- The findings can guide future research and practical implementations in dynamical systems.
Electrical Engineering and Systems Science > Systems and Control arXiv:2506.12819 (eess) [Submitted on 15 Jun 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison Authors:Jan C. Schulze, Alexander Mitsos View a PDF of the paper titled Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison, by Jan C. Schulze and 1 other authors View PDF HTML (experimental) Abstract:Computationally cheap yet accurate dynamical models are a key requirement for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review nonlinear model order reduction methods and provide a comparison of method characteristics. Additionally, we discuss both general-purpose methods and tailored approaches for chemical process systems and we identify similarities and differences between these methods. As machine learning manifold-Galerkin approaches currently do not account for inputs in the construction of the reduced state subspace, we extend these methods to dynamical systems with inputs. In a comparative case study, we apply eight established model order reduction methods to an air separation process model: POD-Galerkin, nonlinear-POD-Galerkin, manifold-Galerkin, dynamic mode decomposi...
