Reduced Order Model Based on Principal Component Analysis for Process Simulation and Optimization

TLDR

Distributed‑parameter CFD models provide more accurate results than lumped‑parameter unit‑operation models, and their use in process‑equipment co‑simulation can optimize plant performance for complex thermal and fluid‑flow phenomena. The study aims to develop fast reduced‑order models (ROMs) that closely approximate high‑fidelity CFD equipment models within co‑simulation. ROMs are constructed by applying principal component analysis to CFD data generated with commercial tools such as Aspen Plus and FLUENT, enabling systematic, efficient model creation for complex‑geometry equipment. The ROMs are robust over well‑sampled input domains, cut CPU time from hours to seconds, and two power‑plant case studies demonstrate their effectiveness for process simulation and optimization.

Abstract

It is well-known that distributed parameter computational fluid dynamics (CFD) models provide more accurate results than conventional, lumped-parameter unit operation models used in process simulation. Consequently, the use of CFD models in process/equipment co-simulation offers the potential to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. Because solving CFD models is time-consuming compared to the overall process simulation, we consider the development of fast reduced order models (ROMs) based on CFD results to closely approximate the high-fidelity equipment models in the co-simulation. By considering process equipment items with complicated geometries and detailed thermodynamic property models, this study proposes a strategy to develop ROMs based on principal component analysis (PCA). Taking advantage of commercial process simulation and CFD software (for example, Aspen Plus and FLUENT), we are able to develop systematic CFD-based ROMs for equipment models in an efficient manner. In particular, we show that the validity of the ROM is more robust within well-sampled input domain and the CPU time is significantly reduced. Typically, it takes at most several CPU seconds to evaluate the ROM compared to several CPU hours or more to solve the CFD model. Two case studies, involving two power plant equipment examples, are described and demonstrate the benefits of using our proposed ROM methodology for process simulation and optimization.