Reduced Order Model Based on Principal Component Analysis 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.

Evaluation of the potential of retrofitting a coal power plant to oxy-firing using CFD and process co-simulation

“…However, the heat transfer in the boiler is highly non-linear with respect to the coal feed rate and/or oxygen concentration. Lang et al [34] employed a principal component analysis (PCA) with a neural network mapping technique to develop the ROMs to interpolate the flow field inside a gasifier and results with satisfying fidelity were obtained. However, this approach is quite complicated in its mathematical form.…”

Systematic Modeling for Optimization

“…Lastly, reduced order models (ROM) have become fashionable in recent years. Especially notable are Lang et al (2009), who deduced ROMs from CFD simulations. The only disadvantages here are the expensive set-up procedure and the small area of applicability.…”

Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms

“…The order reduction can be achieved through multivariate analysis techniques as in principal component analysis (PCA) or proper orthogonal decomposition. [124][125][126][127][128]. Alternatively a computationally expensive model can be replaced by lower dimensional surrogate model obtained through fitting of experimental or simulated data using techniques such as kriging, response surface methodology (RSM), artificial neural networks (ANN) or high dimensional model representation (HDMR) [12,[129][130][131][132][133][134].…”

“…However these models are computationally expensive to evaluate and thus may not be useful for simulation and optimization purposes. Reduced order modeling based on principal component analysis was introduced by Lang et al [128] for the co-simulation of CFD models with unit operation models for process equipment. Boukouvala et al [110] have shown that the same approach can be applied to the reduction of DEM data for use in solids process models.…”

“…Lang et al [124,128] have described the use of Neural Networks for this mapping, while Boukouvala et al [110] have described the use of Kriging. Regardless of the type of mapping used, it is important verify the model accuracy e.g., via cross validation.…”