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عنوان فارسی مقاله:
افزایش سرعت و بهینه سازی عدم قطعیت تعریف کمی کروماتوگرافی SMB غیر خطی با استفاده از مدل کاهش مرتبه
عنوان انگلیسی مقاله:
Accelerating optimization and uncertainty quantification of nonlinear SMB chromatography using reduced-order models
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Simulated moving bed (SMB) chromatography, a continuous multi-column chromatographic process, has been recognized as a very useful technology for separation processes and is widely used in food, fine chemistry, and pharmaceutical industries at all production scales (Rajendran et al., 2009). The separation process is accomplished through continuous and counter-current movement of the liquid and solid phases driven by periodically switching the positions ofthe inlet and outlet streams. Due to the periodic switching procedure,the regime ofthe SMB system never reaches a steady state, but rather a cyclic steady state (CSS). That is, during the CSS period, the concentration profiles are still varying over time, but they are identical between two consecutive switching periods. To make full use ofthe economic potential ofthe SMB process, efficient design and optimization of SMB chromatography play an important role and have gained tremendous attention during the past years; see, e.g., Araújo et al. (2006a), Araújo et al. (2006b), Dünnebier et al. (2000), Dünnebier and Klatt (1999), Kawajiri and Biegler(2006), Li et al. (2010), Rajendran et al. (2009), Seidel-Morgenstern et al. (2008), Toumi et al. (2007, 2003). It is worth noting that all the works mentioned are based on high-fidelity models resulting from discretization of the physical model (partial differential equations). To precisely capture the dynamics of the process, these high-fidelity models are often oflarge size and high complexity. The main advantage of using such large-scale high-fidelity models is that the accuracy and reliability of the optimization can be guaranteed. Nevertheless, it is time-consuming to solve such high-fidelity models, especially in many-query contexts, e.g., in optimization, uncertainty quantification (UQ), and real-time control settings. To overcome this obstacle, surrogate models via reduced-order modeling have gained increasing attention in the past decades (Erdem et al., 2004; Forrester and Keane, 2009; Li et al., 2014a,b; Vilas and Vande Wouwer, 2011). Model order reduction (MOR) is a useful tool in handling large-scale computations in science and engineering. MOR aims at constructing a low-cost reduced-order model (ROM), which can reproduce the main dynamics of the large-scale high-fidelity model, called the full-order model(FOM)in this work. Till now, various MOR methods have been proposed and successfully applied to different engineering contexts (Antoulas, 2005; Baur et al., 2014; Benner et al., 2015b, 2005; Patera and Rozza, 2007; Quarteroni and Rozza, 2014; Schilders et al., 2008). However, research on MOR for SMB chromatography is limited in the literature. A balanced truncation MOR method was applied to a linear SMB model in Erdem et al. (2004). Recently, a Krylov-subspace MOR method was successfully applied, also to linear SMB chromatography in Li et al. (2014b). For nonlinear SMB chromatography, the application of proper orthogonal decomposition (POD) can be found in Li et al. (2014a), Vilas and Vande Wouwer (2011). In particular, multi-fidelity surrogate models were discussed in Li et al. (2014a). Nevertheless, the ROM constructed by the POD or Krylov-subspace MOR method is reliable locally, i.e., it is valid only in the neighborhood of the parameter at which the ROM is constructed. As a result, the ROM needs to be updated in many-query contexts, e.g., during the trust-region optimization process in Li et al. (2014a).
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کلمات کلیدی:
Accelerating optimization and uncertainty quantification of nonlinear ... www.sciencedirect.com/science/article/pii/S0098135416303039 by Y Zhang - 2017 - Cited by 1 - Related articles Accelerating optimization and uncertainty quantification of nonlinear SMB chromatography using reduced-order models. Yongjin Zhang, ,; Lihong Feng, ... Accelerating optimization and uncertainty quantification of nonlinear ... https://www.researchgate.net/.../308802392_Accelerating_optimization_and_uncertainty... Accelerating optimization and uncertainty quantification of nonlinear SMB chromatography using reduced-order models on ResearchGate, the professional ... Accelerating Optimization and Uncertainty Quantification of Nonlinear ... pubman.mpdl.mpg.de/pubman/item/escidoc:2351807:1 Author: Zhang, Yongjin et al.; Genre: Journal Article; Title: Accelerating Optimization and Uncertainty Quantification of Nonlinear SMB Chromatography Using ... Accelerating optimization and uncertainty quantification of nonlinear ... https://www.semanticscholar.org/...optimization-and-uncertainty.../87d23d3408eb69521... Semantic Scholar extracted view of "Accelerating optimization and uncertainty quantification of nonlinear SMB chromatography using reduced-order models" by ...
