Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise
2015-09
发表期刊JOURNAL OF PROCESS CONTROL (IF:3.3[JCR-2023],3.6[5-Year])
ISSN0959-1524
卷号33页码:140-154
发表状态已发表
DOI10.1016/j.jprocont.2015.06.011
摘要Dynamic optimization techniques for complex nonlinear systems can provide the process industry with sustainable and efficient operating regimes. The problem with these regimes is that they usually lie close to the limits of the process. It is therefore paramount that these operating conditions are robust with respect to the parameter uncertainties and to the process noise such that critical constraints are not violated. Besides the uncertainty in the constraints, also the uncertainty in the objective function needs to be taken into account. However, including robustness in an optimization problem typically leads to semi-infinite optimization problems that are challenging to solve in practice. In the current manuscript several computationally tractable methods are exploited to approximately solve the robust dynamic optimization problem. These methods allow the use of fast deterministic gradient based optimization techniques. The first type of methods are based on a linearization approach while the second method exploits the unscented transformation to construct an estimation of the uncertainty propagation. Both types provide the user with an approximation of the variance-covariance matrix of the critical constraints and of the objective function. This allows the user to easily take them into account in the dynamic optimization routine in a stochastic setting without the need of using computationally expensive Monte Carlo simulations in the optimization procedure. Moreover, an iterative scheme is mentioned to evaluate the approximate results and to improve them if necessary. Two illustrative case studies are discussed, a jacketed tubular reactor and the Williams-Otto reactor. (C) 2015 Elsevier Ltd. All rights reserved.
关键词Dynamic optimization Robust optimization Uncertainty propagation Optimal control Parametric uncertainty Process noise
收录类别SCI ; EI
语种英语
资助项目[FWO-G.0930.13]
WOS研究方向Automation & Control Systems ; Engineering
WOS类目Automation & Control Systems ; Engineering, Chemical
WOS记录号WOS:000361934900012
出版者ELSEVIER SCI LTD
EI入藏号20153001057565
EI主题词Covariance matrix ; Intelligent systems ; Iterative methods ; Nonlinear systems ; Optimization ; Stochastic systems
EI分类号Artificial Intelligence:723.4 ; Mathematics:921 ; Statistical Methods:922 ; Mathematical Statistics:922.2 ; Systems Science:961
WOS关键词OPTIMAL EXPERIMENTAL-DESIGN ; BATCH PROCESSES ; DYNAMIC OPTIMIZATION
原始文献类型Article
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2143
专题信息科学与技术学院_PI研究组_Boris Houska组
通讯作者Logist, F.
作者单位
1.Katholieke Univ Leuven, Dept Chem Engn, BioTeC & OFTEC, B-3001 Leuven, Belgium
2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 200031, Peoples R China
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GB/T 7714
Telen, D.,Vallerio, M.,Cabianca, L.,et al. Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise[J]. JOURNAL OF PROCESS CONTROL,2015,33:140-154.
APA Telen, D.,Vallerio, M.,Cabianca, L.,Houska, B.,Van Impe, J.,&Logist, F..(2015).Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise.JOURNAL OF PROCESS CONTROL,33,140-154.
MLA Telen, D.,et al."Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise".JOURNAL OF PROCESS CONTROL 33(2015):140-154.
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