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首页> 外文期刊>Journal of the Taiwan Institute of Chemical Engineers >A two-stage approach of multiplicative dimensional reduction and polynomial chaos for global sensitivity analysis and uncertainty quantification with a large number of process uncertainties
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A two-stage approach of multiplicative dimensional reduction and polynomial chaos for global sensitivity analysis and uncertainty quantification with a large number of process uncertainties

机译：具有大量过程不确定性的全局敏感性分析和不确定性量化的乘法尺寸减少和多项式混沌的两级方法

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Uncertainties associated with estimates of model parameters are inevitable when simulating and modeling chemical processes and significantly affect safety, consistency, and decision making. Quantifying those uncertainties is essential for emulating the actual system behaviors because they can change the management recommendations that are drawn from the model. The use of conventional approaches for uncertainty quantification (e.g., Monte-Carlo and standard polynomial chaos methods) is computationally expensive for complex systems with a large/moderate number of uncertainties. This paper develops a two-stage approach to quantify the uncertainty of complex chemical processes with a moderate/large number of uncertainties (greater than 5). The first stage applies a multiplicative dimensional reduction method to approximate the variance-based global sensitivity measures (Sobol's method), and to simplify the model for the uncertainty quantification stage. The second stage uses the generalized polynomial chaos approach to quantify uncertainty of the simplified model from the first stage. A rigorous simulation illustrates the proposed approach using an interface between MATLAB and HYSYS for three complex chemical processes. The proposed method was compared with conventional approaches, such as the Quasi Monte-Carlo sampling-based method and standard polynomial chaos-based method. The results revealed the clear advantage of the proposed approach in terms of the computational efforts. (C) 2017 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译：在模拟和建模化学过程并显着影响安全性，一致性和决策时，与模型参数估计相关的不确定性是不可避免的。量化这些不确定性对于模拟实际系统行为至关重要，因为它们可以更改从模型中汲取的管理建议。使用常规方法进行不确定量化（例如，Monte-Carlo和标准多项式混沌方法）对于具有大/中等数量的不确定性的复杂系统来计算昂贵。本文开发了两阶段方法，以量化复杂化学过程的不确定性，具有中等/大量的不确定性（大于5）。第一阶段应用乘法尺寸减少方法，以近似于基于方差的全局灵敏度测量（Sobol的方法），并简化了不确定性量化阶段的模型。第二阶段使用广义多项式混沌方法量化从第一阶段的简化模型的不确定性。严格的模拟说明了使用Matlab和Hysys的接口进行三种复杂化学过程的所提出的方法。将所提出的方法与常规方法进行比较，例如基于准蒙特卡罗采样的方法和基于标准多项式混沌的方法。结果表明，在计算努力方面，拟议方法的明显优势。 （c）2017台湾化工工程师研究所。 elsevier b.v出版。保留所有权利。

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