Uncertainty quantification and global sensitivity analysis of complex chemical processes with a large number of input parameters using compressive polynomial chaos-Reference-Cited by-同舟云学术

Uncertainty quantification and global sensitivity analysis of complex chemical processes with a large number of input parameters using compressive polynomial chaos

Published:2016-11 Issue: Volume:115 Page:204-213
ISSN:0263-8762
Container-title:Chemical Engineering Research and Design
language:en
Short-container-title:Chemical Engineering Research and Design

Author:

Duong Pham Luu Trung,Minh Le Quang,Pham Tram Ngoc,Goncalves Jorge,Kwok Ezra,Lee Moonyong

Funder

Basic Science Research Program through the National Research Foundation of Korea (NRF)

Priority Research Centers Program through the National Research Foundation of Korea (NRF)

Publisher

Elsevier BV

Subject

General Chemical Engineering,General Chemistry

Reference34 articles.

1. Reliability of complex chemical engineering processes;Abubakar;Comput. Chem. Eng.,2015

2. Chance constrained optimization of process systems under uncertainty: I. Strict monotonicity;Arellano-Garcia;Comput. Chem. Eng.,2009

3. AspenHYSYS, 2006. Aspentech. http://www.aspentech.com/core/aspen-hysys.aspx (accessed 30.04.16.).

4. Water quality modeling: a review of the analysis of uncertainty;Beck;Water Resour. Res.,1987

5. Monte Carlo Methods: A Powerful Tool of Statistical Physics;Binder,1998

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review;The Canadian Journal of Chemical Engineering;2023-06-21

2. An Efficient Polynomial Chaos Expansion Method for Uncertainty Quantification in Dynamic Systems;Applied Mechanics;2021-07-12

3. Uncertainties of Lumped Reaction Networks in Reactor Design;Industrial & Engineering Chemistry Research;2020-05-18

4. Reliability analysis and design of a single diode solar cell model using polynomial chaos and active subspace;Microelectronics Reliability;2019-09

5. Sparse Bayesian learning for data driven polynomial chaos expansion with application to chemical processes;Chemical Engineering Research and Design;2018-09