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
Journal of the Taiwan Institute of Chemical Engineers
MetadataShow full item record
Minh, L., Duong, P., Mendes Silva Goncalves, J., Kwok, E., & Lee, M. (2017). 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. Journal of the Taiwan Institute of Chemical Engineers, 78 254-264. https://doi.org/10.1016/j.jtice.2017.06.012
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.
uncertainty quantification, variance-based sensitivity analysis, multiplicative dimensional reduction method, polynomial chaos, Gaussian process regression model
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A3A01015621) and by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A6A1031189). Pham L.T. Duong and Jorge Goncalves were supported by FNR CORE project ref 8231540.
External DOI: https://doi.org/10.1016/j.jtice.2017.06.012
This record's URL: https://www.repository.cam.ac.uk/handle/1810/267928
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
Recommended or similar items
The following licence files are associated with this item: