2017
DOI: 10.1002/cjce.22912
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Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions

Abstract: This paper explores the effects of spatially-varying parametric uncertainty on the performance of a heterogeneous catalytic flow reactor system. The catalytic reactor behaviour is simulated using a spatially-dependent multiscale model that combines kinetic Monte Carlo (kMC) with continuum transport equations to capture the relevant phenomena on the scales in which they occur. Polynomial chaos expansions (PCEs) are implemented to effectively propagate parametric uncertainty through the reactor model. These expa… Show more

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Cited by 11 publications
(6 citation statements)
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“…More efficient selection of uncertain parameter realizations can be achieved using Latin hypercube sampling 5,6 and response surface methodology. [7][8][9] Alternatively, low-order closed-form expressions that relate the uncertain parameters to the observables via sensitivities (Taylor Series/ Power Series expansions [PSE] 7,[10][11][12][13][14] or an orthogonal polynomial basis (Polynomial Chaos expansions [PCE] 2,[15][16][17][18][19][20][21] ) can be developed using a small number of time-consuming evaluations of the full model. Once developed, the low-order expressions can be employed to rapidly construct probability density functions of the observables using a sufficiently large number of realizations of uncertain parameters.…”
Section: Introductionmentioning
confidence: 99%
“…More efficient selection of uncertain parameter realizations can be achieved using Latin hypercube sampling 5,6 and response surface methodology. [7][8][9] Alternatively, low-order closed-form expressions that relate the uncertain parameters to the observables via sensitivities (Taylor Series/ Power Series expansions [PSE] 7,[10][11][12][13][14] or an orthogonal polynomial basis (Polynomial Chaos expansions [PCE] 2,[15][16][17][18][19][20][21] ) can be developed using a small number of time-consuming evaluations of the full model. Once developed, the low-order expressions can be employed to rapidly construct probability density functions of the observables using a sufficiently large number of realizations of uncertain parameters.…”
Section: Introductionmentioning
confidence: 99%
“…It is recommended that the proposed algorithms should be tested on larger-scale SDE parameter estimation problems and that the performance of these algorithm should be compared with particle filter-based methods and PC-based methods. These types of simulations can determine whether the B-spline and other approximations used to develop the proposed methods result in any significant degradation in the quality of parameter estimates when compared with particle filter methods.The computation time for the proposed ABEM and LAB methods is expected to be significantly lower than for particle filter techniques. ,, In future, it will be desirable to test the proposed methodologies using larger-scale models with a larger number of states and parameters …”
Section: Simulation Resultsmentioning
confidence: 99%
“…Monte Carlo sampling of uncertain parameters are then used to compute the distribution for the rate vector and its confidence intervals. In the context of application to uncertain reacting systems, PCE has been found to be more computationally efficient and accurate than power series expansion. …”
Section: Model Development and Analysismentioning
confidence: 99%