Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth

Rasoulian, Shabnam; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.ces.2014.05.027

Cited by 58 publications

(66 citation statements)

References 43 publications

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“…This averaging technique has been used in previous works to reduce the noise produced by smaller KMC lattices. 35,64 As shown in Figure 4, the noise in the coverage of species A using the averaged 30 3 30 lattices is comparable to the noise of the result obtained from a 100 3 Figure 3. The gap-tooth scheme for the catalytic surface of the reactor.…”

Section: Lattice Size Determination and Solution Approachsupporting

confidence: 56%

“…Needless to say, the computational cost for PSE depends on the number of uncertain parameters and the number of terms considered in the PSE, which both require the calculation of additional sensitivities to construct representative low-order models. 67,68 Adding more terms to the PSE can improve the accuracy of the PSEbased approximation 35,36,38 and the number of terms required for reasonable accuracy depends on the degree of nonlinearity of the system model. Higher-order series expansions improve the model predictions but result in higher computational costs due to the computation of additional higher-order sensitivity terms.…”

Section: Uncertainty Analysis Using Psementioning

confidence: 99%

“…Rasoulian and Ricardez-Sandoval have proposed an uncertainty analysis framework using PSE, which has been applied for robust optimization and online control of a thin film deposition process using standard proportional integral controllers and advanced model-based control schemes. 35,36,[49][50][51] Nagy and Allg€ ower employed a meanvariance approach to integrate the effects of parametric uncertainty into a robust end-point nonlinear model predictive control scheme for a thin film deposition process. 52 These studies, however, analyze the uncertainty propagation in spatially homogeneous multiscale systems.…”

Section: Introductionmentioning

confidence: 99%

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Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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in Wiley Online Library (wileyonlinelibrary.com) Multiscale models have been developed to simulate the behavior of spatially-heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially-dependent. While such a model provides an adequate representation of the catalytic reactor, model-plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE-based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters.

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Section: Lattice Size Determination and Solution Approachsupporting

confidence: 56%

Section: Uncertainty Analysis Using Psementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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“…We acknowledge that it is necessary that other key important effects be included in the present model, such as lateral interactions between the analyte and the extractant, migration effects, and many body and uncertainty effects resulting from surface microstructures. 26,27 However, in this model, the micro-structure geometry was ignored due to the fact that our study was focused on the macro-scale kinetic feature of extraction, in which extraction is fully controlled by the diffusion layer. 28 A refined mesh involving 92,000 finite elements was used in the present analysis.…”

Section: Numerical Simulationmentioning

confidence: 99%

Calibrant Free Sampling and Enrichment with Solid-Phase Microextraction: Computational Simulation and Experimental Verification

Alam

Ricardez‐Sandoval

Pawliszyn

2017

Ind. Eng. Chem. Res.

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Despite the benefits of diffusion-based calibrant-free sampling based on solid-phase microextraction (SPME), this quantification approach is often underestimated due to an inadequate understanding of how extraction parameters influence the extracted amount and quantification of analytes. Currently, application of this approach for complex samples with binding matrix components is very limited. This study presents the development of a computational model that is used to identify the critical parameters for the diffusion-based sampling. Simulations are conducted under simultaneous variations in mass transfer and adsorptive surface binding constants, and the presence of a binding matrix component in the sample. The simulation results correlate well with previously reported experimental data, and improve the predictions when compared to previously introduced semi-empirical models. This work enhanced basic understanding of physical processes involved in analyte quantification with SPME, which is of benefit when performing experimental designs, particularly where traditional calibration methods are not suitable.………………………………………………………………………………..

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“…To provide a confidence final decision, there are two straightforward approaches to cope with uncertainty so far [30], [31]. One of the approaches is to analyse uncertainty as a post-analysis method [32].…”

Section: B Uncertainty Handling In Requirements Selection and Optimimentioning

confidence: 99%

Exact Analysis for Next Release Problem

Li¹

2016

2016 IEEE 24th International Requirements Engineering Conference (RE)

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Abstract-In software engineering, determining the set of requirements to implement in the next release is a critical foundation for the success of a project. Inappropriately including or excluding requirements may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. In the meantime, uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and could lead to unsound requirement decisions. To ease the impact of uncertainty in the software development process, it is important to provide techniques that explicitly manage uncertainty in requirements analysis and optimisation.This proposed research aims to provide a decision support framework for analysing uncertainty in requirements selection and optimisation. The proposed research involves three stages. Firstly, a simulation optimisation technique is introduced to model requirements uncertainty in requirements optimisation. Then, an exact technique is designed to eliminate the algorithmic uncertainty. Lastly, a probabilistic uncertainty analysis is applied to help the decision maker to understand requirement uncertainty propagation and the characteristics of requirements in requirements selection process.

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Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth

Cited by 58 publications

References 43 publications

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Calibrant Free Sampling and Enrichment with Solid-Phase Microextraction: Computational Simulation and Experimental Verification

Exact Analysis for Next Release Problem

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