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

Chaffart, Donovan; Rasoulian, Shabnam; Ricardez‐Sandoval, Luis A.

doi:10.1002/aic.15215

Cited by 31 publications

(30 citation statements)

References 73 publications

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“…The following sections summarize the models used to approximate the fluid phase domain and the catalyst surface domain of the multiscale model, while subsequent sections discuss the handshake region and the overall model assembly. Further details about the multiscale catalytic reactor model can be found in our previous work …”

Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

“…Note that the boundary condition shown in Equation depends on parameters that are determined from the microscopic catalyst surface model ( ω s,ads , ω s,des , ω s,prod , and ω s,cons ). Supplementary details about the macroscopic fluid phase model are provided in our previous work …”

Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

“…As a result, it is desirable to determine the smallest lattice size that provides negligible stochastic interference with the results. The kMC models implemented in this work were incorporated using a 30 × 30 lattice size for a total of N total = 900 lattice sites based on test results performed in our previous work …”

Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

“…Both expansions have been incorporated into a number of optimal design and robust optimization studies for a variety of different system models . In particular, these approaches have been previously implemented to propagate uncertainty through multiscale systems such as thin film deposition and catalytic flow reactors . However, the implementation of PSE and PCE is limited in models that lack a closed‐form representation, such as the aforementioned multiscale processes.…”

Section: Introductionmentioning

confidence: 99%

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Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions

Chaffart

Ricardez‐Sandoval

2017

Can J Chem Eng

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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 expansions are used to perform uncertainty analysis on the catalytic reactor system in order to accurately and effectively evaluate and compare the effects of spatially-constant and spatially-varying uncertainty distributions. The uncertainty comparison is further extended through application to robust optimization. To reduce the computational cost of the optimization, statistical data-driven models (DDMs) are identified to approximate the key statistical parameters (mean, variance, and probabilistic bounds) of the reactor output variability for each uncertainty description. The DDMs are incorporated into robust optimization formulations that maximize the reactor productivity and minimize the output variability subject to parametric uncertainty. The results demonstrate the impact of spatially-varying parametric uncertainty on the catalytic reactor performance and highlight the importance of its inclusion to adequately account for phenomena such as catalyst fouling in robust optimization and process improvement studies.

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Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

Section: Catalytic Flow Reactor Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions

Chaffart

Ricardez‐Sandoval

2017

Can J Chem Eng

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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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A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems

Kimaev

Ricardez‐Sandoval

2017

AIChE Journal

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The aim of this article is to compare the performance of efficient uncertainty propagation techniques (Polynomial Chaos [PCE] and Power Series [PSE] expansions) for uncertainty quantification in multiscale systems where discrete (molecular) scale is modeled without closed‐form expressions. A multiscale model of thin film formation by chemical vapor deposition was used to study the effects of single parameter and multivariate uncertainty. For the single parameter uncertainty, 2nd order PSE approximations were the most accurate and computationally attractive. For the multivariate uncertainty, PSE performance deteriorated, while 2nd order PCE provided the highest accuracy when its expansion coefficients were calculated using the Least Squares method. However, comparable accuracy was achieved at half the computational cost when the coefficients were calculated using Nonintrusive Spectral Projection (NISP). The response variables were subsequently controlled using robust optimization, and the results obtained using PCE NISP satisfied the optimization constraints more closely than other methods. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3361–3373, 2017

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

Cited by 31 publications

References 73 publications

Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions

Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions

Exact Analysis for Next Release Problem

A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems

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