Multi-scenario modelling of uncertainty in stochastic chemical systems

Evans, Rob; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.jcp.2014.05.028

Cited by 16 publications

(12 citation statements)

References 34 publications

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“…Previous experimental and theoretical studies have reported that the activation energies associated with Ni catalytic methane cracking are in the range of 0.91-1.01 eV [98-100] and 1.06-1.46 eV [101][102][103][104][105][106], respectively. These discrepancies in the results illustrate the uncertainties in the DFT estimation of activation energies which translates into variability in the predicted CNT growth rate; this is a key emerging subject in multi-scale modeling [107][108][109][110]. To examine the sensitivity of the predicted CNT growth rates on the energetics used in the AKMC simulations, the uncertainty associated with the energetics in the critical elementary steps was studied in this work.…”

Section: Sensitivity On the Kinetic Parametersmentioning

confidence: 99%

Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

Croiset

Ricardez‐Sandoval

2015

Journal of Catalysis

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Section: Sensitivity On the Kinetic Parametersmentioning

confidence: 99%

Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

Croiset

Ricardez‐Sandoval

2015

Journal of Catalysis

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“…30 Neglecting model-plant mismatch in modelbased optimization and control schemes can cause substantial deviations from the predicted reactor performance, which can lead to considerable losses in reactor efficiency. [31][32][33][34][35][36] It is therefore necessary to develop tools that can efficiently account for uncertainty in multiscale system models. One approach to improve the control performance of the batch processes in the presence of model-plant mismatch is run-torun control, where data from previous batches is used to update the parameters and reduce the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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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“…In catalytic reactor systems, one of the most common and significant sources of model‐plant mismatch stems from uncertainty in the system parameters . Parametric uncertainty typically originates from inaccuracies in measuring the parameter values from experimental results or Density Functional Theory (DFT) simulations, which are limited by experimental/measurement errors and truncation errors respectively . In addition, large kinetic models often contain a sizeable number of kinetic parameters that cannot be accurately determined from experimental data.…”

Section: Introductionmentioning

confidence: 99%

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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Multi-scenario modelling of uncertainty in stochastic chemical systems

Cited by 16 publications

References 34 publications

Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

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

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

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