A robust nonlinear model predictive controller for a multiscale thin film deposition process

Rasoulian, Shabnam; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.ces.2015.02.002

Cited by 40 publications

(20 citation statements)

References 38 publications

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“…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%

“…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%

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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: Uncertainty Analysis Using Psementioning

confidence: 99%

Section: Introductionmentioning

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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“…Rasoulian and Ricardez‐Sandoval tackle the same process, developing a robust NMPC algorithm to minimize the surface roughness of the film. They developed a closed‐form model that can predict the control objectives in the presence of uncertainties . Later, they extended their work and applied the model in a stochastic NMPC to provide a robust control strategy for the deposition process under uncertainties in the multiscale model parameters .…”

Section: Introductionmentioning

confidence: 99%

A multi‐scenario nonlinear model predictive control approach for robust product transitions

Flores‐Tlacuahuac

Gutiérrez-Limón

2018

Can J Chem Eng

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Dynamic product transitions are ubiquitous operations in the processing industry. When a first‐principles dynamic model is deployed for real system representation, the calculation of the dynamic optimal trajectory for product transition can be cast as an optimal control problem. A common practice in addressing the solution of optimal product transitions lies in the assumption of free of uncertainty first‐principle models. Ignoring the effect of model uncertainty on product transitions can result in unfeasible dynamic trajectories. In this work, an optimization scenario approach, featuring variable scenario weighting functions, is deployed for assessing the impact of model uncertainty on the control actions such that feasible and optimal transition trajectories are computed featuring minimum deviation from target values. The optimization approach was applied to three nonlinear reaction systems. The results demonstrate that when the variable weighting optimization scenario approach is suitable for approximating model uncertainty, feasible transition trajectories can be calculated at relatively low computational cost (for small or medium scale systems).

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“…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%

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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A robust nonlinear model predictive controller for a multiscale thin film deposition process

Cited by 40 publications

References 38 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

A multi‐scenario nonlinear model predictive control approach for robust product transitions

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

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