Control and Optimization of Multiscale Process Systems

Christofides, Panagiotis D.; Armaou, Antonios; Lou, Yifei; Varshney, Amit

doi:10.1007/978-0-8176-4793-3

Cited by 40 publications

(61 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10,11 Different domains can be described through a multiscale algorithm that utilizes a lattice-based kinetic Monte Carlo (KMC) model to capture the surface behavior coupled with a continuum model to describe the behavior of the fluid phase. This framework has been used extensively to model a wide range of multiscale process systems such as thin film growth, 12,13 crystallization, 14,15 copper electrodeposition, 16,17 and spatially homogeneous catalytic reactors. [18][19][20] This approach accounts for lateral interactions and other surface nonuniformities in catalytic reactors through the lattice-based KMC model.…”

Section: Introductionmentioning

confidence: 99%

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

2016

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2016

View full text Add to dashboard Cite

show abstract

“…Vlachos, 1997;Lam and Vlachos, 2001;Christofides et al, 2009). As shown in Figure 2, a fluid (Gas phase) contains a precursor that diffuses to the surface of the catalyst.…”

Section: Illustrative Multiscale Modelling Examplementioning

confidence: 99%

“…Therefore, experiments may be repeated many times to obtain products with similar (desirable) characteristics. While this trialand-error approach is often used to design materials and devices at the nanoscopic and microscopic level, a more systematic approach can be developed where the manipulation of the events occurring at the fine scales can be explicitly treated to improve the design and control of macroscopic processes (Maroudas, 2000;Kevrekidis et al, 2004;Vlachos, 2005;Braatz et al, 2006a;Ferreira and Lee, 2007;Adalsteinsson et al, 2008;Christofides et al, 2009;Ramesh, 2009;Yang and Marquardt, 2009).…”

Section: Introductionmentioning

confidence: 99%

Current challenges in the design and control of multiscale systems

Ricardez‐Sandoval

2011

Can J Chem Eng

View full text Add to dashboard Cite

Multiscale modelling is a new emerging field in process systems engineering. Although the idea of linking events occurring across time and length scales is not new, the numerical solution of these models is challenging because of computational limitations and the difficulty in coupling modelling methods with different characteristics. Although an extensive set of tools are currently available to improve the performance of processes described using continuum models, most of these tools are not suitable to design and control a multiscale process. This work presents the approaches that are currently available to perform multiscale modelling and identifies the key challenges that need to be addressed to improve the performance of macroscopic processes by controlling events occurring at the atomistic, molecular and nanoscopic levels.

show abstract

“…where ( ) [25,26]. Note that some mesoscopic models can have a closed form when describing certain finer-scale phenomena (e.g., particle flying during paint spray, and heat transfer and solvent diffusion through the film in coating curing).…”

Section: Multiscale Modeling For Paint Spray and Film Curingmentioning

confidence: 99%