Optimal process and control design under uncertainty: A methodology with robust feasibility and stability analyses

Trainor, M.; Giannakeas, Vasily; Kiss, Christine; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.ces.2013.10.017

Cited by 40 publications

(23 citation statements)

References 30 publications

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“…Several approaches has been proposed to address problem (A1) by making simplifying assumptions, e.g., Mohideen et al (1996), Trainor et al (2013), Gerhard et al (2008). One of the key challenges faced is the computational costs associated with the repetitive evaluation of the process constraints and cost function for every set of decision variables' values tested by the optimization algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…While the idea is quite straightforward, there are several challenges and complexities in the mathematical formulation and the corresponding solution strategies of an integration of design and control problem, e.g., the allocation of cost to product variability, description of the disturbances dynamics, control structure selection. Accordingly, several methodologies have been proposed to address this problem, e.g., Sanchez-Sanchez & Ricardez-Sandoval (2013a, 2013b; Gerhard et al (2008); Gutierrez et al (2011); Mohideen et al (1996); Bahakim & Ricardez-Sandoval (2014); Trainor et al (2013); Hamid et al (2010). Reviews on this subject can be found elsewhere (Sakizlis et al, 2004;Ricardez-Sandoval et al, 2009;Yuan et al, 2012).…”

Section: Introductionmentioning

confidence: 98%

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Integration of Design and Control Using Efficient PSE Approximations

et al. 2015

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A new method for the optimal design and control of chemical processes under process disturbances and parameter uncertainty is presented. The key idea is to back-off from the optimal (dynamically inoperable) steady-state design by solving a series of optimization sub-problems constructed using Power Series Expansion (PSE) functions. The PSE functions estimate the actual constraints and cost functions' worst-case variability due to disturbances and parameter uncertainty. The proposed method was applied to address the optimal design of a non-isothermal CSTR. The results show that this method has the potential to address the optimal design of dynamic systems at low computational costs.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Integration of Design and Control Using Efficient PSE Approximations

et al. 2015

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“…In that regard, power series expansions (PSEs) have been used as a basis to capture the behavior of the system for optimal process improvement under uncertainty . Model‐based approaches have also been proposed where the nonlinear behavior of the system is approximated using suitable model structures . Likewise, dynamic high‐fidelity models of the process are represented using approximation and model reduction techniques for simultaneous design and control .…”

Section: Introductionmentioning

confidence: 99%

A trust‐region framework for integration of design and control

Rafiei

Ricardez‐Sandoval

2020

AIChE Journal

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A trust‐region approach is presented to address the simultaneous design and control of large‐scale systems under uncertainty. The key idea is to represent the system using power series expansions (PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE‐based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near‐optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.

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“…Review papers are available which summarize recent works in this subject area. 22,23 Robust solutions are typically more conservative than a nominal solution, but the advantage is that they are able to accommodate process variability. [11][12][13][14][15][16][17][18][19] Uncertainty and process disturbances can have a significant effect on process optimality and feasibility, as model parameters and external perturbations are typically not known a priori with absolute certainty, resulting in variability in process output.…”

Section: Introductionmentioning

confidence: 99%

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

2018

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An algorithm that employs the back‐off method to provide optimal solutions for integration of design, control, and scheduling for multiproduct systems is presented, featuring a flexibility and feasibility analysis. The algorithm employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back‐off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. Through successive iterations, the algorithm converges, terminating on a solution that is robust to a specified level of process variability due to stochastic realizations in the disturbances and uncertain parameters. The proposed algorithm has been successfully applied to a multiproduct continuous stirred tank reactor for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. The present approach has been compared to a critical‐set (multiscenario) method showing the benefits and limitations of both approaches. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2379–2389, 2018

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Optimal process and control design under uncertainty: A methodology with robust feasibility and stability analyses

Cited by 40 publications

References 30 publications

Integration of Design and Control Using Efficient PSE Approximations

Integration of Design and Control Using Efficient PSE Approximations

A trust‐region framework for integration of design and control

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

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