Optimal design of chemical processes with chance constraints

Ostrovsky, G. M.; Ziyatdinov, N. N.; Lapteva, T. V.

doi:10.1016/j.compchemeng.2013.05.029

Cited by 20 publications

(10 citation statements)

References 26 publications

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“…Subsequently, once the uncertain parameters have been realized, further design or operational policy improvements can be made by selecting, at a certain cost, the values of the second-stage variables. Due to uncertainty, the second-stage cost is a random variable (Biegler et al, 1997;Ostrovsky et al, 2013). The objective is to choose the first-stage variables in a way that the addition of the firststage costs and the expected value of the random second-stage costs are minimized (Sahinidis, 2004).…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

Robust optimization of a post-combustion CO2 capture absorber column under process uncertainty

Cerrillo-Briones

Ricardez‐Sandoval

2019

Chemical Engineering Research and Design

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Section: Optimization Under Uncertaintymentioning

confidence: 99%

Robust optimization of a post-combustion CO2 capture absorber column under process uncertainty

Cerrillo-Briones

Ricardez‐Sandoval

2019

Chemical Engineering Research and Design

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“…Soft inequality constraints do not have to be strictly satisfied, but in a probabilistic manner. Inequality constraints h nq (x(t), u(t), p) ≤ 0 formulated on the probability space are also named chance constraints [44] and read as:…”

Section: Robust Formulation Of Soft Inequality Constraintsmentioning

confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

2018

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Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

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“…Indeed, the chemical engineering community has proposed numerous optimization approaches that explicitly account for uncertainty in problem parameters. These include design flexibility approaches, − stochastic programming approaches, − chance-constrained optimization approaches, − and, more recently, robust optimization approaches. − …”

Section: Introductionmentioning

confidence: 99%

“…In the work of Li et al, the joint chance constraints are handled by relaxing the problem into a nonlinear program, and the major challenge is the calculation of the probability and its derivatives to satisfy inequality constraints. Two-stage optimization problems with chance constraints and Gaussian-distributed uncertainties for chemical process design are also considered. , More recently, Zymler et al derived a tractable formulation for CCP by assuming that only first- and second-order moments are given. Yang and Xu further considered nonlinear constraints in CCP.…”

Section: Introductionmentioning

confidence: 99%

Chance-Constrained Optimization for Refinery Blend Planning under Uncertainty

Yang

Vayanos

Barton

2017

Ind. Eng. Chem. Res.

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A near-global optimization approach is proposed to design blending recipes for refinery products under uncertainties. In the refining industry, the most valuable products, such as gasoline and diesel, are produced by blending several intermediate feedstocks to maximize profit and ensure that all qualities are on specification. Because of the presence of property uncertainties, optimal blending recipes under linear mixing laws should be designed by solving a linear program with joint chance constraints. However, joint chance-constrained programming is generally intractable even with Gaussian distributions, and thereby, it is usually converted to an individual chance-constrained (ICC) program to achieve a conservative approximation. To reduce this conservatism, we find a global optimal solution for the ICC program. In case studies, a multiproduct blending problem with 12 chance constraints and a crude oil procurement example with 14 chance constraints are studied to test and compare the proposed scheme with state-of-the-art optimization software to demonstrate its superior performance in terms of computational time.

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Optimal design of chemical processes with chance constraints

Cited by 20 publications

References 26 publications

Robust optimization of a post-combustion CO2 capture absorber column under process uncertainty

Robust optimization of a post-combustion CO2 capture absorber column under process uncertainty

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Chance-Constrained Optimization for Refinery Blend Planning under Uncertainty

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