Optimal design of chemical processes with chance constraints

Ostrovsky, G. M.; Zyatdinov, Nadir; Lapteva, T. V.

doi:10.1016/b978-0-444-59519-5.50120-9

Cited by 6 publications

(21 citation statements)

References 6 publications

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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 [43] and read as:…”

Section: Robust Formulation Of Soft Inequality Constraintsmentioning

confidence: 99%

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

Xie

Schenkendorf

Krewer

2018

Preprint

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Model-based design has received considerable attention in biological and chemical industries over the last two decades. However, the parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies were 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 computation 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. We also suggest employing the information from 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 where one is to design a heating/cooling profile for the essential part of a continuous production process and the other is to optimize the feeding profile for a fed-batch reactor of the penicillin fermentation process. The results reveal that the proposed approach can be used successfully for complex (bio)chemical problems in model-based design.

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Section: Robust Formulation Of Soft Inequality Constraintsmentioning

confidence: 99%

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

Xie

Schenkendorf

Krewer

2018

Preprint

View full text Add to dashboard Cite

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“…One special case of SMOO is deterministic multiobjective optimization (MOO), for which general formulations and computational frameworks are well-established [35,41,22]. SMOO formulations have a a wide range of applications that include finance [2], energy systems [9], chemical processes [43,37,31], transportation logistics [21], facility location [16,14], manufacturing and production planning [42], supply chain management [40], telecommunication, health care management [3,5], budget allocation [23], and project management [20]. SMOO problems present several interesting technical challenges.…”

Section: Motivationmentioning

confidence: 99%

Scalable modeling and solution of stochastic multiobjective optimization problems

Cao

Fuentes-Cortés

Chen

et al. 2017

Computers & Chemical Engineering

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We present a scalable computing framework for the solution stochastic multiobjective optimization problems. The proposed framework uses a nested conditional value-at-risk (nCVaR) objective to find compromise solutions among conflicting random objectives. We prove that the associated nCVaR minimization problem can be cast as a standard stochastic programming problem with expected value (linking) constraints. We also show that these problems can be implemented in a modular and compact manner using PLASMO (a Julia-based structured modeling framework) and can be solved efficiently using PIPS-NLP (a parallel nonlinear solver). We apply the framework to a CHP design study in which we seek to find compromise solutions that trade-off cost, water, and emissions in the face of uncertainty in electricity and water demands.

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“…This is the field of robust optimal experiment design (Goodwin et al, 2007). The approaches presented in the literature, i.e., a worst case approach (Körkel et al, 2004) or an expected value approach (Ostrovsky et al, 2013;Li et al, 2008;Galvanin et al, 2010;Telen et al, 2014;Mesbah…”

Section: The Droop Modelmentioning

confidence: 99%

Multi-purpose economic optimal experiment design applied to model based optimal control

Telen

Houska

Logist

et al. 2016

Computers & Chemical Engineering

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In contrast to classical experiment design methods, often based on alphabetic criteria, economic optimal experiment design assumes that our ultimate goal is to solve an optimization or optimal control problem. As the system parameters of physical models are in practice always estimated from measurements, they cannot be assumed to be exact. Thus, if we solve the model based optimization problem using the estimated, non-exact parameters, an inevitable loss of optimality is faced. The aim of economic optimal experiment design is precisely to plan an experiment in such a way that the expected loss of optimality in the optimization is minimized. This paper analyzes the question how to design economic experiments under the asssumption that we have more than one candidate objective function. Here, we want to take measurements and estimate the parameters before we actually decide which objective we want to minimize.

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Optimal design of chemical processes with chance constraints

Cited by 6 publications

References 6 publications

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

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

Scalable modeling and solution of stochastic multiobjective optimization problems

Multi-purpose economic optimal experiment design applied to model based optimal control

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