Multi-scale optimization for process systems engineering

Biegler, Lorenz T.; Lang, Yidong; Lin, Weijie

doi:10.1016/j.compchemeng.2013.07.009

Cited by 117 publications

(74 citation statements)

References 53 publications

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“…This point cannot be randomly selected but a pre-specified space filling design must be used. Biegler et al (2014) showed that frequent resampling of the original models can result in prohibitively large computational times. Instead, they proposed exhaustive evaluations of the original models over large trust regions before starting the optimization.…”

Section: Methodsmentioning

confidence: 99%

“…All of them share the main basic steps with different modifications depending on the final objective (local or global optimization), the availability of derivatives and the accuracy of the initial Kriging interpolator. Biegler et al (2014) in the context of multi-scale optimization, proposes three algorithms for using surrogate models with trust regions concept from non-linear programming that guarantee convergence to the optimum of the original problem. Biegler et al (2014) also established the convergence conditions of these algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…Biegler et al (2014) in the context of multi-scale optimization, proposes three algorithms for using surrogate models with trust regions concept from non-linear programming that guarantee convergence to the optimum of the original problem. Biegler et al (2014) also established the convergence conditions of these algorithms. In this paper, we follow an exhaustive sampling to minimize the number of resamplings and Kriging calibration (algorithm 3 in the Biegler's taxonomy).…”

Section: Methodsmentioning

confidence: 99%

“…If the number of degrees of freedom is not too large it is possible to follow the strategy proposed by Biegler et al (2014), and perform exhaustive sampling a priori in order to minimize resampling and recalibration. Table 3 shows all the input-output Kriging models used in this example.…”

Section: Case Study: Sour Water Stripping Plantmentioning

confidence: 99%

“…Also, small trust regions could eventually require resampling and recalibrating the Kriging models. As commented before, in this paper we follow an exhaustive sampling to minimize the number of resamplings and Kriging calibrations (algorithm 3 in the Biegler's taxonomy (Biegler et al, 2014)). In the case in which the model cannot be considered accurate enough, a reduction of the domain is mandatory.…”

Section: Algorithm Implementationmentioning

confidence: 99%

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Large scale optimization of a sour water stripping plant using surrogate models

Quirante

2016

Computers & Chemical Engineering

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Please cite this article as: Quirante, Natalia., & Caballeroa, José A., Large scale optimization of a sour water stripping plant using surrogate models.Computers and Chemical Engineering http://dx.doi.org/10. 1016/j.compchemeng.2016.04.039 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Large scale optimization of a sour water stripping plant using surrogate models We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA).The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%