A trust-region framework for constrained optimization using reduced order modeling

Agarwal, Anshul; Biegler, Lorenz T.

doi:10.1007/s11081-011-9164-0

Cited by 64 publications

(45 citation statements)

References 45 publications

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“…However, gradients of the original and surrogate models could differ. A common approach to solve this difference consists of using scaled functions by using local corrections to the current iteration (Agarwal & Biegler, 2013).…”

Section: Methodsmentioning

confidence: 99%

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%

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

Quirante

2016

Computers & Chemical Engineering

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“…9. h-type step Add ðh k ; f k Þ to the filter and accept trial step x k11 5x k 1s k . Update the trust region using (12) and (13). Increment k by one and go to Step 2.…”

Section: Switching Conditionmentioning

confidence: 99%

“…The method is well-grounded in trust region theory but the authors stop short of proving convergence. Agarwal and Biegler 13 discuss convergence results for constrained subproblems when the derivatives of the black box are known. Biegler et al 6 use a penalty function to solve inequality constrained problems when the derivatives are unavailable and suggest stopping criteria based on the RM errors.…”

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confidence: 99%

A trust region filter method for glass box/black box optimization

Eason

Biegler

2016

AIChE Journal

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Modern nonlinear programming solvers can be utilized to solve very large scale problems in chemical engineering. However, these methods require fully open models with accurate derivatives. In this article, we address the hybrid glass box/black box optimization problem, in which part of a system is modeled with open, equation based models and part is black box. When equation based reduced models are used in place of the black box, NLP solvers may be applied directly but an accurate solution is not guaranteed. In this work, a trust region filter algorithm for glass box/black box optimization is presented. By combining concepts from trust region filter methods and derivative free optimization, the method guarantees convergence to first-order critical points of the original glass box/black box problem. The algorithm is demonstrated on three comprehensive examples in chemical process optimization.

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“…The filter topology determined by scaling and the optimization is summarized in Table I. The method of optimization used is Trust region framework 27 where we optimized at most three parameters at a time from the total number of six optimization parameters (l k (3) and d k (3)). The scaled and optimized values for l k and d k differ less than 6.3% and 16.4% from the corresponding initial values determined by the direct coupling theory, respectively.…”

Section: E-plane Bandpass Filter -Simulations and Measurementsmentioning

confidence: 99%

W-band waveguide bandpass filter with E-plane cut

Furtula

Salewski

2014

Review of Scientific Instruments

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In this paper, we present a design and measurements of a five-section bandpass filter with a passband from 96 to 106 GHz. The insertion loss is less than 1.4 dB in the passband, and the rejection is better than 40 dB in the range from 115 to 142 GHz. We use transmission line coupling theory based on Tchebyscheff's synthesis in order to provide an initial guess for the geometrical parameters of the filter such as cavity lengths and coupling widths. The filter is manufactured from brass in two halves in the E-plane cut topology. The S-parameters of the filter are measured and compared with the simulations. The measured passband insertion loss is approximately 0.4 dB worse than in the simulation, and the measured passband width is approximately 3.4% narrower. The measured filter attenuation roll-off corresponds well to the simulation. We also compare our S-parameter measurements of the E-plane filter with corresponding measurements of a very similar H-plane filter. The transmission and reflection characteristics of the E-plane filter are better than those of the H-plane filter.

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A trust-region framework for constrained optimization using reduced order modeling

Cited by 64 publications

References 45 publications

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

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

A trust region filter method for glass box/black box optimization

W-band waveguide bandpass filter with E-plane cut

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