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

Eason, John P.; Biegler, Lorenz T.

doi:10.1002/aic.15325

Cited by 62 publications

(42 citation statements)

References 29 publications

(51 reference statements)

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“…The approach guarantees convergence to the target problem with truth models and minimizes the evaluation of these models. The convergence properties are shown by Eason et al 11,12 based on the analysis in the work. 13,14 Moreover, the gradient based optimization strategy is driven by the NLP solver such as IPOPT 15 and CONOPT 16 which solves the trust region subproblem and also has strong algorithmic properties that confirm convergence to a KKT point of the truth models.…”

Section: Introductionmentioning

confidence: 74%

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Demand‐based optimization of a chlorobenzene process with high‐fidelity and surrogate reactor models under trust region strategies

Yoshio

Biegler

2020

AIChE Journal

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This work demonstrates the optimization of the industrial scale chlorobenzene process, which continuously produces multiple products and includes a multiphase reaction with bubble column reactors (BCRs). The trust region filter (TRF) method is applied to carry out the demand‐based optimization of large chlorobenzene process with high‐fidelity BCR models. The TRF method uses surrogate models that substitute the high‐fidelity BCR models in the process model, and avoids the direct implementation of high‐fidelity models, which leads to a large and intractable optimization problem. The surrogate models are constructed based on basis functions that apply first order corrections from the gradients of high‐fidelity models. Different basis functions, CSTR and linear models, are studied in this work. As a result, the usage of CSTR models for the basis function leads to fewer function evaluations of the high‐fidelity model because CSTR model is a reasonable approximation of the high‐fidelity models and an initial guess of the optimization problem. Also, the TRF with surrogate models successfully provides an optimal solution of the high‐fidelity process model with few iterations and function evaluations of the high‐fidelity model itself. From the comparison with a low‐fidelity CSTR model, the solution with the TRF presents more accurate results. The surrogate approaches also make a smooth transition from low‐ to high‐fidelity models in process development. We apply this approach to a demand‐based optimization that integrates nontrivial business options, including optimal shortage of customer demands for profitable operation.

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Section: Introductionmentioning

confidence: 74%

“…For instance, Eason et al 11,12 developed the trust region filter (TRF) method for derivative free optimization that includes black box models. In the TRF framework, the block box models are considered truth models against surrogate models and could be high-fidelity models which are expensive in computation.…”

Section: Introductionmentioning

confidence: 99%

Demand‐based optimization of a chlorobenzene process with high‐fidelity and surrogate reactor models under trust region strategies

Yoshio

Biegler

2020

AIChE Journal

Self Cite

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“…A surrogate model is a good choice when it is relatively easy to generate simulation data as training data from the rigorous model. Although the model accuracy is reduced, it is easier to solve and to obtain the global solution 45,48‐50 . After model substitution, the newly generated optimization problem should be solved and the optimal solution obtained can be denoted as

V_{i}^{*}

.…”

Section: Iterative Model Adoption and Optimization Solution Strategymentioning

confidence: 99%

Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics

Zhang

Zhou

2020

AIChE Journal

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Multiple functional and hard‐to‐quantify sensorial product attributes that can be satisfied by a large number of cosmetic ingredients are required in the design of cosmetics. To overcome this problem, a new optimization‐based approach for expediting cosmetic formulation is presented. It exploits the use of a hierarchy of models in an iterative manner to refine the search for creating the highest‐quality cosmetic product. First, a systematic procedure is proposed for optimization problem formulation, where the cosmetic formulation problem is defined, design variables are specified, and a set of models for sensorial perception and desired product properties are identified. Then, a solution strategy that involves iterative model adoption and two numerical techniques (i.e., generalized disjunctive programming reformulation and model substitution) is applied to improve the efficiency of solving the optimization problem and to find better solutions. The applicability of the proposed procedure and solution strategy is illustrated with a perfume formulation example.

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“…Both of these approaches would benefit from increased attention. Surrogate models have garnered the most recent attention as a way to address this need (242,253). Systematic methods for deriving surrogate models such as ALAMO (254) show great potential for allowing consideration of detailed phenomena at the process synthesis level.…”

Section: Critical Assessmentmentioning

confidence: 99%

Recent Developments and Challenges in Optimization-Based Process Synthesis

Chen

Grossmann

2017

Annu. Rev. Chem. Biomol. Eng.

149

111

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This article first reviews recent developments in process synthesis and discusses some of the major challenges in the theory and practice in this area. Next, the article reviews key concepts in optimization-based conceptual design, namely superstructure representations, multilevel models, optimization methods, and modeling environments. A brief review of the synthesis of major subsystems and flowsheets is presented. Finally, the article closes with a critical assessment and future research challenges for the process synthesis area.

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

Cited by 62 publications

References 29 publications

Demand‐based optimization of a chlorobenzene process with high‐fidelity and surrogate reactor models under trust region strategies

Demand‐based optimization of a chlorobenzene process with high‐fidelity and surrogate reactor models under trust region strategies

Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics

Recent Developments and Challenges in Optimization-Based Process Synthesis

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