A trust region framework for heat exchanger network synthesis with detailed individual heat exchanger designs

Kazi, Saif R.; Short, Michael; Biegler, Lorenz T.

doi:10.1016/j.compchemeng.2021.107447

Cited by 12 publications

(17 citation statements)

References 52 publications

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“…Due to that, stochastic algorithms, such as simulated annealing, 31 genetic algorithms, 32 and particle swarm optimization, 33 are frequently used to generate a good solution within affordable time for large‐scale strongly nonconvex MINLP problems. However, the stochastic algorithms lead to long computational times and no guarantee of optimality 34 . Another class of methods are to relax an MINLP problem as an NLP problem and add some special constraints to force the integrality of the relaxed integer variables gradually 35 .…”

Section: Introductionmentioning

confidence: 99%

“…For example, a trust region filter (TRF) method has been proposed 56 and refined 57 for optimization of chemical processes with high-fidelity models, and for heat exchanger network synthesis with detailed exchanger design. 34,55 However, the convergence of the algorithm may largely rely on the robust convergence and accurate derivative information of the simulation using the original model, which may be unavailable or unreliable in some cases. In addition, which parts of the high-fidelity model are substituted with what kinds of surrogate models might need expert knowledge to achieve good convergence and high efficiency.…”

mentioning

confidence: 99%

“…However, the stochastic algorithms lead to long computational times and no guarantee of optimality. 34 Another class of methods are to relax an MINLP problem as an NLP problem and add some special constraints to force the integrality of the relaxed integer variables gradually. 35 Such method has been applied to optimization of distillation columns 36 and synthesis of reaction-separation-recycle processes.…”

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confidence: 99%

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Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

2022

AIChE Journal

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Large‐scale strongly nonlinear and nonconvex mixed‐integer nonlinear programming (MINLP) models frequently appear in optimization‐based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB‐FP and HCBB‐RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB‐RB is superior to HCBB‐FP due to much lower computational effort required for the same locally optimal solution.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

2022

AIChE Journal

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“…16 Recently, heat exchanger models associated with numerical discretization procedures have also been used for the design optimization 17 and heat exchanger network synthesis. 18,19 The limitations exposed above of the closed-form analytical solutions hinder the utilization of design optimization tools for engineering practice. 20 Therefore, this paper discusses the optimization of heat exchangers based on models where the physical properties and the heat transfer coefficients vary along the heat transfer surface.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, several papers addressed the simulation of heat exchangers through discretization techniques or even computational fluid dynamics . Recently, heat exchanger models associated with numerical discretization procedures have also been used for the design optimization and heat exchanger network synthesis. , …”

Section: Introductionmentioning

confidence: 99%

Design Optimization of Double-Pipe Heat Exchangers Using a Discretized Model

Nahes

Bagajewicz

Costa

2021

Ind. Eng. Chem. Res.

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The design optimization of heat exchangers is a topic extensively investigated in the literature. The majority of the papers that addressed this problem employed closed-form analytical solutions to describe the behavior of the equipment, such as the logarithmic mean temperature difference (LMTD) and effectiveness (ε-NTU) methods. These analytical solutions are based on the hypothesis of uniform values of the physical properties and heat transfer coefficients. This assumption may imply considerable errors in several situations. Aiming at eliminating these limitations, we present a novel integer linear model for the optimal design of hairpin double-pipe heat exchangers. Our novel method discretizes the temperature field inside the exchangers and, together with appropriate rigorous reformulations, renders a linear model. Numerical results illustrate the performance of the proposed approach, showing that the analytical solutions can significantly undersize or oversize the heat exchanger.

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Globally optimal simultaneous heat exchanger network synthesis and basic heat exchanger design

Oliva,

Nahes,

Lemos

et al. 2024

AIChE Journal

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In this article, we extend a previously developed globally optimal enumeration methodology for the synthesis of heat exchanger networks (HENs) to include the basic design of heat exchangers (HEXs). The method addresses together all trade‐offs between network structure, energy usage, and the basic design of the HEXs. Without loss of generality, we focus on shell‐and‐tube HEXs. Unlike previous approaches, such as Pinch Analysis, Metaheuristic methods, or Mathematical Programming, our procedure guarantees global optimality. The procedure is not iterative and does not present any convergence challenges. We enumerate HEN structures using a mixed‐integer linear programming method and we use Set Trimming followed by sorting for the HEX design. In addition, because some network structures are incompatible with single shell exchangers, we use multiple shell exchangers in series. The comparison of the results of the proposed approach with solutions obtained using two alternative methods extracted from the literature indicates that considerable cost reductions may be obtained.

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A trust region framework for heat exchanger network synthesis with detailed individual heat exchanger designs

Cited by 12 publications

References 52 publications

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

Design Optimization of Double-Pipe Heat Exchangers Using a Discretized Model

Globally optimal simultaneous heat exchanger network synthesis and basic heat exchanger design

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