Generalized optimization framework for synthesis of thermally coupled distillation columns
In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, an...
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| Veröffentlicht in: | AIChE journal Jg. 71; H. 6 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Hoboken, USA
John Wiley & Sons, Inc
01.06.2025
American Institute of Chemical Engineers |
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| ISSN: | 0001-1541, 1547-5905 |
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| Abstract | In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero‐flow issues. The synthesis problem is formulated as a Mixed Integer Nonlinear Programming problem, which is optimized using our previously developed Feasible Path‐Based Branch and Bound method, coupled with an improved Sequential Quadratic Programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%. |
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| AbstractList | In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero‐flow issues. The synthesis problem is formulated as a Mixed Integer Nonlinear Programming problem, which is optimized using our previously developed Feasible Path‐Based Branch and Bound method, coupled with an improved Sequential Quadratic Programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%. |
| Author | Ma, Yingjie Liu, Chao Li, Jie |
| Author_xml | – sequence: 1 givenname: Chao orcidid: 0000-0002-3251-7819 surname: Liu fullname: Liu, Chao organization: The University of Manchester – sequence: 2 givenname: Yingjie surname: Ma fullname: Ma, Yingjie organization: Massachusetts Institute of Technology – sequence: 3 givenname: Jie surname: Li fullname: Li, Jie email: jie.li-2@manchester.ac.uk organization: The University of Manchester |
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| Snippet | In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented... |
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| SubjectTerms | Algorithms branch and bound Branch and bound methods complex distillation system Computer applications Computing time Distillation Mathematical models Mixed integer mixed‐integer nonlinear programming Nonlinear programming Optimization process synthesis Quadratic programming Superstructures Synthesis thermally coupled distillation |
| Title | Generalized optimization framework for synthesis of thermally coupled distillation columns |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faic.18776 https://www.proquest.com/docview/3228820712 |
| Volume | 71 |
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