Superstructure-based optimization of membrane network systems for multicomponent liquid mixture separation
The design of membrane networks to recover chemical components from liquid mixtures is intricate due to the large number of potential network configurations. Additionally, nonidealities present in liquid mixtures pose difficulties in describing membrane permeators. In this work, we present a framewo...
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| Vydané v: | Journal of membrane science Ročník 717; číslo C; s. 123574 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.02.2025
Elsevier |
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| ISSN: | 0376-7388 |
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| Abstract | The design of membrane networks to recover chemical components from liquid mixtures is intricate due to the large number of potential network configurations. Additionally, nonidealities present in liquid mixtures pose difficulties in describing membrane permeators. In this work, we present a framework for membrane network synthesis to recover valuable components from liquid mixtures using mixed-integer nonlinear programming (MINLP). First, to model membrane permeation, we develop a physics-based nonlinear surrogate model for crossflow membranes. Second, we construct a richly connected superstructure to account for all potential membrane network configurations. Third, we demonstrate how the two aforementioned elements can be integrated into a MINLP model to obtain the optimal membrane network configuration. Finally, through various applications, we illustrate how the proposed approach can achieve globally optimal solutions.
[Display omitted]
•Developed surrogate membrane model for multicomponent liquid mixture separation.•Generated rich-superstructure embedding various membrane network configurations.•Proposed novel MINLP model to obtain globally optimal membrane network.•Applied approach to demonstrate applications in liquid mixture separation problems. |
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| AbstractList | The design of membrane networks to recover chemical components from liquid mixtures is intricate due to the large number of potential network configurations. Additionally, nonidealities present in liquid mixtures pose difficulties in describing membrane permeators. In this work, we present a framework for membrane network synthesis to recover valuable components from liquid mixtures using mixed-integer nonlinear programming (MINLP). First, to model membrane permeation, we develop a physics-based nonlinear surrogate model for crossflow membranes. Second, we construct a richly connected superstructure to account for all potential membrane network configurations. Third, we demonstrate how the two aforementioned elements can be integrated into a MINLP model to obtain the optimal membrane network configuration. Finally, through various applications, we illustrate how the proposed approach can achieve globally optimal solutions.
[Display omitted]
•Developed surrogate membrane model for multicomponent liquid mixture separation.•Generated rich-superstructure embedding various membrane network configurations.•Proposed novel MINLP model to obtain globally optimal membrane network.•Applied approach to demonstrate applications in liquid mixture separation problems. |
| ArticleNumber | 123574 |
| Author | Verma, Harshit Jassby, David Maravelias, Christos T. |
| Author_xml | – sequence: 1 givenname: Harshit orcidid: 0009-0003-4439-6959 surname: Verma fullname: Verma, Harshit organization: Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08540, United States – sequence: 2 givenname: David surname: Jassby fullname: Jassby, David organization: Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States – sequence: 3 givenname: Christos T. orcidid: 0000-0002-4929-1748 surname: Maravelias fullname: Maravelias, Christos T. email: maravelias@princeton.edu organization: Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08540, United States |
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