Hybrid model generation for superstructure optimization with Generalized Disjunctive Programming
•Novel iterative procedure to generate hybrid models within an optimization framework to solve design problems.•Hybrid models based on first principle and surrogate models (SMs) and represent potential plant process units embedded within a superstructure representation•Iterative procedure: generatio...
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| Vydané v: | Computers & chemical engineering Ročník 154; s. 107473 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.11.2021
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| ISSN: | 0098-1354, 1873-4375 |
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| Abstract | •Novel iterative procedure to generate hybrid models within an optimization framework to solve design problems.•Hybrid models based on first principle and surrogate models (SMs) and represent potential plant process units embedded within a superstructure representation•Iterative procedure: generation of initial SMs with simple algebraic regression models and refinement with adding Gaussian Radial Basis Functions•Three-step refinement: initial SM refinement, domain exploration, and, after solution of the optimal design problem, further exploitation of the domain region•The superstructure optimization problem modeled as a Generalized Disjunctive Programming problem and solved with the Logic-based Outer Approximation algorithm.•Two case studies: methanol synthesis and propylene production plant design via olefin metathesis.•Compared to the optimal design determined with rigorous models, the proposed hybrid models give the same optimal configuration and objective functions with relative differences less than 1.1 %.
We propose a novel iterative procedure to generate hybrid models (HMs) within an optimization framework to solve design problems. HMs are based on first principle and surrogate models (SMs) and they may represent potential plant units embedded within a superstructure. We generate initial SMs with simple algebraic regression models and refine them by adding Gaussian Radial Basis Functions in three steps: initial SM refinement, domain exploration, and, after solving the optimal design problem, further domain exploitation, until the convergence criterion is fulfilled. The superstructure optimization problem is formulated with Generalized Disjunctive Programming and solved with the Logic-based Outer Approximation algorithm. We addressed methanol synthesis and propylene plant design problems. Compared to rigorous model-based optimal design, the proposed HMs gave the same configuration, objective function and decision variables with maximum relative differences of 1 and 7 %, respectively. A sensitivity analysis shows that the proposed strategy reduced CPU time by 33 %. |
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| AbstractList | •Novel iterative procedure to generate hybrid models within an optimization framework to solve design problems.•Hybrid models based on first principle and surrogate models (SMs) and represent potential plant process units embedded within a superstructure representation•Iterative procedure: generation of initial SMs with simple algebraic regression models and refinement with adding Gaussian Radial Basis Functions•Three-step refinement: initial SM refinement, domain exploration, and, after solution of the optimal design problem, further exploitation of the domain region•The superstructure optimization problem modeled as a Generalized Disjunctive Programming problem and solved with the Logic-based Outer Approximation algorithm.•Two case studies: methanol synthesis and propylene production plant design via olefin metathesis.•Compared to the optimal design determined with rigorous models, the proposed hybrid models give the same optimal configuration and objective functions with relative differences less than 1.1 %.
We propose a novel iterative procedure to generate hybrid models (HMs) within an optimization framework to solve design problems. HMs are based on first principle and surrogate models (SMs) and they may represent potential plant units embedded within a superstructure. We generate initial SMs with simple algebraic regression models and refine them by adding Gaussian Radial Basis Functions in three steps: initial SM refinement, domain exploration, and, after solving the optimal design problem, further domain exploitation, until the convergence criterion is fulfilled. The superstructure optimization problem is formulated with Generalized Disjunctive Programming and solved with the Logic-based Outer Approximation algorithm. We addressed methanol synthesis and propylene plant design problems. Compared to rigorous model-based optimal design, the proposed HMs gave the same configuration, objective function and decision variables with maximum relative differences of 1 and 7 %, respectively. A sensitivity analysis shows that the proposed strategy reduced CPU time by 33 %. |
| ArticleNumber | 107473 |
| Author | Rodriguez Reartes, S.B. Grossmann, I.E. Vecchietti, A.R. Pedrozo, H.A. Diaz, M.S. Bernal, D.E. |
| Author_xml | – sequence: 1 givenname: H.A. surname: Pedrozo fullname: Pedrozo, H.A. organization: Planta Piloto de Ingeniería Química (PLAPIQUI CONICET-UNS), Camino La Carrindanga km. 7, Bahía Blanca, Argentina – sequence: 2 givenname: S.B. surname: Rodriguez Reartes fullname: Rodriguez Reartes, S.B. organization: Planta Piloto de Ingeniería Química (PLAPIQUI CONICET-UNS), Camino La Carrindanga km. 7, Bahía Blanca, Argentina – sequence: 3 givenname: D.E. surname: Bernal fullname: Bernal, D.E. organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA – sequence: 4 givenname: A.R. surname: Vecchietti fullname: Vecchietti, A.R. organization: INGAR – Instituto de Desarrollo y Diseño (CONICET-UTN), Avellaneda 3657, Santa Fe, Argentina – sequence: 5 givenname: M.S. surname: Diaz fullname: Diaz, M.S. email: sdiaz@plapiqui.edu.ar organization: Planta Piloto de Ingeniería Química (PLAPIQUI CONICET-UNS), Camino La Carrindanga km. 7, Bahía Blanca, Argentina – sequence: 6 givenname: I.E. surname: Grossmann fullname: Grossmann, I.E. organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA |
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| Cites_doi | 10.1007/s11081-019-09438-1 10.1002/aic.14418 10.1016/0098-1354(95)00219-7 10.1002/aic.12341 10.1016/j.compchemeng.2020.107015 10.1007/s00158-016-1569-0 10.1016/j.compchemeng.2021.107295 10.1016/j.compchemeng.2016.02.013 10.1016/j.compchemeng.2017.02.010 10.1016/j.rser.2016.02.021 10.1016/j.compchemeng.2017.09.017 10.1016/S0098-1354(98)00293-2 10.1016/S1570-7946(10)28189-0 10.1007/s11590-016-1028-2 10.1016/j.compchemeng.2020.106808 10.1016/j.compstruc.2005.02.025 10.3390/pr7110839 10.1016/j.apm.2006.08.008 10.1016/j.compchemeng.2017.12.011 10.1016/S0098-1354(99)00279-3 10.1002/aic.11579 10.1007/s10898-012-9951-y 10.1016/j.compchemeng.2020.106847 10.1016/S0098-1354(00)00582-2 10.1021/ie00023a069 10.1016/j.compchemeng.2014.05.013 10.1007/978-3-642-39572-7_2 |
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| Keywords | GDP Superstructure optimization propylene production Hybrid models Logic-based Outer Approximation algorithm State equipment network |
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| SubjectTerms | GDP Hybrid models Logic-based Outer Approximation algorithm propylene production State equipment network Superstructure optimization |
| Title | Hybrid model generation for superstructure optimization with Generalized Disjunctive Programming |
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