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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Vydáno v:Computers & chemical engineering Ročník 154; s. 107473
Hlavní autoři: Pedrozo, H.A., Rodriguez Reartes, S.B., Bernal, D.E., Vecchietti, A.R., Diaz, M.S., Grossmann, I.E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2021
Témata:
GDP
Hybrid models
Logic-based Outer Approximation algorithm
propylene production
State equipment network
Superstructure optimization
GDP
Superstructure optimization
propylene production
Hybrid models
Logic-based Outer Approximation algorithm
State equipment network
ISSN:0098-1354, 1873-4375
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Popis
Shrnutí:•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 %.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2021.107473