Hybrid model generation for superstructure optimization with Generalized Disjunctive Programming

Hector Alejandro Pedrozo, Sabrina Belen Rodriguez Reartes, David E. Bernal, Aldo R. Vecchietti, María Soledad Díaz, Ignacio E. Grossmann

Abstract: Highlights•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 %.