OPTIMAL DESIGN AND HEAT INTEGRATION WITH HYBRID MODELS USING GENERALIZED DISJUNCTIVE PROGRAMMING

PEDROZO, H.A.; RODRIGUEZ REARTES, S.B.; BERNAL, D.E.; VECCHIETTI, A.R.; DIAZ, M.S.; GROSSMANN, I.E.

Boston

Conferencia; 2021 AICHE Annual Meeting; 2021

In this work, we propose a novel optimization framework for chemical process optimal design through first principle-based and surrogate models, i.e., hybrid models, which are embedded within a superstructure representation that is in turn modeled with a GDP formulation, and solved with a custom implementation of the L-bOA algorithm. We build surrogate models using a combination of Simple Algebraic Regression Functions (SARFs) and Gaussian Radial Basis Functions (GRBFs).