Logic-Based Outer Approximation for Nonconvex Synthesis of Process Network Problems

AGUIRRE, PIO; LORENA BERGAMINI,; IGNACIO GROSSMANN,

Princeton, New Jersey

Congreso; FOCAPD-2004; 2004

Grossmann, 1994), where a logic-based representation is used to deal with the discrete and continuous decisions. A new deterministic algorithm for the global optimization of process networks is presented in this work. The proposed algorithm is a derivation of the Logic-Based Outer Approximation algorithm (Duran and Grossmann, 1996) that exploits the special structure of flowsheet synthesis models. The method is capable of considering nonconvexities, while guaranteeing globality in the solution of an optimal synthesis of process network problem. This is accomplished by solving iteratively reduced NLP subproblems to global optimality and MILP master problems, which are valid outer approximations of the original problem. Linear under and overestimators for bilinear and concave terms have been constructed with the property of having zero gap in a finite set of points. The global optimization of the reduced NLP may be performed either with a suitable global solver or using the inner optimization strategy that is also proposed in this work. Theoretical properties are discussed as well as several alternatives for implementing the proposed algorithm. Several examples were successfully solved with this algorithm. It requires only few iterations to solve them to global optimality.