A modelling and decomposition strategy for the minlp optimization of process flowsheets

Abstract

This paper shows that in structural flowsheet optimization problems that are formulated as mixed-integer nonlinear programming (MINLP) problems, modelling can have a great impact in the quality of solutions that are obtained, as well as on the computational efficiency. A modelling/decomposition strategy is proposed to exploit the special structure of flowsheet synthesis problems that are to be solved with the OA/ER algorithm. The objective of this procedure is to reduce the computational effort required to solve the MINLP optimization problem, and to reduce the effect that nonconvexities can have in cutting-off the global optimum. The modelling strategy eliminates nonconvexities in the interconnection nodes through linear constraints and valid outer-approximations. The decomposition strategy has the important feature of only requiring the NLP optimization of the current candidate flowsheet. Nonexisting units in the superstructure are suboptimized through a Lagrangian decomposition scheme. Application of the proposed modelling/decomposition procedure is illustrated with several examples, including the synthesis of the HDA toluene process.