An approximate novel method for the stochastic optimization and MINLP synthesis of chemical processes under uncertainty

Abstract

Abstract In this work we present a novel central basic point (CBP) method for the approximate stochastic optimization and MINLP synthesis of chemical processes under uncertainty. The main feature of this method is that the expected value of the objective function is evaluated by solving a nonlinear program at one central basic point with lower bounds on design variables, while feasibility is ensured by simultaneous solution of the NLP at critical vertices. The central basic point and lower bounds are obtained through a set-up procedure which relies on a one-dimensional calculation of the objective function's conditional expectations for each uncertain parameter. On the basis of this method, a twolevel MINLP strategy for the synthesis of flexible chemical processes is proposed