A parametric MINLP algorithm for process synthesis problems under uncertainty

In this paper, we describe an alogirthm for the parametric solution of MINLP models in the context of process synthesis problems under uncertainty. The procedure, based on the outerapproximation/equation relaxation algorithm, involves the iterative solution of NLP subproblems and a parametric MILP master problem, with which an E-approximate parametric solution profile can be obtained which corresponds to the set of optimal structures/designs as a function of a scalar uncertain parameter varying within a closed range. Three example problems are presented in detail to illustrate the steps of the proposed algorithm; its applicability to address general process synthesis problems under uncertainty is also briefly discussed.