A Parametric MINLP Algorithm for Process Synthesis Problems under Uncertainty Academic Article uri icon

abstract

  • 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 ∈-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.

author list (cited authors)

  • Acevedo, J., & Pistikopoulos, E. N.

citation count

  • 72

publication date

  • January 1996