A parametric MINLP algorithm for process synthesis problems under uncertainty

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.

authors

• Pistikopoulos, Stratos

published proceedings

• INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

author list (cited authors)

• Acevedo, J., & Pistikopoulos, E. N.

citation count

• 87

complete list of authors

• Acevedo, J||Pistikopoulos, EN

publication date

• January 1996

publisher

• American Chemical Society (ACS)  Publisher

published in

• Industrial & Engineering Chemistry Research  Journal

Digital Object Identifier (DOI)

• 10.1021/ie950135r

start page

• 147

end page

• 158

volume

• 35

issue

• 1

URL

• http://dx.doi.org/10.1021/ie950135r