A framework to obtain
optimal operating conditions is proposed
for a cryogenic air separation unit case study. The optimization problem
is formulated considering three objective functions, 11 decision variables,
and two constraint setups. Different optimization algorithms simultaneously
evaluate the conflicting objective functions: the annualized cash
flow, the efficiency at the compression stage, and capital expenditures.
The framework follows a modular approach, in which the process simulator
PRO/II and a Python environment are combined. The results permit us
to assess the applicability of the tested algorithms and to determine
optimal operational windows based on the resultant 3-D Pareto fronts.