Air separation systems are crucial in the production of oxygen, which has gained particular relevance during the COVID-19 outbreak. Mechanical ventilation can compensate respiratory deficiencies along with the use of medical oxygen in vulnerable patients infected with this disease. In this contribution, a many-objective simulation-based optimization framework is proposed for determining eleven decision variables for the operation of an air separation unit. The framework combines the capabilities of the process simulator PRO/II with a Python environment. Three objective functions are optimized together towards the construction of a 3-D Pareto front. Results provide insightful information regarding the most adequate operating conditions of the unit, including the definition of an operational window rather than a single operational point.
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.
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