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A multi-objective evolutionary optimization framework for a natural gas liquids recovery unit

Abstract:

A simulation-based multi-objective optimization scheme is proposed for determining the optimal operating conditions of a natural gas liquids (NGL) recovery unit. Two objective functions are considered, the annualized profitability of the unit and the concentration of methane in the NGL product stream. Two problem formulations are studied including a deterministic model and a stochastic model which incorporates market uncertainty. The techno-economic framework combines the process simulation package PRO/II and a Python environment, in which the simulation status is tracked through the optimization. An evolutionary optimization algorithm simultaneously optimizes eight decision variables for constructing a 2-D Pareto front. Results provide insightful guidance on determining the most adequate conditions of a gas subcooled process (GSP) unit and portray an operational back-off which aims to reduce the impact introduced by market uncertainties.