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.

提出了一种基于仿真的多目标优化方案，用于确定天然气液（NGL）回收装置的最佳运行条件。考虑了两个目标函数，该单元的年化获利能力和NGL产品流中甲烷的浓度。研究了两个问题公式，包括确定性模型和结合了市场不确定性的随机模型。技术经济框架将过程仿真程序包PRO / II与Python环境结合在一起，在该环境中，通过优化来跟踪仿真状态。进化优化算法可同时优化八个决策变量，以构建二维帕累托前沿。