The operational optimization of refining process is facing the complex coupled state and frequently changed conditions. Especially the feed of fluid catalytic cracking (FCC) has property fluctuations which may lead to uncertainties in profit and lead to suboptimal optimization schemes from the deterministic optimization model. This study designed a bilevel data-driven robust optimization framework that optimizes the feed selection and reaction temperature of an industrial FCC unit under feed property uncertainty. Two uncertainty sets based on the feed properties data were derived from 2-year historical industrial data and simulation data. As most of the chemical reaction models are differential equations, a bilevel programming framework designed in Julia was the key point to solve the nested numerical and mathematic problems. A real-world case study is conducted to demonstrate the effectiveness of the proposed approach in protecting against uncertainties to ensure profits for FCC units.

炼油工艺的运行优化面临着复杂的耦合状态和频繁变化的条件。尤其是流化催化裂化（FCC）的原料具有性质波动，可能导致利润的不确定性，并导致确定性优化模型的优化方案次优。本研究设计了一个双层数据驱动的稳健优化框架，在进料特性不确定的情况下优化工业 FCC 装置的进料选择和反应温度。基于进料特性数据的两个不确定性集来自 2 年的历史工业数据和模拟数据。由于大多数化学反应模型都是微分方程，因此在 Julia 中设计的双层编程框架是解决嵌套数值和数学问题的关键。