A bilevel data-driven framework for robust optimization under uncertainty – applied to fluid catalytic cracking unit

Abstract

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.