A robust model for prediction of aromatic content in fluid catalytic cracking feedstocks

The aromatic content of fluid catalytic cracking (FCC) feedstocks significantly impacts their crackability and product yields, yet direct measurement is often limited by sophisticated instrumentation. The present work fills this critical gap by developing a robust empirical model for predicting aromatic content using readily available parameters like specific gravity, mean average boiling point, refractive index, and carbon-to-hydrogen mass ratio. Employing Design Expert v13 software and a dataset comprising over 140 diverse feedstocks, the model identified specific gravity and refractive index as key predictors of aromatic content. Validation with 27 independent samples demonstrated high accuracy, with an average absolute deviation of 1.59 wt%, outperforming established correlations. Supporting experimental evaluations using an ACEMAT reactor revealed that elevated aromatic content reduces feed crackability and light olefin yields, underscoring the operational importance of accurate aromatic quantification. This model offers a practical and accessible tool for refinery applications, enabling quick assessment of FCC feed quality without reliance on complex analytical equipment. The findings advance the field by providing an effective approach for feedstock evaluation, aiding optimization of FCC operations toward enhanced petrochemical production.