Design of a combined isomerization-catalytic reforming unit for increasing gasoline octane number using an AI-based quality prediction model

Aruzhan Abdigali, Nuriya Aikenova, Zhansaya Zhakhanshayeva

2026

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DOI 10.54859/kjogi109015
Data confidence 70 medium
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Abstract

Background: The production of high-octane gasoline meeting stringent environmental requirements remains an important task for petroleum-refining industries, including Kazakhstan. At the same time, artificial intelligence and digitalization are increasingly applied to support process design and fuel-quality prediction. Aim: To design a combined isomerization-catalytic reforming system for producing AI-92, AI-95, and AI-98 gasoline grades using an AI-based model for gasoline-quality prediction. Materials and Methods: Three commercial gasoline grades (AI-92, AI-95, and AI-98) were characterized by pycnometric density measurement, capillary viscometry, and thermogravimetric analysis with differential thermal analysis. Linear Regression, k-Nearest Neighbors, and Decision Tree models were used to predict research octane number (RON). Kinetic models were developed for light-naphtha isomerization and catalytic reforming, together with material and energy balance calculations, DIP-column design, and reformate-yield estimation. Results: The Decision Tree model provided exact RON predictions for the three tested grades (MAE = 0.00; R² = 1.00). The isomerization unit with DIP recycle produced an isomerate with RON 87.4, while the catalytic reforming unit produced reformate with RON 98.5 at 500°C and a liquid yield of 85.4 vol%. The calculated blended RON values were 92.1, 95.3, and 98.2 for AI-92, AI-95, and AI-98, respectively. Conclusions: The combined process configuration enables the production of the full range of studied gasoline grades by adjusting the proportions of isomerate and reformate in the blending pool. The AI-based quality model can be used as a supporting tool for defining target octane specifications during process design.