An Ergun-based model for estimating pressure drop buildup from fines deposition during hydrotreating of bitumen-derived gas oil
Year: 2026
Type: article
Abstract: Hydrotreating of bitumen-derived gas oils is essential for producing clean fuels, but the process is challenged by fines deposition, which leads to progressive pressure drop buildup and premature reactor shutdown. Pressure drop correlations can serve as valuable tools for anticipating such buildups. However, existing correlations, including the Ergun equation and its two-phase extensions, do not account for the hydrodynamic effects of fines deposition, thereby limiting their applicability. In this study, an Ergun-based model was developed to predict pressure drop during fines deposition. The model integrates a two-phase multiplier with a time-dependent fines deposition multiplier, both expressed as functions of operating conditions and packing properties. Model coefficients were estimated via non-linear regression using experimental data spanning temperatures of 350-390 °C, gas velocities of 0.03-0.10 m/s, packing sizes of 2.1-2.7 mm and fines sizes of 0.2-20 µm. The integrated model demonstrated strong predictive capability across the examined range of conditions. Sensitivity analysis revealed that fines size had the strongest individual influence on pressure drop buildup, whereas gas velocity and temperature dominated when interaction effects were considered. The resulting framework provides a predictive tool for estimating pressure drop during fines deposition and offers valuable insights for mitigation strategies when processing fines-laden oil feeds.
Institution: University of Saskatchewan
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W7204879505