Construction of Molecular Model-Driven Hybrid Model for Atmospheric Distillation Separation of Crude Oil and Dynamic Prediction Study of Product Composition Distribution

Abstract

This study proposes a dynamic hybrid modeling approach to predict the time-dependent molecular composition distribution of separated products in atmospheric crude oil distillation processes. A benchmark model system incorporating 152 real molecular species is developed. By integrating a dynamic first-principles model for the Pre-Flash column, first-principles models for the atmospheric column’s top and bottom sections, and a surrogate model for the main column, a dynamic hybrid simulation framework for atmospheric distillation is established. Validation under steady-state conditions demonstrates strong agreement between the hybrid model and Aspen Plus simulations, with absolute errors for all product components remaining below ±5%. The model achieves high predictive accuracy for molecular mole fractions and vapor-liquid equilibrium constants. Under dynamic operational scenarios, the model exhibits robust stability, maintaining product composition errors within ±5%. Over 90% of molecular content predictions show absolute errors between -0.5% and 1.0%. Predicted vapor-liquid equilibrium constants align closely with reference values, as evidenced by data points distributed uniformly along the parity line. Experimental results confirm the hybrid model’s capability to capture dynamic characteristics of crude oil distillation, demonstrating both high accuracy in molecular-level product distribution predictions and practical engineering applicability.

Keywords: Molecular model, Atmospheric distillation, Hybrid model, Dynamic prediction, Process simulation