Prediction of Kinematic Viscosity for Lube Base Oils using Feedstock Properties and Reactor Operating Parameters

In the refinery industry, producing Group III Base Oils requires stringent control over viscosity index (VI) and kinematic viscosity (KV) as key measures of product quality. The traditional process of sampling and experimentally determining viscosity is highly time-consuming, often taking up to one full shift. This extended duration can lead to product waste and suboptimal yield, posing significant challenges for efficient production. To address these issues, a regression model using the LightGBM algorithm was developed to predict base oils’ VI and KV. By implementing Recursive Feature Elimination (RFE), crucial factors such as feedstock properties and compositions, flow rate, hydrogen flow rate, pressures, and temperatures were identified as significant contributors to the VI and KV of base oils. The model’s performance was impressive, demonstrating mean absolute errors (MAE) of 1.12 for VI, 0.88 for KV at 40°C, and 0.20 for KV at 100°C. On average, the three models achieved an R-squared value of 98% using LightGBM. The model developed in this study holds substantial potential to aid operators in estimating product rundown times accurately while maximizing product yields. By significantly reducing the time and labor associated with traditional sampling methods, this predictive model enhances operational efficiency and ensures consistent product quality.