The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%.

Polyisobutylene succinic anhydride (PIBSA) is an important intermediate for ashless dispersant additives used in lubricant formulations, where conversion and exo-selectivity strongly influence product performance. In this work, the thermal synthesis of PIBSA derived from highly reactive polyisobutylene (HR-PIB) 1300 and 2300 was systematically investigated using a full factorial experimental design to evaluate the effects of feed molar ratio, reaction temperature, and stirring rate on conversion and exo-PIBSA yield. Structural confirmation was performed using ¹H NMR spectroscopy, gel permeation chromatography (GPC), and saponification value analysis. HR-PIB 1300 achieved conversions of 60–94%, while HR-PIB 2300 achieved conversions of 75–88% under the investigated conditions. A clear molecular-weight-dependent trade-off between conversion and exo-selectivity was observed. For PIBSA 1300, higher temperatures increased conversion but reduced selectivity, resulting in exo-PIBSA yields of 24–56%. In contrast, PIBSA 2300 achieved yields of up to 71%, highlighting the importance of mixing and mass-transfer effects in higher-viscosity systems. These findings demonstrate that optimal thermal maleation conditions are strongly dependent on polymer molecular weight and provide mechanistic insight for future optimization of industrial PIBSA production.