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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%.

Oxidative desulfurisation is a method of removing sulfur from diesel fuel that has the potential to compete with conventional hydrodesulfurisation processes in refineries.Ultrasound has been shown to greatly increase peroxide oxidation rates of sulfur compounds and can thereby enhance the technology. Through the use of conceptual design modelling, this article critically assesses a range of novel process options. Calculations show that the rate enhancement achieved by ultrasound can translate into reduced process complexity and costs.By modelling various process options, the separation stage of the process is optimised to reveal that a solid adsorbent combined with a combustion regeneration method is the most economically viable. Although the process is limited to feeds with low sulfur content, it is competitive with conventional hydrotreater technology and superior to upgrading an ageing facility.

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