Enhancing refinery heavy oil fractions analytical performance through real-time predicative modeling.

Abstract

This study introduces a suite of robust models aimed to advance the determination of physiochemical properties in heavy oil refinery fractions. By integrating real-time analytical technique inside the refinery analysis, we have developed a single analyzer capable of employing six partial least square regression equations. These designed models enable to provide real-time prediction of critical petroleum properties, such as sulfur content, micro carbon residues (MCR), asphaltene content, heating value, and the concentrations of nickel and vanadium metals. Specifically tailored for heavy oil in refinery feeds with an American petroleum institute (API) gravity range of 3° to 32° and sulfur content of 2.8 to 5.5 wt%, the models streamline the analysis process within refinery operations, bridging the gap between catalytic and non-catalytic processes across refinery units. The accuracy of our physiochemical prediction models has been validated against American Society for Testing and Materials (ASTM) standards, demonstrating their capability to deliver precise real-time property values. This approach not only enhances the efficiency of refinery analysis but also sets a new standard for the monitoring and optimization of heavy oil processing in real-time approach.