Application of data-driven modeling approaches to industrial hydroprocessing units

Abstract Hydroprocessing units in petroleum refineries comprise of several complex interconnected network of unit operations, and perform the function of removing impurities from the crude, and cracking it to lighter products for subsequent operations. Modeling these units play a pivotal role in predicting future values of important variables, improving the control and optimization of the plant for efficient operation among several other applications. This paper presents the development and implementation of data-based models in estimating product qualities and other key monitoring variables in the hydroprocessing unit of an industrial refinery. Real industrial data from two different units was used and appropriate data-driven modeling strategies were formulated in order to address this problem. In one instance, the usefulness of Dynamic-Partial Least Squares (DPLS) over Partial Least Squares (PLS) in the estimation of important variables of the unit is demonstrated. In the other instance, subspace identification methodology is found to yield a superior model. The methods used in this study can also elegantly handle the missing data problem associated with real data sets, and thus demonstrate the ability, and the importance of using the right data driven technique for specific problems in the context of hydroprocessing refinery.