Operation optimization of hydrocracking process based on Kriging surrogate model

Abstract

Abstract Hydrocracking is one of the key technologies in oil refining. It has become a critical secondary processing unit in the refinery for improving the quality of product oil and increasing the light oil volume of production. As such, operation optimization for this process is significant. The basis of operation optimization is the model, and several mechanisms for hydrocracking models have been proposed and studied. However, these models usually require time consuming and exhibit low efficiency especially when applied to optimize operating conditions. In this study, a Kriging surrogate model of hydrocracking is developed based on the mechanism and industrial data. An optimization algorithm is then proposed to optimize operating conditions. The proposed algorithm integrates adaptive step-size global and local search strategy (GLSS) for minimizing the predictor. Simulation results indicate that this optimization strategy integrating GLSS and Kriging surrogate model obtains better revenue of the process production than conventional algorithms such as EGO, DDS, and CAND.