APPLICATION OF BIG DATA-DRIVEN MODELING PLATFORM TECHNOLOGY IN HYDROCRACKING PROCESS PREDICTION

Abstract

Abstract: With the popularization of sensors and distributed control systems in refining and chemical plants, the analysis and simulation of production using big data based on data-driven modeling technology can guide production optimization, process monitoring and early warning, which has become a hot spot of research and application. Data-driven modeling methods, including the application of multivariate statistical process control (MSPC), machine learning (ML) and deep learning (DL) in the process of refining and chemical process modeling, were summarized. The big data-driven modeling platform built by CNOOC Huizhou Petrochemical Company for refining and petrochemical industry was introduced, and based on the big data-driven modeling platform, the stacked auto-encoders-Gaussian mixture model (SAEs-GMM) algorithm was proposed to predict the jet fuel yield of hydrocracking unit under multi-modes. The results showed that the prediction accuracy of the refinery and petrochemical big data-driven model was excellent, and compared with the traditional data-driven modeling methods, the refinery and petrochemical big data-driven modeling platform realized the code-less modeling workflow, the time of modeling was shortened from the traditional 7 days to about 2 hours, and the efficiency of modeling was greatly improved.

Key words: data-driven modeling, petrochemical big data, modeling platform, hydrocracking, multi-modes

References

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