A Real-time Prediction System for Molecular-level Information of Heavy Oil Based on Machine Learning

Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the "selection of the optimal processing route" strategy. With the development of data prediction systems represented by machine learning, it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry. However, the biggest difficulty lies in acquiring the data required for training the neural network. To address these issues, this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database. Subsequently, a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure. After training, the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery. The validation and test sets exhibit R-2 values of 0.99769 and 0.99807, respectively, and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%. Finally, the SHAP (SHapley Additive ExPlanation) interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.