Application of Artificial Neural Network in the Residual Oil Hydrotreatment Process

Based on the industrial measured data of the residual oil hydrotreatment process, the artificial neural network (ANN) model was developed to determine metal, sulfur, nitrogen, and carbon residue content of hydrogenated residual oil. The established ANN model has seven input variables, four output variables, and 1 hidden layer with 15 neurons. The training results show that the agreement between predicted and industrial measured values is good. The mean relative errors of the testing data for the four output variables are less than 6%. It indicated that the developed ANN model has good predictive precision and extrapolative features. The model can provide reference for the further processing of hydrogenated residual oil. This kind of application can be easily developed in any other hydrotreatment process with available adequate historical data.