Abstract: In view of the product quality was difficult to be predicted in diesel hydrofining process, the artificial neural network model was proposed. Based on the production operation data of 1.0 Mt/a diesel hydrofining unit in a petrochemical enterprise, the model for predicting sulfur content of diesel hydrogenation products were established by using momentum BP neural network, LMBP neural network and RBF neural network. The generalization ability of the RBF neural network model was also investigated. The results showed that, the average relative errors of the prediction of momentum BP neural network, LMBP neural network and RBF neural network are 3.50%, 2.30% and 2.18%, respectively. The RBF neural network model has the best prediction performance and good generalization ability. The RBF neural network could predict the sulfur content of the diesel product accurately when the process parameters changes. The work provides guidance for the better operation of the diesel hydrofining unit.

Key words: diesel, hydrofining, artificial neural network