文献类型：期刊文献

英文题名：Application of Artificial Neural Network in the Residual Oil Hydrotreatment Process

作者：Ma, C. G.[1];Weng, H. X.[1]

机构：[1]E China Univ Sci & Technol, Res Ctr Petr Proc, Shanghai 200237, Peoples R China

年份：2009

卷号：27

期号：18

起止页码：2075

外文期刊名：PETROLEUM SCIENCE AND TECHNOLOGY

收录：;EI(收录号：20100412652595);WOS:【SCI-EXPANDED(收录号:WOS:000272760600001)】；

语种：英文

外文关键词：artificial neural network; hydrotreatment process residual oil

摘要：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.