文献类型：期刊文献

英文题名：A data-driven soft sensor modeling for furnace temperature of opposed multi-burner gasifier

作者：Li, Jie[1]; Zhong, Weimin[1]; Cheng, Hui[1]; Kong, Xiangdong[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2011

卷号：2

起止页码：705

外文期刊名：Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011

收录：EI(收录号：20114014404320)

语种：英文

外文关键词：Coal combustion - Neural networks - Principal component analysis - Furnaces - High temperature corrosion - Temperature measurement - Temperature

摘要：The Opposed Multi-Burner (OMB) Coal-Water Slurry (CWS) gasification is a new large-scale coal gasification technology with higher product yield, lower oxygen and coal consumption than that of Texaco CWS gasification technology. However, current furnace temperature measurements of OMB and other gaisifiers are unstable and even short-life due to the extreme internal environment: high temperature, strong corrosion, etc. Therefore a new data-driven soft sensor modeling technique for furnace temperature of OMB gasifier is proposed and the selection of secondary variables and model structure of BP neural network is studied in this paper. Results indicate that, the furnace temperature predictive model integrating Principal Component Analysis (PCA) and BP neural network has a promising performance with good predictive precision. ? 2011 IEEE.