文献类型：期刊文献

英文题名：Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved particle swarm optimization and BP neural network

作者：Xu, Wei[1];Zhang, Lingbo[1];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

年份：2011

卷号：89

期号：10A

起止页码：2102

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20113814344454);WOS:【SCI-EXPANDED(收录号:WOS:000295770000022)】；

基金：We are very grateful to the editor and anonymous reviewers for their valuable comments and suggestions to help improve our paper. This work is supported by National Natural Science Foundation of China (Grant no. 60774078), Shanghai Commission of Science and Technology (Grant no. 08JC1408200), Shanghai Leading Academic Discipline Project (Grant no. B504), Doctor Foundation of Ministry of Education of China (Grant no. 200802510010), China Postdoctoral Science Foundation Funded Project (Grant no. 20080430080), and National High Technology Research and Development Program of China (863 Program) (Grant no. 2009AA04Z141).

语种：英文

外文关键词：Soft sensor; Ammonia synthesis; Ammonia concentration; Particle swarm optimization

摘要：Ammonia synthesis production is a critical chemical industry around the world. As the key process variable, the ammonia concentration at the ammonia converter outlet reflects the production status and provides good advices for the operators. However, it cannot be easily measured because of high expenditure and deficient reliability of online sensors in a real-world ammonia synthesis process. Due to this, a soft sensor, which is used to predict the outlet ammonia concentration, is developed using BP neural network (BPNN). An improved particle swarm optimization with expansion and constriction operation (PSOEC) is proposed to optimize the weights and thresholds of BPNN. The PSOEC and BPNN based soft-sensing model (PSOEC-NN) is applied to inferring the outlet ammonia concentration in a fertilizer plant. Results using other modeling methods (BPNN and PSO-NN) are presented for comparison purpose. The proposed PSOEC-NN based soft sensor shows high precision and good generalization capability. PSOEC-NN model would offer great help for further work like advanced control and operational optimization in the ammonia synthesis process. (C) 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.