文献类型：期刊文献

英文题名：Modeling of ammonia conversion rate in ammonia synthesis based on a hybrid algorithm and least squares support vector regression

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：2012

卷号：7

期号：1

起止页码：150

外文期刊名：ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20120814781779);WOS:【SCI-EXPANDED(收录号:WOS:000299998200017)】；

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 High Technology Research and Development Program of China (863 Program) (Grant No. 2009AA04Z141), National Natural Science Foundation of China (Grant No. 60774078), Shanghai Commission of Science and Technology (Grant No. 08JC1408200), and Shanghai Leading Academic Discipline Project (Grant No. B504).

语种：英文

外文关键词：ammonia synthesis; ammonia conversion rate; particle swarm optimization; differential evolution; least squares support vector regression; operational parameter

摘要：In ammonia synthesis production, the ammonia conversion rate reflects how well the synthesis proceeds. In this paper, a model, which characterizes the relationship between operational variables and ammonia conversion rate, is established using least squares support vector regression (LSSVR). A hybrid algorithm of particle swarm optimization and differential evolution (HPSODE) is proposed to identify the hyperparameters of LSSVR, i.e. the regulation parameter and the width of the kernel function. HPSODE is first tested through benchmark functions and the performance is evaluated with traditional particle swarm optimization (PSO), differential evolution (DE), and a hybrid particle swarm optimization with differential evolution operator (DEPSO). It is then applied to the modeling of ammonia synthesis process. Results using other modeling methods [back propagation neural network (BPNN), LSSVR, PSOLSSVR, and DELSSVR] are presented for comparison purpose. The proposed HPSODELSSVR modeling shows good feasibility of the algorithm and reliability of global convergence. Copyright (C) 2010 Curtin University of Technology and John Wiley & Sons, Ltd.