文献类型：期刊文献

中文题名：Modeling and Optimization of the Steam Turbine Network of an Ethylene Plant

英文题名：Modeling and Optimization of the Steam Turbine Network of an Ethylene Plant

作者：Li Zeqiu[1];Zhao Liang[1];Du Wenli[1];Qian Feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2013

卷号：21

期号：5

起止页码：520

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：CSTPCD;;EI(收录号：20132516431593);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000320294300007)】；CSCD:【CSCD2013_2014】；

基金：Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202), the National Science Fund for Outstanding Young Scholars (61222303) and the National Natural Science Foundation of China (21276078, 61174118, 21206037).

语种：英文

中文关键词：hybrid model;steam turbine network;utility system;economic objective

外文关键词：hybrid model; steam turbine network; utility system; economic objective

摘要：In this paper,we developed a hybrid model for the steam turbines of a utility system,which combines an improved neural network model with the thermodynamic model.Then,a nonlinear programming(NLP) model of the steam turbine network is formulated by utilizing the developed steam turbine models to minimize the total steam cost for the whole steam turbine network.Finally,this model is applied to optimize the steam turbine network of an ethylene plant.The obtained results demonstrate that this hybrid model can accurately estimate and evaluate the performance of steam turbines,and the significant cost savings can be made by optimizing the steam turbine network operation at no capital cost.
In this paper, we developed a hybrid model for the steam turbines of a utility system, which combines an improved neural network model with the thermodynamic model. Then, a nonlinear programming (NLP) model of the steam turbine network is formulated by utilizing the developed steam turbine models to minimize the total steam cost for the whole steam turbine network. Finally, this model is applied to optimize the steam turbine network of an ethylene plant. The obtained results demonstrate that this hybrid model can accurately estimate and evaluate the performance of steam turbines, and the significant cost savings can be made by optimizing the steam turbine network operation at no capital cost.