DOI: 10.1016/j.atmosenv.2014.05.034
Scopus记录号: 2-s2.0-84902188797
论文题名: Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere
作者: Ma D ; , Wang S ; , Zhang Z
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 94 起始页码: 637
结束页码: 646
语种: 英语
英文关键词: Atmosphere pollution
; Gas emission
; Minimum relative entropy
; Optimization
; Source term
Scopus关键词: Algorithms
; Gas emissions
; Identification (control systems)
; Inverse problems
; Iterative methods
; Linear systems
; Optimization
; Particle swarm optimization (PSO)
; Atmosphere pollution
; Experiment verification
; Identification method
; Linear inverse problems
; Lower and upper bounds
; Minimum relative entropy
; Particle swarm optimization method (PSO)
; Source terms
; Parameter estimation
; algorithm
; atmospheric chemistry
; atmospheric pollution
; inverse analysis
; optimization
; parameterization
; simulation
; air pollution control
; air pollution indicator
; algorithm
; article
; gas analysis
; hybrid
; linear system
; measurement error
; minimum relative entropy and particle swarm optimization method
; noise reduction
; nonpoint source pollution
; normal value
; performance
; priority journal
; simulation
; system analysis
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: In order to identify the source term of gas emission in atmosphere, an improved hybrid algorithm combined with the minimum relative entropy (MRE) and particle swarm optimization (PSO) method was presented. Not only are the estimated source parameters obtained, but also the confidence intervals at some probability levels. If only the source strength was required to be determined, the problem can be viewed as a linear inverse problem directly, which can be solved by original MRE method successfully. When both source strength and location are unknown, the common gas dispersion model should be transformed to be a linear system. Although the transformed linear model has some differences from that in original MRE method, satisfied estimation results were still obtained by adding iteratively adaptive adjustment parameters in the MRE-PSO method. The dependence of the MRE-PSO method on prior information such as lower and upper bound, prior expected values and noises were also discussed. The results showed that the confidence intervals and estimated parameters are influenced little by the prior bounds and expected values, but the errors affect the estimation results greatly. The simulation and experiment verification results showed that the MRE-PSO method is able to identify the source parameters with satisfied results. Finally, the error model was probed and then it was added in the MRE-PSO method. The addition of error model improves the performance of the identification method. Therefore, the MRE-PSO method with adjustment parameters proposed in this paper is a potential good method to resolve inverse problem in atmosphere environment. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80877
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China; School of Chemical Engineering and Technology, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China
Recommended Citation:
Ma D,, Wang S,, Zhang Z. Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere[J]. Atmospheric Environment,2014-01-01,94