气候变化领域集成服务门户(CCPortal): Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm

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DOI	10.1016/j.atmosenv.2020.117515
	Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm
	Wang J.; Zhang R.; Li J.; Xin Z.
发表日期	2020
ISSN	1352-2310
卷号	230
英文摘要	Identifying multi-point hazardous or contaminating gas leak sources is important for emergence treatment and pollution control, whose difficulty, however, may increase if the number of sources is unknown a priori. This study proposed a novel method to estimate the number and locations of several leak sources using Principal Component Analysis (PCA) and a Modified Genetic Algorithm (MGA). PCA works by counting the number of leak sources and providing zones possibly containing each source. MGA is then implemented sequentially to accurately locate each source in those zones. This method was tested in a leak field generated by a steady-state two-dimensional Gaussian plume model with one, two and three leak sources. The effects of concentration sensor array size, leak source location and measuring noise on PCA and MGA performance were analyzed. Using more sensors increases the identification accuracy of PCA but reduces the MGA calculation speed. PCA cannot identify leak sources locating too downstream or having spreading fields with a large overlapping part. The measuring noise generated by Gaussian Noise has little effect on PCA performance, but increases MGA estimation error when identifying source locations. © 2020 Elsevier Ltd
关键词	Genetic algorithm Hazardous gas leak Multi-point release identification Primary component analysis Source inversion
语种	英语
scopus关键词	Acoustic noise measurement; Gaussian noise (electronic); Genetic algorithms; Hazards; Location; Pollution control; Calculation speed; Concentration sensors; Estimation errors; Gaussian plume models; Identification accuracy; Modified genetic algorithms; Number of sources; Source location; Principal component analysis; error correction; genetic algorithm; leakage; performance assessment; pollution control; principal component analysis; Article; gas analysis; genetic algorithm; plume; pollution control; principal component analysis; priority journal; steady state; velocity
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249178
作者单位	Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, 102249, China; Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, China
推荐引用方式 GB/T 7714	Wang J.,Zhang R.,Li J.,et al. Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm[J],2020,230.
APA	Wang J.,Zhang R.,Li J.,&Xin Z..(2020).Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm.ATMOSPHERIC ENVIRONMENT,230.
MLA	Wang J.,et al."Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm".ATMOSPHERIC ENVIRONMENT 230(2020).