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

ISSN号：1352-2310

卷、期、页：v 230,

发表日期：2020-06-01

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：ATMOSPHERIC ENVIRONMENT

参与作者：张儒,李俊明

通讯作者：辛志诚

第一作者：王吉

论文类型：期刊论文

论文概要：王吉,张儒,李俊明,辛志诚，Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm，ATMOSPHERIC ENVIRONMENT，2020，v 230,

论文题目：Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm