Membrane fouling diagnosis of membrane components based on multi-feature information fusion

doi:10.1016/j.memsci.2022.120670

	Membrane fouling diagnosis of membrane components based on multi-feature information fusion
	Shi, Yaoke 1,5; Wang, Zhiwen1,2,3 ; Du, Xianjun1,2,3 ; Gong, Bin 1; Lu, Yanrong 1,2,3; Li, Long 1,4
	2022-09-05
发表期刊	JOURNAL OF MEMBRANE SCIENCE
ISSN	0376-7388
卷号	657
摘要	CBAM-MUL-CNN (convolutional block attention module - multiple - convolutional neural networks) model based on attention mechanism is proposed to solve the problem that the membrane fouling feature extraction capability of membrane bioreactor membrane component is insufficient, which resulted in the complex structure of the membrane fouling data, so that the efficient localization and classification of membrane fouling in membrane bioreactor could not be achieved. First, the time domain and frequency domain information about the fault data is used as the input of CNN (convolutional neural networks), and the features are extracted by convolution layer. Then, the input classifier is classified by splicing the time domain and frequency domain features using the full connection layer. BN (batch normalization) layer in the model can effectively prevent the disappearance of gradients, ReLU (rectified linear uint) layer can improve the non-linear model expression ability, CBAM (convolutional block attention module) can simplify the model complexity, improve the network features expression ability, and pooling layer can improve the model fault tolerance. The comparison results show that the model has excellent comprehensive performance in the membrane fouling diagnosis experiments of series tubular membrane devices and parallel hollow fiber membrane devices, and can effectively classify and locate all membrane fouling, making the treatment of water by membrane process improve the quality of effluent while reducing energy consumption, which provides a theoretical basis for actual production.
关键词	MBR CBAM-MUL-CNN Feature fusion Attention mechanism Membrane fouling diagnosis
DOI	10.1016/j.memsci.2022.120670
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Engineering ; Polymer Science
WOS类目	Engineering, Chemical ; Polymer Science
WOS记录号	WOS:000808466700004
出版者	ELSEVIER
EI入藏号	20222312204309
EI主题词	Convolution
EI分类号	445.2 Water Analysis452.3 Industrial Wastes461.8 Biotechnology525.3 Energy Utilization539.1 Metals Corrosion716.1 Information Theory and Signal Processing722 Computer Systems and Equipment723.2 Data Processing and Image Processing802.1 Chemical Plants and Equipment921.3 Mathematical Transformations951 Materials Science
来源库	WOS
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/158872
专题	电气工程与信息工程学院
通讯作者	Shi, Yaoke
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China; 3.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Educ, Lanzhou 730050, Peoples R China; 4.Unis Intelligent Transportat Syst & Control Techno, Lanzhou 730050, Peoples R China; 5.Lanzhou Univ Technol, Coll Elect & Informat Engn, 36 Pengjiaping Rd, Qilihe Dist, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Shi, Yaoke,Wang, Zhiwen,Du, Xianjun,et al. Membrane fouling diagnosis of membrane components based on multi-feature information fusion[J]. JOURNAL OF MEMBRANE SCIENCE,2022,657.
APA	Shi, Yaoke,Wang, Zhiwen,Du, Xianjun,Gong, Bin,Lu, Yanrong,&Li, Long.(2022).Membrane fouling diagnosis of membrane components based on multi-feature information fusion.JOURNAL OF MEMBRANE SCIENCE,657.
MLA	Shi, Yaoke,et al."Membrane fouling diagnosis of membrane components based on multi-feature information fusion".JOURNAL OF MEMBRANE SCIENCE 657(2022).