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, Zhiwen 2,3,5; Du, Xianjun 2,3,5; Gong, Bin 5; Lu, Yanrong 2,3,5; Li, Long 4,5
刊名	JOURNAL OF MEMBRANE SCIENCE
	2022-09-05
卷号	657
关键词	MBR CBAM-MUL-CNN Feature fusion Attention mechanism Membrane fouling diagnosis
ISSN号	0376-7388
DOI	10.1016/j.memsci.2022.120670
英文摘要	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.
WOS研究方向	Engineering ; Polymer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000808466700004
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158872]
专题	电气工程与信息工程学院
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, 36 Pengjiaping Rd, Qilihe Dist, 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, Lanzhou 730050, Peoples R China;
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).