Membrane Fouling Prediction Based on Tent-SSA-BP

doi:10.3390/membranes12070691

	Membrane Fouling Prediction Based on Tent-SSA-BP
	Ling, Guobi 1; Wang, Zhiwen1,2,3 ; Shi, Yaoke 1; Wang, Jieying 1; Lu, Yanrong 1,2,3; Li, Long 1,4
	2022-07
发表期刊	Membranes
卷号	12 期号:7
摘要	In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
关键词	Bioreactors Forecasting Genetic algorithms Learning algorithms Mapping Membrane fouling Particle swarm optimization (PSO) Chaotic mapping Membrane bioreactor Membrane flux prediction Membrane fluxes Network models Search Algorithms Sparrow search algorithm Tent chaotic mapping Tent sparrow search algorithm back propagation network model
DOI	10.3390/membranes12070691
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Chemistry ; Engineering ; Materials Science ; Polymer Science
WOS类目	Biochemistry & Molecular Biology ; Chemistry, Physical ; Engineering, Chemical ; Materials Science, Multidisciplinary ; Polymer Science
WOS记录号	WOS:000833700000001
出版者	MDPI
EI入藏号	20222812347849
EI主题词	Membranes
EI分类号	405.3 Surveying - 461.8 Biotechnology - 539.1 Metals Corrosion - 723 Computer Software, Data Handling and Applications - 723.4.2 Machine Learning - 802.1 Chemical Plants and Equipment - 921.5 Optimization Techniques - 951 Materials Science
来源库	WOS
引用统计	被引频次：7[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/159426
专题	电气工程与信息工程学院
通讯作者	Wang, Zhiwen
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 Edu, Lanzhou 730050, Peoples R China; 4.GS Unis Intelligent Transportat Syst & Control Te, 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; 兰州理工大学
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	Ling, Guobi,Wang, Zhiwen,Shi, Yaoke,et al. Membrane Fouling Prediction Based on Tent-SSA-BP[J]. Membranes,2022,12(7).
APA	Ling, Guobi,Wang, Zhiwen,Shi, Yaoke,Wang, Jieying,Lu, Yanrong,&Li, Long.(2022).Membrane Fouling Prediction Based on Tent-SSA-BP.Membranes,12(7).
MLA	Ling, Guobi,et al."Membrane Fouling Prediction Based on Tent-SSA-BP".Membranes 12.7(2022).