Membrane Fouling Prediction Based on Tent-SSA-BP

doi:10.3390/membranes12070691

	Membrane Fouling Prediction Based on Tent-SSA-BP
	Ling, Guobi 4; Wang, Zhiwen 2,3,4; Shi, Yaoke 4; Wang, Jieying 4; Lu, Yanrong 2,3,4; Li, Long 1,4
刊名	Membranes
	2022-07-01
卷号	12 期号:7
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
英文摘要	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.
WOS研究方向	Biochemistry & Molecular Biology ; Chemistry ; Engineering ; Materials Science ; Polymer Science
语种	英语
出版者	MDPI
WOS记录号	WOS:000833700000001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159426]
专题	电气工程与信息工程学院
作者单位	1.GS-Unis Intelligent Transportation System & Control Technology Co., Ltd, Lanzhou; 730050, China 2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 3.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou; 730050, China; 4.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China;
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).