Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method

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	Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method
	Chang, T (Chang, Tian)[ 1,3 ]; Lu, JQ (Lu, Jiaqi)[ 1 ]; Shen, ZX (Shen, Zhenxing)[ 1,2 ]; Huang, Y (Huang, Yu)[ 2 ]; Lu, D (Lu, Di)[ 1 ]; Wang, X (Wang, Xin)[ 4 ]; Cao, JJ (Cao, Junji)[ 2 ]; Morent, R (Morent, Rino)[ 3 ]
	2019-05-05
发表期刊	APPLIED CATALYSIS B-ENVIRONMENTAL
卷号	244 页码:107-119
产权排序	2
摘要	In this study, a post-non-thermal plasma (NTP)-catalytic system was developed for the removal of toluene over a series of MnCoOx/gamma-Al2O3 catalysts. The addition of the MnCoOx/gamma-Al2O3 catalysts markedly promoted the toluene removal efficiency, CO. yield, CO2 yield and energy yield (EY) compared with the plasma alone system. The 5 wt% MnCoOx/gamma-Al2O3 catalyst exhibited the best reaction performance, which could be attributed to the reducibility and surface active oxygen species of the catalyst. With artificial neural network (ANN), the effects of experimental parameters on the reaction performance of toluene degradation were modeled and analyzed; for this analysis, four parameters were considered, namely, discharge power, initial concentration of toluene, flow rate, and relative humidity. The results indicated that the predicted results fitted well with the experimental results. The discharge power was the most significant factor for the toluene removal efficiency and CO. yield, whereas the EY was the most influenced by the gas flow rate. A multi-objective optimization model was proposed to determine optimal experimental parameters, which was then solved using the non-dominating sorting genetic algorithm II (NSGA-II). The results revealed that the Pareto front obtained from the hybrid ANN and NSGA-II method provided a series of feasible and optimal process parameters for the post-NTP-catalytic system. This hybrid method also served as an effective tool to select process parameters according to application conditions and preferences.
关键词	Post-plasma-catalytic system Dielectric barrier discharge Toluene removal Artificial neural network Non-dominating sorting genetic algorithm II
收录类别	SCI ; SCIE
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ieecas.cn/handle/361006/10929
专题	粉尘与环境研究室
通讯作者	Shen, ZX (Shen, Zhenxing)[ 1,2 ]
作者单位	1.Department of Environmental Sciences and Engineering, Xi’an Jiaotong University, Xi’an, 710049, China; 2.Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710049, China; 3.Research Unit Plasma Technology, Department of Applied Physics, Faculty of Engineering and Architecture, Ghent University, Sint-Pietersnieuwstraat 41 – B4, 9000 Ghent, Belgium; 4.Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, 55128, Germany
推荐引用方式 GB/T 7714	Chang, T ,Lu, JQ ,Shen, ZX ,et al. Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method[J]. APPLIED CATALYSIS B-ENVIRONMENTAL,2019,244:107-119.
APA	Chang, T .,Lu, JQ .,Shen, ZX .,Huang, Y .,Lu, D .,...&Morent, R .(2019).Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method.APPLIED CATALYSIS B-ENVIRONMENTAL,244,107-119.
MLA	Chang, T ,et al."Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method".APPLIED CATALYSIS B-ENVIRONMENTAL 244(2019):107-119.

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