Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model

doi:10.1016/j.jhazmat.2020.123965

	Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model
	Wang, Yaolin 1; Liao, Zinan 1; Mathieu, Stephanie 1; Bin F(宾峰)1,2 ; Tu, Xin 1
通讯作者	Bin, Feng(binfeng@imech.ac.cn) ; Tu, Xin(xin.tu@liverpool.ac.uk)
发表期刊	JOURNAL OF HAZARDOUS MATERIALS
	2021-02-15
卷号	404 页码:10
ISSN	0304-3894
摘要	We have developed a hybrid machine learning (ML) model for the prediction and optimization of a gliding arc plasma tar reforming process using naphthalene as a model tar compound from biomass gasification. A linear combination of three well-known algorithms, including artificial neural network (ANN), support vector regression (SVR) and decision tree (DT) has been established to deal with the multi-scale and complex plasma tar reforming process. The optimization of the hyper-parameters of each algorithm in the hybrid model has been achieved by using the genetic algorithm (GA), which shows a fairly good agreement between the experimental data and the predicted results from the ML model. The steam-to-carbon (S/C) ratio is found to be the most critical parameter for the conversion with a relative importance of 38%, while the discharge power is the most influential parameter in determining the energy efficiency with a relative importance of 58%. The coupling effects of different processing parameters on the key performance of the plasma reforming process have been evaluated. The optimal processing parameters are identified achieving the maximum tar conversion (67.2%), carbon balance (81.7%) and energy efficiency (7.8 g/kWh) simultaneously when the global desirability index I-2 reaches the highest value of 0.65.
关键词	Machine learning Non-thermal plasma Biomass gasification Tar reforming Naphthalene
DOI	10.1016/j.jhazmat.2020.123965
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000598929700005
关键词[WOS]	NEURAL-NETWORK ; BIOMASS GASIFICATION ; TAR SURROGATE ; TOLUENE ; COMPOUND ; DECOMPOSITION ; CONVERSION ; METHANE ; REACTOR ; OPTIMIZATION
WOS研究方向	Engineering ; Environmental Sciences & Ecology
WOS类目	Engineering, Environmental ; Environmental Sciences
资助项目	European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant[722346] ; Royal Society Newton Advanced Fellowship[NAF/R1/180230]
项目资助者	European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant ; Royal Society Newton Advanced Fellowship
论文分区	一类
力学所作者排名	1
RpAuthor	Bin, Feng ; Tu, Xin
引用统计	被引频次：55[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/85865
专题	高温气体动力学国家重点实验室
作者单位	1.Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England; 2.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yaolin,Liao, Zinan,Mathieu, Stephanie,et al. Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model[J]. JOURNAL OF HAZARDOUS MATERIALS,2021,404:10.
APA	Wang, Yaolin,Liao, Zinan,Mathieu, Stephanie,宾峰,&Tu, Xin.(2021).Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model.JOURNAL OF HAZARDOUS MATERIALS,404,10.
MLA	Wang, Yaolin,et al."Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model".JOURNAL OF HAZARDOUS MATERIALS 404(2021):10.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Jp2021071.pdf（7145KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览请求全文