中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 高温气体动力学国家重点实验室
Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model

文献类型：期刊论文

作者Wang, Yaolin2; Liao, Zinan2; Mathieu, Stephanie2; Bin F(宾峰)1,2; Tu, Xin2
刊名JOURNAL OF HAZARDOUS MATERIALS
出版日期2021-02-15
卷号404页码:10
关键词Machine learning Non-thermal plasma Biomass gasification Tar reforming Naphthalene
ISSN号0304-3894
DOI10.1016/j.jhazmat.2020.123965
通讯作者Bin, Feng(binfeng@imech.ac.cn) ; Tu, Xin(xin.tu@liverpool.ac.uk)
英文摘要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.
分类号一类
WOS关键词NEURAL-NETWORK ; BIOMASS GASIFICATION ; TAR SURROGATE ; TOLUENE ; COMPOUND ; DECOMPOSITION ; CONVERSION ; METHANE ; REACTOR ; OPTIMIZATION
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]
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000598929700005
资助机构European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant ; Royal Society Newton Advanced Fellowship
其他责任者Bin, Feng ; Tu, Xin
源URL[http://dspace.imech.ac.cn/handle/311007/85864]  
专题力学研究所_高温气体动力学国家重点实验室
作者单位1.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China
2.Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England;
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.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。