| Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model | |
Wang, Yaolin1; Liao, Zinan1; Mathieu, Stephanie1; Bin F(宾峰)1,2 ; Tu, Xin1
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| Corresponding Author | Bin, Feng([email protected]) ; Tu, Xin([email protected]) |
| Source Publication | JOURNAL OF HAZARDOUS MATERIALS
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| 2021-02-15 | |
| Volume | 404Pages:10 |
| ISSN | 0304-3894 |
| Abstract | 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. |
| Keyword | Machine learning Non-thermal plasma Biomass gasification Tar reforming Naphthalene |
| DOI | 10.1016/j.jhazmat.2020.123965 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000598929700005 |
| WOS Keyword | NEURAL-NETWORK ; BIOMASS GASIFICATION ; TAR SURROGATE ; TOLUENE ; COMPOUND ; DECOMPOSITION ; CONVERSION ; METHANE ; REACTOR ; OPTIMIZATION |
| WOS Research Area | Engineering ; Environmental Sciences & Ecology |
| WOS Subject | Engineering, Environmental ; Environmental Sciences |
| Funding Project | European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant[722346] ; Royal Society Newton Advanced Fellowship[NAF/R1/180230] |
| Funding Organization | European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant ; Royal Society Newton Advanced Fellowship |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Bin, Feng ; Tu, Xin |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/85865 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 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 |
| Recommended Citation 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.Rp_Au:Bin, Feng, Tu, Xin |
| 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. |
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| Jp2021071.pdf（7145KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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