A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method
文献类型：期刊论文
| 作者 | Liu, Yiyang2; Liu, Jinze2; He, Hongzhen2; Yang, Shanru3; Wang, Yixiao2; Hu, Jin2; Jin, Huan4; Cui, Tianxiang4; Yang, Gang1; Sun, Yong2,5 |
| 刊名 | Energies
 |
| 出版日期 | 2021-09-02 |
| 卷号 | 14期号:18 |
| 关键词 | Hydrogen production - Supervised learning - Chemical analysis - Neural networks - Particle size |
| DOI | 10.3390/en14185916 |
| 英文摘要 | In this work, the impact of chemical additions, especially nanoparticles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)response surface methodology (RSM) algorithm. Febased and Nibased NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Febased NPs and ions, but not for Nibased NPs and ions. An optimal range of particle size (86鈥?20 nm) and Niion/NP concentration (81鈥?20 mg L鈭?) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40鈥?0 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+. 漏 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
| 学科主题 | Ions |
| 项目编号 | This work was supported by the Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province (2020E10018), the Qianjiang Talent Scheme (QJD1803014), the Ningbo Science and Technology Innovation 2025 Key Project (2020Z100), and the Ningbo Municipal Commonweal Key Program (2019C10033 & 2019C10104), UNNC FoSE Research-ers Grant 2020 (I01210100011). |
| 出版者 | MDPI |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/60340]  |
| 作者单位 | 1.Institute of Process Engineering, Chinese Academy of Sciences, Beijing; 10086, China 2.Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo; 315100, China 3.Centre for English Language Education (CELE), University of Nottingham Ningbo, Ningbo; 315100, China 4.School of Computer Science, University of Nottingham Ningbo, Ningbo; 315100, China 5.School of Engineering, Edith Cowan University, 70 Joondalup Drive, Perth; WA; 6027, Australia |
| 推荐引用方式 GB/T 7714 | Liu, Yiyang,Liu, Jinze,He, Hongzhen,et al. A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method[J]. Energies,2021,14(18). |
| APA | Liu, Yiyang.,Liu, Jinze.,He, Hongzhen.,Yang, Shanru.,Wang, Yixiao.,...&Sun, Yong.(2021).A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method.Energies,14(18). |
| MLA | Liu, Yiyang,et al."A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method".Energies 14.18(2021). |
入库方式： OAI收割
来源：过程工程研究所
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