A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method

Yiyang Liu, Jinze Liu, Hongzhen He, Shanru Yang, Yixiao Wang, Jin Hu, Huan Jin, Tianxiang Cui, Gang Yang and Yong Sun
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Yiyang Liu: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Jinze Liu: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Hongzhen He: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Shanru Yang: Centre for English Language Education (CELE), University of Nottingham Ningbo, Ningbo 315100, China
Yixiao Wang: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Jin Hu: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Huan Jin: School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
Tianxiang Cui: School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
Gang Yang: Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100864, China
Yong Sun: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China

Energies, 2021, vol. 14, issue 18, 1-16

Abstract: In this work, the impact of chemical additions, especially nano-particles (NPs), was quantitatively analyzed using our constructed artificial neural networks (ANNs)-response surface methodology (RSM) algorithm. Fe-based and Ni-based NPs and ions, including Mg 2+ , Cu 2+ , Na + , NH 4 + , 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 Fe-based NPs and ions, but not for Ni-based NPs and ions. An optimal range of particle size (86–120 nm) and Ni-ion/NP concentration (81–120 mg L ?1 ) 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 (40–50 nm) seemed to significantly increase the H 2 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 + > Mg 2+ > Cu 2+ > NH 4 + > K + .

Keywords: biohydrogen (BioH 2 ); nanoparticles; quantitative assessment; artificial neuron networks; process intensifications (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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