Document Type
Journal Article
Publication Title
Energies
Volume
14
Issue
18
Publisher
MDPI
School
School of Engineering
RAS ID
39757
Funders
Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province Qianjiang Talent Scheme Ningbo Science and Technology Innovation 2025 Key Project Ningbo Municipal Commonweal Key Program UNNC FoSE Researchers Grant
Abstract
In this work, the impact of chemical additions, especially nano‐particles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)‐response surface methodology (RSM) algorithm. Fe‐based and Ni‐based 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 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 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+.
DOI
10.3390/en14185916
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Liu, Y., Liu, J., He, H., Yang, S., Wang, Y., Hu, J., . . . Sun, Y. (2021). A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method. Energies, 14(18), article 5916. https://doi.org/10.3390/en14185916