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; Yong Sun

LAPSE:2023.19556

Published Article

LAPSE:2023.19556

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, Yong Sun

March 9, 2023

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 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+.

Record ID

LAPSE:2023.19556

Keywords

artificial neuron networks, biohydrogen (BioH2), nanoparticles, process intensifications, quantitative assessment

Subject

Materials

Suggested Citation

Liu Y, Liu J, He H, Yang S, Wang Y, Hu J, Jin H, Cui T, Yang G, Sun Y. A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method. (2023). LAPSE:2023.19556

Author Affiliations

Liu Y: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Liu J: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
He H: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Yang S: Centre for English Language Education (CELE), University of Nottingham Ningbo, Ningbo 315100, China
Wang Y: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Hu J: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
Jin H: School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
Cui T: School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
Yang G: Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100864, China
Sun Y: Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China; School of Engineering, Edith Cowan University, 70 Joondalup Drive, Perth, WA 6027, Australia

Journal Name

Energies

Volume

14

Issue

18

First Page

5916

Year

2021

Publication Date

2021-09-17