A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method
Abstract
:1. Introduction
2. Materials and Methods
3. Literature Survey Comparisons
4. Underlying Mechanisms of Metal Ions and Metal-Based Nanoparticles
4.1. Fe-Based Ions and Nanoparticles
4.2. Ni-Based Ions and Nanoparticles
5. Results
5.1. Impact of Fe-Based Ions and NP Addition
5.2. Impact of Ni-Based Ions and NP Addition
5.3. Impact of Other Metal and Non-Metal Nanoparticle Addition
5.4. Impact of Ion Addition
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANOVA | Analysis of variation |
ANNs | Artificial neural networks |
ATR | Auto-thermal reforming |
BioH2 | Biological hydrogen |
BBD | Box–Behnken design |
CCSU | Carbon capture storage and utilization |
CCD | Central composite design |
DO | Dissolved oxygen |
GHE | Greenhouse gas emission |
H2 | Hydrogen |
HER | Hydrogen evolution rate |
HY | Hydrogen yield |
MSE | Mean square error |
NGSR | Natural gas steam reforming |
NGTC | Nature gas thermal cracking |
NPs | Nanoparticles |
RSM | Response surface methodology |
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---|---|---|---|---|---|---|---|
Fe (NPs) | 400 | Grass | 10.7 | 50 | 2.9 | 5.4 | [45] |
Fe (NPs) | 25 | Starch | 5 | 35 | 3 | - | [18] |
Fe (NPs) | 300 | Malate | 3 | 16 | 20 | 0.4 | [46] |
Fe (NPs) | 50 | Xylose | 30 | 75 | 13.3 | 2 | [47] |
Fe (NPs) | 200 | MSJ | 10 | 50 | 0.9 | 2.4 | [48] |
Fe (NPs) | 200 | Sucrose | 7.5 | 50 | 15.9 | 10.1 | [27] |
Fe (NPs) | 175 | Glucose | 7.5 | 59 | 12.9 | 5.69 | [28] |
Fe (NPs) | 50 | Starch | 6 | 35 | 5 | - | [43] |
Fe (NPs) | 250 | Malate | 4 | 12 | 24.2 | 0.8 | [44] |
Fe2O3 (NPs) | 50 | Glucose | 5 | 50 | 1.92 | 2.5 | [49] |
Fe2O3 (NPs) | 50 | CDW | 15.3 | 33 | 16.75 | 102.5 | [17] |
Fe2O3 (NPs) | 200 | DW | 56 | 23 | 7.85 | 62.4 | [30] |
Fe2O3 (NPs) | 50 | Wastewater | 110 | 6.5 | 1.9 | 49.4 | [50] |
Fe2O3 (NPs) | 200 | MEG | 4 | 100 | 8.4 | 0.6 | [51] |
Fe2O3 (NPs) | 300 | CAS | 10 | 20 | 3.875 | 1.92 | [52] |
Fe2O3 (NPs) | 200 | Glucose | 10 | 20 | 9.2 | 3.1 | [52] |
Fe2O3 (NPs) | 60 | Glucose | 6 | 60 | 1.92 | 2.5 | [49] |
Fe3O4(NPs) | 10 | Glucose | 2.5 | 100 | 10.1 | 0.23 | [53] |
Fe3O4(A-C-NPs) | 250 | Glucose | 5 | 30 | 11.656 | 3.2 | [38] |
GT-INP (Fe2O4 and FeO(OH)(NPs) | 1000 | CO | 1.008 | 70 | 1.58 | 0.0662 | [54] |
Magnetite (NPs) | 200 | SJ | 3 | 50 | 6.7 | 0.23 | [55] |
Hematite (NPs) | 200 | Sucrose | 12.5 | 55 | 10.4 | 6 | [56] |
Nanoparticles | Opt/mg L−1 | Substrate | SC/g L−1 | Size/nm | HY /mmol g−1 | HER /mmol L−1h−1 | Reference |
---|---|---|---|---|---|---|---|
Ni (NPs) | 5.7 | Glucose | 14.01 | 13.6 | 14.1 | 11.5 | [57] |
Ni (NPs) | 32 | Starch | 8 | 80 | 2.4 | 10.3 | [18] |
Ni (NPs) | 60 | MEG | 4.7 | 60 | 1.11 | 1.5 | [23] |
Ni (NPs) | 10 | Glucose | 1 | 25 | 9.5 | 30 | [32] |
Ni (NPs) | 1 | Glucose | 2.5 | 100 | 11.7 | 0.28 | [53] |
Ni (NPs) | 4.3 | Glucose | 13.92 | 28 | 12.7 | 10.4 | [57] |
Ni (NPs) | 2.5 | Glucose | 5 | 42.5 | 10.8 | 1.3 | [58] |
Ni (NPs) | 25 | Starch | 10 | 40 | 2.7 | 11.5 | [18] |
Ni (NPs) | 11 | Glucose | 2.7 | 120 | 1.21 | 0.22 | [59] |
NiO (NPs) | 20 | MEG | 4 | 100 | 7.25 | 0.5 | [51] |
NiO (NPs) | 10 | CDW | 15.3 | 23 | 15.7 | 44.9 | [17] |
NiO (NPs) | 1.5 | Wastewater | 9.6 | 23.6 | 0.5 | 12 | [31] |
Ni (NPs) | 100 | CS | 20 | 50 | 20 | 0.27 | [60] |
NPs | Opt/mg L−1 | Substrate | SC/g L−1 | Size/nm | HY /mmol g−1 | HER /mmol L−1h−1 | Reference |
---|---|---|---|---|---|---|---|
Ag | 0.002 | Glucose | 12.5 | 15 | 13.8 | 10.5 | [61] |
Cu | 2.5 | Glucose | 2.5 | 97 | 2.8 | 5.4 | [62] |
Pd | 5 | Glucose | 10 | 100 | 8.1 | 6.7 | [63] |
Au | 0.002 | Sucrose | 15 | 5 | 7.5 | 7.3 | [64] |
Co | 1 | Glucose | 2.5 | 100 | 4.85 | 0.16 | [53] |
CoO | 1 | POME | 76.5 | 17 | 22.5 | 0.7 | [31] |
TiO2 | 100 | Xylose | 30 | 30 | 12 | 1.8 | [47] |
ZnO | 10 | MEG | 4 | 100 | 7.3 | 0.58 | [51] |
MgO | 1 | Glucose | 100 | 100 | 4.3 | 0.1 | [53] |
Cu/SiO2 | 0.064 | Glucose | 5 | 2.5 | 5.8 | 0.54 | [65] |
Ag/SiO2 | 0.107 | Glucose | 5 | 2.5 | 5.4 | 0.5 | [65] |
Pd/SiO2 | 0.207 | Glucose | 5 | 2.5 | 5.4 | 0.52 | [65] |
Source | Sum of Squares | DF | Mean Square | F-Value | p-Value |
---|---|---|---|---|---|
Model | 38,286.08 | 20 | 1914.30 | 4.16 | 0.0005 |
A-Mg2+ | 2467.73 | 1 | 2467.73 | 5.36 | 0.0291 |
B-Cu2+ | 1729.50 | 1 | 1729.50 | 3.75 | 0.0640 |
C-Na+ | 7543.84 | 1 | 7543.84 | 16.38 | 0.0004 |
D-NH4+ | 496.57 | 1 | 496.57 | 1.08 | 0.3091 |
E-K+ | 261.49 | 1 | 261.49 | 0.5677 | 0.4582 |
AB | 7903.35 | 1 | 7903.35 | 17.16 | 0.0003 |
AC | 1957.27 | 1 | 1957.27 | 4.25 | 0.0498 |
AD | 513.91 | 1 | 513.91 | 1.12 | 0.3009 |
AE | 1109.51 | 1 | 1109.51 | 2.41 | 0.1332 |
BC | 41.84 | 1 | 41.84 | 0.0908 | 0.7656 |
BD | 330.26 | 1 | 330.26 | 0.7170 | 0.4052 |
BE | 16.50 | 1 | 16.50 | 0.0358 | 0.8514 |
CD | 4919.66 | 1 | 4919.66 | 10.68 | 0.0031 |
CE | 2100.83 | 1 | 2100.83 | 4.56 | 0.0427 |
DE | 1719.79 | 1 | 1719.79 | 3.73 | 0.0647 |
A2 | 801.66 | 1 | 801.66 | 1.74 | 0.1990 |
B2 | 3897.09 | 1 | 3897.09 | 8.46 | 0.0075 |
C2 | 2148.80 | 1 | 2148.80 | 4.67 | 0.0406 |
D2 | 387.39 | 1 | 387.39 | 0.8410 | 0.3679 |
E2 | 1539.54 | 1 | 1539.54 | 3.34 | 0.0795 |
Residue | 11,515.22 | 25 | 460.61 | ||
Lack of fit | 11,515.22 | 20 | 575.76 | ||
Pure Error | 0.0000 | 5 | 0.0000 | ||
Cor total | 49,801.31 | 45 |
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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. Energies 2021, 14, 5916. https://doi.org/10.3390/en14185916
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. Energies. 2021; 14(18):5916. https://doi.org/10.3390/en14185916
Chicago/Turabian StyleLiu, Yiyang, Jinze Liu, Hongzhen He, Shanru Yang, Yixiao Wang, Jin Hu, Huan Jin, Tianxiang Cui, Gang Yang, and Yong Sun. 2021. "A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method" Energies 14, no. 18: 5916. https://doi.org/10.3390/en14185916