Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning
Nguyen, XC
Ly, QV
Peng, W
Nguyen, V-H
Nguyen, DD
Tran, QB
Huyen Nguyen, TT
Sonne, C
Lam, SS
Ngo, HH
Goethals, P
Le, QV
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Journal of Hazardous Materials, 2021, 413, pp. 125426
- Issue Date:
- 2021-02-13
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, XC | |
dc.contributor.author | Ly, QV | |
dc.contributor.author | Peng, W | |
dc.contributor.author | Nguyen, V-H | |
dc.contributor.author | Nguyen, DD | |
dc.contributor.author | Tran, QB | |
dc.contributor.author | Huyen Nguyen, TT | |
dc.contributor.author | Sonne, C | |
dc.contributor.author | Lam, SS | |
dc.contributor.author | Ngo, HH | |
dc.contributor.author | Goethals, P | |
dc.contributor.author | Le, QV | |
dc.date.accessioned | 2021-12-07T05:13:11Z | |
dc.date.available | 2021-02-11 | |
dc.date.available | 2021-12-07T05:13:11Z | |
dc.date.issued | 2021-02-13 | |
dc.identifier.citation | Journal of Hazardous Materials, 2021, 413, pp. 125426 | |
dc.identifier.issn | 0304-3894 | |
dc.identifier.issn | 1873-3336 | |
dc.identifier.uri | http://hdl.handle.net/10453/152183 | |
dc.description.abstract | This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF<sub>1</sub>) and biochar (VF<sub>2</sub>), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD<sub>5</sub>) and nitrogen, accounting for 9.5 - 10.5 g<sup>.</sup>BOD<sub>5</sub><sup>.</sup>m<sup>-2.</sup>d<sup>-1</sup> and 3.5 - 3.6 g<sup>.</sup>NH<sub>4</sub>-N<sup>.</sup>m<sup>-2.</sup>d<sup>-1</sup>, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO<sub>3</sub>-N (P > 0.05), but they did influence NH<sub>4</sub>-N effluent as evidenced by the removal rate of that by VF<sub>1</sub> and VF<sub>2</sub> being of 8<sub>2</sub>.4 ± 5.7 and 84.6 ± 6.4%, respectively (P < 0.05). The results obtained from the designed systems were further subject to machine learning to clarify the effecting factors and predict the effluents. The optimal algorithms were random forest, generalized linear model, and support vector machine. The values of the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg<sup>.</sup>L<sup>-1</sup>, 80.0% and 0.3 mg<sup>.</sup>L<sup>-1</sup>, 90.1% and 2.9 mg<sup>.</sup>L<sup>-1</sup>, and 48.5% and 0<sup>.</sup>5 mg<sup>.</sup>L<sup>-1</sup> for BOD<sub>5</sub>_VF<sub>1</sub>, NH<sub>4</sub><sup>-</sup>N_VF<sub>1</sub>, BOD<sub>5</sub>_VF<sub>2</sub>, and NH<sub>4</sub>-N_VF<sub>2</sub>, respectively. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | Elsevier BV | |
dc.relation.ispartof | Journal of Hazardous Materials | |
dc.relation.isbasedon | 10.1016/j.jhazmat.2021.125426 | |
dc.rights | info:eu-repo/semantics/embargoedAccess | |
dc.subject | 03 Chemical Sciences, 05 Environmental Sciences, 09 Engineering | |
dc.subject.classification | Strategic, Defence & Security Studies | |
dc.subject.mesh | Charcoal | |
dc.subject.mesh | Nitrogen | |
dc.subject.mesh | Waste Disposal, Fluid | |
dc.subject.mesh | Wetlands | |
dc.subject.mesh | Biological Oxygen Demand Analysis | |
dc.subject.mesh | Waste Water | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Clay | |
dc.subject.mesh | Biological Oxygen Demand Analysis | |
dc.subject.mesh | Charcoal | |
dc.subject.mesh | Clay | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Nitrogen | |
dc.subject.mesh | Waste Disposal, Fluid | |
dc.subject.mesh | Waste Water | |
dc.subject.mesh | Wetlands | |
dc.title | Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning | |
dc.type | Journal Article | |
utslib.citation.volume | 413 | |
utslib.location.activity | Netherlands | |
utslib.for | 03 Chemical Sciences | |
utslib.for | 05 Environmental Sciences | |
utslib.for | 09 Engineering | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering | |
pubs.organisational-group | /University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment | |
utslib.copyright.status | open_access | * |
utslib.copyright.embargo | 2023-07-01T00:00:00+1000Z | |
dc.date.updated | 2021-12-07T05:13:10Z | |
pubs.publication-status | Published | |
pubs.volume | 413 |
Abstract:
This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF1) and biochar (VF2), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5.m-2.d-1 and 3.5 - 3.6 g.NH4-N.m-2.d-1, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 82.4 ± 5.7 and 84.6 ± 6.4%, respectively (P < 0.05). The results obtained from the designed systems were further subject to machine learning to clarify the effecting factors and predict the effluents. The optimal algorithms were random forest, generalized linear model, and support vector machine. The values of the coefficient of determination (R2) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg.L-1, 80.0% and 0.3 mg.L-1, 90.1% and 2.9 mg.L-1, and 48.5% and 0.5 mg.L-1 for BOD5_VF1, NH4-N_VF1, BOD5_VF2, and NH4-N_VF2, respectively.
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