Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning
Copyright © 2024 Elsevier Ltd. All rights reserved..
The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, "Clean Water and Sanitation". However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43-) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43-, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4-73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43- removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:356 |
|---|---|
| Enthalten in: |
Journal of environmental management - 356(2024) vom: 26. Apr., Seite 120728 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Mohit, Aggarwal [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
059QF0KO0R |
|---|
| Anmerkungen: |
Date Completed 08.04.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.jenvman.2024.120728 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370206932 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370206932 | ||
| 003 | DE-627 | ||
| 005 | 20240408232845.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240328s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jenvman.2024.120728 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1369.xml |
| 035 | |a (DE-627)NLM370206932 | ||
| 035 | |a (NLM)38531138 | ||
| 035 | |a (PII)S0301-4797(24)00714-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Mohit, Aggarwal |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 08.04.2024 | ||
| 500 | |a Date Revised 08.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, "Clean Water and Sanitation". However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43-) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43-, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4-73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43- removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a AdaBoost | |
| 650 | 4 | |a Microalgal cultivation | |
| 650 | 4 | |a Modelling | |
| 650 | 4 | |a RSM | |
| 650 | 4 | |a Wastewater treatment | |
| 650 | 4 | |a XGBoost | |
| 650 | 7 | |a Phosphorus |2 NLM | |
| 650 | 7 | |a 27YLU75U4W |2 NLM | |
| 650 | 7 | |a Carbon |2 NLM | |
| 650 | 7 | |a 7440-44-0 |2 NLM | |
| 650 | 7 | |a Nitrogen |2 NLM | |
| 650 | 7 | |a N762921K75 |2 NLM | |
| 650 | 7 | |a Water |2 NLM | |
| 650 | 7 | |a 059QF0KO0R |2 NLM | |
| 700 | 1 | |a Remya, Neelancherry |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of environmental management |d 2001 |g 356(2024) vom: 26. Apr., Seite 120728 |w (DE-627)NLM112866786 |x 1095-8630 |7 nnns |
| 773 | 1 | 8 | |g volume:356 |g year:2024 |g day:26 |g month:04 |g pages:120728 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.jenvman.2024.120728 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 356 |j 2024 |b 26 |c 04 |h 120728 | ||