Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature..
Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
|---|---|
| Enthalten in: |
Environmental science and pollution research international - 31(2024), 18 vom: 20. Apr., Seite 27286-27303 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Huang, Wei [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
16291-96-6 |
|---|
| Anmerkungen: |
Date Completed 26.04.2024 Date Revised 26.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s11356-024-32951-5 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369967755 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369967755 | ||
| 003 | DE-627 | ||
| 005 | 20240426233943.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240322s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11356-024-32951-5 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1388.xml |
| 035 | |a (DE-627)NLM369967755 | ||
| 035 | |a (NLM)38507168 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Huang, Wei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed |
| 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 26.04.2024 | ||
| 500 | |a Date Revised 26.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | ||
| 520 | |a Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biochar | |
| 650 | 4 | |a Pb2+ adsorption | |
| 650 | 4 | |a fluidized bed | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a tube furnace | |
| 650 | 7 | |a Charcoal |2 NLM | |
| 650 | 7 | |a 16291-96-6 |2 NLM | |
| 650 | 7 | |a biochar |2 NLM | |
| 650 | 7 | |a Lead |2 NLM | |
| 650 | 7 | |a 2P299V784P |2 NLM | |
| 650 | 7 | |a Water Pollutants, Chemical |2 NLM | |
| 700 | 1 | |a Wang, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, JingJing |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Hongyun |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, LinLing |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Zhong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Environmental science and pollution research international |d 1994 |g 31(2024), 18 vom: 20. Apr., Seite 27286-27303 |w (DE-627)NLM093849869 |x 1614-7499 |7 nnns |
| 773 | 1 | 8 | |g volume:31 |g year:2024 |g number:18 |g day:20 |g month:04 |g pages:27286-27303 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s11356-024-32951-5 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 31 |j 2024 |e 18 |b 20 |c 04 |h 27286-27303 | ||