A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon
The adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After...
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MDPI AG
2021-10-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/11/10/2734 |
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| author | Seyedehmaryam Moosavi Otilia Manta Yaser A. El-Badry Enas E. Hussein Zeinhom M. El-Bahy Noor fariza Binti Mohd Fawzi Jaunius Urbonavičius Seyed Mohammad Hossein Moosavi |
| author_facet | Seyedehmaryam Moosavi Otilia Manta Yaser A. El-Badry Enas E. Hussein Zeinhom M. El-Bahy Noor fariza Binti Mohd Fawzi Jaunius Urbonavičius Seyed Mohammad Hossein Moosavi |
| author_sort | Seyedehmaryam Moosavi |
| collection | DOAJ |
| description | The adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After comprehensive feature selection analysis, five important variables were selected from nine variables. The RF with the highest accuracy (R<sup>2</sup> = 0.9) was selected as the best model for prediction of adsorption capacity of agro-waste using the five selected variables. The results suggested that agro-waste characteristics (pore volume, surface area, agro-waste pH, and particle size) accounted for 50.7% contribution for adsorption efficiency. The pore volume and surface area are the most important influencing variables among the agro-waste characteristics, while the role of particle size was inconspicuous. The accurate ability of the developed models’ prediction could significantly reduce experimental screening efforts, such as predicting the dye removal efficiency of agro-waste activated carbon according to agro-waste characteristics. The relative importance of variables could provide a right direction for better treatments of dyes in the real wastewater. |
| first_indexed | 2024-03-10T06:19:41Z |
| format | Article |
| id | doaj.art-f6b8b545fa6248faafc88eb0a44565c7 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-10T06:19:41Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-f6b8b545fa6248faafc88eb0a44565c72023-11-22T19:25:47ZengMDPI AGNanomaterials2079-49912021-10-011110273410.3390/nano11102734A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated CarbonSeyedehmaryam Moosavi0Otilia Manta1Yaser A. El-Badry2Enas E. Hussein3Zeinhom M. El-Bahy4Noor fariza Binti Mohd Fawzi5Jaunius Urbonavičius6Seyed Mohammad Hossein Moosavi7Department of Chemistry and Bioengineering, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaRomanian Academy, Center for Financial and Monetary Research “Victor Slavescu”, 050711 Bucharest, RomaniaChemistry Department, Faculty of Science, Taif University, Khurma, P.O. Box 11099, Taif 21944, Saudi ArabiaNational Water Research Centre, P.O. Box 74, Shubra EI-Kheima 13411, EgyptChemistry Department, Faculty of Science, Al-Azhar University, Cairo 11884, EgyptNanotechnology & Catalysis Research Centre (NANOCAT), Institute for Advanced Studies (IAS), University for Malaya (UM), Kuala Lumpur 50603, MalaysiaDepartment of Chemistry and Bioengineering, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaFaculty of Engineering, Centre for Transportation Research (CTR), University of Malaya (UM), Kuala Lumpur 50603, MalaysiaThe adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After comprehensive feature selection analysis, five important variables were selected from nine variables. The RF with the highest accuracy (R<sup>2</sup> = 0.9) was selected as the best model for prediction of adsorption capacity of agro-waste using the five selected variables. The results suggested that agro-waste characteristics (pore volume, surface area, agro-waste pH, and particle size) accounted for 50.7% contribution for adsorption efficiency. The pore volume and surface area are the most important influencing variables among the agro-waste characteristics, while the role of particle size was inconspicuous. The accurate ability of the developed models’ prediction could significantly reduce experimental screening efforts, such as predicting the dye removal efficiency of agro-waste activated carbon according to agro-waste characteristics. The relative importance of variables could provide a right direction for better treatments of dyes in the real wastewater.https://www.mdpi.com/2079-4991/11/10/2734machine learningwastewater treatmentdye adsorptionagricultural wasteactivated carbon |
| spellingShingle | Seyedehmaryam Moosavi Otilia Manta Yaser A. El-Badry Enas E. Hussein Zeinhom M. El-Bahy Noor fariza Binti Mohd Fawzi Jaunius Urbonavičius Seyed Mohammad Hossein Moosavi A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon Nanomaterials machine learning wastewater treatment dye adsorption agricultural waste activated carbon |
| title | A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon |
| title_full | A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon |
| title_fullStr | A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon |
| title_full_unstemmed | A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon |
| title_short | A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon |
| title_sort | study on machine learning methods application for dye adsorption prediction onto agricultural waste activated carbon |
| topic | machine learning wastewater treatment dye adsorption agricultural waste activated carbon |
| url | https://www.mdpi.com/2079-4991/11/10/2734 |
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