Moosavi, Seyedehmaryam and Manta, Otilia and El-Badry, Yaser A. and Hussein, Enas E. and El-Bahy, Zeinhom M. and Mohd Fawzi, Noor fariza Binti and Urbonavicius, Jaunius and Moosavi, Seyed Mohammad Hossein (2021) A study on machine learning methods' application for dye adsorption prediction onto agricultural waste activated carbon. Nanomaterials, 11 (10). ISSN 2079-4991, DOI https://doi.org/10.3390/nano11102734.
Full text not available from this repository.Abstract
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-2 = 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.
| Item Type: | Article |
|---|---|
| Funders: | Taif University [Grant No: TURSP-2020/106], Research Council of Lithuania (LMTLT) [Grant No: S-MIP-19-61], Taif University, Taif, Saudi Arabia [Grant No: TURSP-2020/106] |
| Uncontrolled Keywords: | Machine learning; Wastewater treatment; Dye adsorption; Agricultural waste; Activated carbon |
| Subjects: | Q Science > QC Physics Q Science > QD Chemistry |
| Divisions: | Faculty of Engineering Nanotechnology & Catalysis Research Centre |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 18 Jul 2022 07:07 |
| Last Modified: | 18 Jul 2022 07:07 |
| URI: | http://eprints.um.edu.my/id/eprint/33885 |
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