Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon
Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen deman...
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| Published in: | Adsorption science & technology Vol. 2022 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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2022
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| Abstract | Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97. |
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| AbstractList | Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97. Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD 5) , total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97. |
| Author | Yaseen, Ishfaq Al-Hagery, Mohammed Abdullah Motwakel, Abdelwahed Asiri, Mashael M. Rizwanullah, Mohammed Alsolai, Hadeel Hilal, Anwer Mustafa Alabdan, Rana |
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| Cites_doi | 10.1016/j.scitotenv.2019.05.142 10.1016/j.jclepro.2020.122915 10.1016/j.apsusc.2020.147749 10.1371/journal.pone.0255269 10.1016/j.matpr.2020.09.311 10.1016/j.jenvman.2019.02.068 10.1051/e3sconf/20172200174 10.1007/s11554-021-01106-x 10.1109/MCAS.2006.1688199 10.1016/j.cej.2020.126782 10.1016/j.jhazmat.2019.121769 10.1016/j.jhazmat.2020.122598 10.1007/s00521-022-06925-y 10.1016/j.progpolymsci.2004.11.002 10.1007/s12652-020-02122-8 10.1016/j.carbon.2003.09.022 10.23919/JSEE.2021.000018 10.1016/j.knosys.2015.12.022 10.1007/s11036-021-01860-z 10.1016/j.jconhyd.2018.10.003 10.1007/s11036-021-01861-y 10.3390/nano11102734 10.2166/wst.2018.467 10.1109/ACCESS.2021.3107371 10.1016/j.polymertesting.2019.105909 10.1016/j.scitotenv.2019.134847 10.1007/s12652-020-02138-0 10.1016/j.cej.2020.128116 10.7717/peerj-cs.569 |
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| Copyright | Copyright © 2022 Hadeel Alsolai et al. Copyright Sage Publications Ltd. Jan 2022 |
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| SubjectTerms | Activated carbon Adsorption Adsorptivity Algorithms Artificial neural networks Biochemical oxygen demand Chemical oxygen demand Contaminants Machine learning Organic chemicals Organic chemistry Pore size Quality assessment Solid suspensions Trigonometric functions Wastewater management Water quality |
| Title | Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon |
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