Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

• Published in: Adsorption science & technology Vol. 2022
• Main Authors: Alsolai, Hadeel, Asiri, Mashael M., Alabdan, Rana, Al-Hagery, Mohammed Abdullah, Hilal, Anwer Mustafa, Rizwanullah, Mohammed, Motwakel, Abdelwahed, Yaseen, Ishfaq
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2022; Sage Publications Ltd; SAGE Publications
• Subjects: 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
• ISSN: 0263-6174; 2048-4038
• DOI: 10.1155/2022/3410872

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.