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Application of Artificial Neural Network and Response Surface Methodology in Adsorption of Acenaphthene Using Tea Waste Biochar
Acenaphthene has been well recognized as a significant organic pollutant in wastewater, exhibiting detrimental impacts on both flora and fauna. Several water treatment techniques have demonstrated considerable potential for effectively removing ACEs from the wastewater. However, the techniques are considered expensive. Adsorption is considered an economical method for the pollutants removal in wastewater. The present study aimed to assess the efficiency of utilizing tea waste biochar as an adsorbent for acenaphthene. The biochar was synthesized through the process of pyrolysis, therefore turning waste tea into a valuable form of biochar. During the adsorption procedure the analysis of acenaphthene was conducted using High Performance Liquid Chromatography. Three controlled factors were used to determine the efficiency of the adsorbent material: pH value, contact time (in min), and dosage of the biochar (in mg/L). The response surface methodology and artificial neural network were used to determine the optimal settings of factors. The findings of the study indicate that tea waste biochar exhibited a significant capacity for adsorption by achieving a 99.95% removal percentage of acenaphthene, making it a promising and effective adsorbent. The optimal pH for this adsorption process was determined to be 5.4, while the ideal contact duration was found to be 12.8 min. Additionally, the optimum dosage of the adsorbent was determined to be 185 mg/L. Both models performed well for the optimization of parameters. Artificial neural network was less complex and needed less computation time compared to response surface methodology.
Application of Artificial Neural Network and Response Surface Methodology in Adsorption of Acenaphthene Using Tea Waste Biochar
Acenaphthene has been well recognized as a significant organic pollutant in wastewater, exhibiting detrimental impacts on both flora and fauna. Several water treatment techniques have demonstrated considerable potential for effectively removing ACEs from the wastewater. However, the techniques are considered expensive. Adsorption is considered an economical method for the pollutants removal in wastewater. The present study aimed to assess the efficiency of utilizing tea waste biochar as an adsorbent for acenaphthene. The biochar was synthesized through the process of pyrolysis, therefore turning waste tea into a valuable form of biochar. During the adsorption procedure the analysis of acenaphthene was conducted using High Performance Liquid Chromatography. Three controlled factors were used to determine the efficiency of the adsorbent material: pH value, contact time (in min), and dosage of the biochar (in mg/L). The response surface methodology and artificial neural network were used to determine the optimal settings of factors. The findings of the study indicate that tea waste biochar exhibited a significant capacity for adsorption by achieving a 99.95% removal percentage of acenaphthene, making it a promising and effective adsorbent. The optimal pH for this adsorption process was determined to be 5.4, while the ideal contact duration was found to be 12.8 min. Additionally, the optimum dosage of the adsorbent was determined to be 185 mg/L. Both models performed well for the optimization of parameters. Artificial neural network was less complex and needed less computation time compared to response surface methodology.
Application of Artificial Neural Network and Response Surface Methodology in Adsorption of Acenaphthene Using Tea Waste Biochar
Lecture Notes in Civil Engineering
Mansour, Yasser (editor) / Subramaniam, Umashankar (editor) / Mustaffa, Zahiraniza (editor) / Abdelhadi, Abdelhakim (editor) / Ezzat, Mohamed (editor) / Abowardah, Eman (editor) / Raza Ul Mustafa, Muhammad (author) / Anuar Shah, Nur Afiq Arif Shah Bin (author) / Khurshid, Hifsa (author) / Kilic, Zeyneb (author)
Proceedings of the International Conference on Sustainability: Developments and Innovations ; 2024 ; Riyadh, Saudi Arabia
2024-11-17
9 pages
Article/Chapter (Book)
Electronic Resource
English
Artificial neural networks , Response surface method , Wastewater , PAHs , Micropollutants Engineering , Building Construction and Design , Geoengineering, Foundations, Hydraulics , Sustainable Architecture/Green Buildings , Engineering Economics, Organization, Logistics, Marketing , Energy Policy, Economics and Management , Renewable and Green Energy
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