Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs

Molecules

Nuruol Syuhadaa Mohd¹  Rusul Khaleel Ibrahim¹  Lai Sai Hin¹  Ahmed Elshafie¹  Haitham Abdulmohsin Afan¹  Mohammed Abdulhakim AlSaadi²  Ali Najah Ahmed³  Shaliza Ibrahim⁴  Chow Ming Fai⁵  Seef Saadi Fiyadh⁶ 

[1] Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia;Department of Materials Science and Metallurgy, University of Nizwa, Birkat Al Mawz 616, Oman;Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia;Institute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 50603, Malaysia;Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia;Nanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur 50603, Malaysia;

关键词: water quality;    deep eutectic solvents;    carbon nanotubes;    feedforward back propagation neural network;    adsorption;   

DOI  :  10.3390/molecules25071511

来源: DOAJ

【 摘 要 】

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R²) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10⁻⁵. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R² of 0.99.

【 授权许可】

Unknown