Performance of functionalized CNT membranes for desalination - Parametric effects and Artificial neural network modelling

Abstract

Desalination, decisive for mitigating global water scarcity, faces challenges due to the high energy consumption and operational costs associated with traditional methods like reverse osmosis and distillation. The present research investigated the efficiency of eight combinations of fabricated single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) composite membranes with -OH, -COOH, and -NH₂ functionalities for the removal of salt under varying flow rates (100, 150, 200, and 250 ml/h) and influent (2500, 3000, 4000, 5000 mg/l) rates by membrane filtration. Isothermal analysis was conducted to evaluate the membranes' performance in removing dissolved sodium chloride in de-ionized water. Efficient salt removal was observed with amino-functionalized SWCNTs (84 % salt rejection with 2500 mg/l feed) compared to other functionalized MWCNTs at a flow rate of 200 ml/h. Among the two isotherms, Langmuir isotherm fitted the experimental data better than the Freundlich equation. An Artificial Neural Network (ANN) model was used to predict the behaviour of the membranes under different conditions. The model's predictions closely aligned with the observed experimental outcomes, affirming its reliability and utility in optimizing membrane performance. While amino-functionalized SWCNTs outperformed MWCNTs in desalination applications, potential challenges related to scalability and long-term stability were identified. Future work will explore these aspects to enhance practical applicability and cost-efficiency in large-scale operations.