Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management

Abdelfattah Amari; Mohammed Hasan Ali; Mustafa Musa Jaber; Velibor Spalevic; Rajko Novicevic

doi:10.3390/membranes13010031

Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management

Membranes (Basel). 2022 Dec 26;13(1):31. doi: 10.3390/membranes13010031.

Authors

Abdelfattah Amari^{1

2}, Mohammed Hasan Ali³, Mustafa Musa Jaber^{4

5}, Velibor Spalevic⁶, Rajko Novicevic⁷

Affiliations

¹ Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia.
² Research Laboratory of Processes, Energetics, Environment and Electrical Systems, National School of Engineers of Gabes, Gabes University, Gabes 6072, Tunisia.
³ Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja'afar Al-Sadiq University, Najaf 10070, Iraq.
⁴ Computer Techniques Engineering Department, Dijlah University College, Baghdad 10070, Iraq.
⁵ Computer Techniques Engineering Department, Al-Farahidi University, Baghdad 10070, Iraq.
⁶ Biotechnical Faculty, University of Montenegro, Mihaila Lalica 1, 81000 Podgorica, Montenegro.
⁷ Faculty of Business Economics and Law, Adriatic University, 85000 Bar, Montenegro.

Abstract

Water resources management is one of the most important issues nowadays. The necessity of sustainable management of water resources, as well as finding a solution to the water shortage crisis, is a question of our survival on our planet. One of the most important ways to solve this problem is to use water purification systems for wastewater resources, and one of the most necessary reasons for the research of water desalination systems and their development is the problem related to water scarcity and the crisis in the world that has arisen because of it. The present study employs a carbon nanotube-containing nanocomposite to enhance membrane performance. Additionally, the rise in flow brought on by a reduction in the membrane's clogging surface was investigated. The filtration of brackish water using synthetic polyamide reverse osmosis nanocomposite membrane, which has an electroconductivity of 4000 Ds/cm, helped the study achieve its goal. In order to improve porosity and hydrophilicity, the modified raw, multi-walled carbon nanotube membrane was implanted using the polymerization process. Every 30 min, the rates of water flow and rejection were evaluated. The study's findings demonstrated that the membranes have soft hydrophilic surfaces, and by varying concentrations of nanocomposite materials in a prescribed way, the water flux increased up to 30.8 L/m²h, which was notable when compared to the water flux of the straightforward polyamide membranes. Our findings revealed that nanocomposite membranes significantly decreased fouling and clogging, and that the rejection rate was greater than 97 percent for all pyrrole-based membranes. Finally, an artificial neural network is utilized to propose a predictive model for predicting flux through membranes. The model benefits hyperparameter tuning, so it has the best performance among all the studied models. The model has a mean absolute error of 1.36% and an R² of 0.98.

Keywords: artificial neural network; carbon nanotubes; desalination; machine learning; polyamide reverse osmosis membrane.

Grants and funding

RGP.2/182/43/Deanship of Scientific Research at King Khalid University