Advanced Antifouling Performance of PSF HNT Al₂O₃ GO Membranes through a Synergistic Approach Using Nanocomposite Tuning and Machine Learning

Suleiman Ibrahim Mohammad^1,2Hamza Abu Owida³Asokan Vasudevan^2,4,5ALI ARISHI⁶Shaaban M Shaaban⁷Saad Shauket Sammen⁸Ali Salem^10,9*

• ¹Zarqa University Faculty of Economics and Business Administrative Sciences, Az-Zarqa, Jordan
• ²INTI International University, Nilai, Malaysia
• ³Al-Ahliyya Amman University, Amman, Jordan
• ⁴Shinawatra University, Sam Khok District, Thailand
• ⁵Wekerle Sandor Uzleti Foiskola, Budapest, Hungary
• ⁶King Khalid University, Abha, Saudi Arabia
• ⁷Northern Border University, Arar, Saudi Arabia
• ⁸University of Diyala, Baqubah, Iraq
• ⁹Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary
• ¹⁰Minia University, Minya, Egypt

This work examines the antifouling characteristics of a new polysulfone (PSF) ultrafiltration membrane augmented with halloysite nanotubes (HNT) and Al2O3-GO. The membrane was prepared by phase separation with different PSF and HNT/Al2O3-GO concentrations. Protein removal, resistance to sedimentation, and pure water flow were used to evaluate the membrane's performance. After first being modified with Al2O3-GO, the HNT nanoparticles were examined by X-ray diffraction (XRD), which was used to assess the membrane's structure. Interestingly, we found that at 6 bar pressure, the water flow increased significantly from 190 to 360 𝐿/(𝑚² ℎ), which we ascribed to the functional groups in the Al2O3-GO and HNT nanoparticles. A mix of bovine serum albumin (BSA) was used to test how resistant membranes were to fouling. Membranes with 0.75 and 1wt% HNT were the most antifouling and recovered 100% water flow. We also proposed a double-factorial data relationship method for finding the optimal parameter ranges. We employed machine learning methods, including support vector regression (SVR), random forest (RF), and artificial neural network (ANN), to predict bovine serum albumin (BSA) removal and spread. To optimize outcomes, we modified hyperparameters. This paper discusses how nanomaterials and cutting-edge computer approaches may enhance membrane filter systems.