Advanced antifouling performance of PSF HNT Al2O3 GO membranes through a synergistic approach using nanocomposite tuning and machine learning - Mohammad, Suleiman Ibrahim; Owida, Hamza Abu; Vasudevan, Asokan; Arishi, Ali; Shaaban, Shaaban M.; Sammen, Saad Shauket; Salem, Ali (Frontiers in Environmental Science, 2025)
IntroductionThis work examines the antifouling characteristics of a new polysulfone (PSF) ultrafiltration membrane augmented with halloysite nanotubes (HNT) and Al2O3-GO.MethodsThe 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.ResultsInterestingly, 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 1 wt% 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.DiscussionWe 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.