RESUMO
Polyfluoroalkyl and perfluoroalkyl (PFAS) chemicals are fluorinated and exhibit complicated behavior. They are determined and highly resistant to ecological modifications that render plants ecologically robust. Thermal stability and water and oil resistance are examples of material qualities. Their adverse consequences are causing increasing worry due to their bioaccumulative nature in humans and other creatures. Direct data indicates that PFAS exposure in humans causes endocrine system disruption, immune system suppression, obesity, increased cholesterol, and cancer. Several PFASs are present in drinking water at low doses and may harm people. These cancer-causing PFAS have caused concern for water bodies all around the globe. Analytical techniques are used to identify and measure PFAS in an aqueous medium (membrane). Furthermore, a deeper explanation is provided for PFAS removal methods, including mixed matrix membrane (MMM) technology. By removing over 99 % of the PFAS from wastewater, MMMs may effectively remove PFAS from sewage when the support matrix contains adsorbing components. Furthermore, we consider several factors affecting the removal of PFAS and practical sorption methods for PFAS onto various adsorbents.
Assuntos
Água Potável , Fluorocarbonos , Neoplasias , Poluentes Químicos da Água , Humanos , Águas Residuárias , Fluorocarbonos/análise , Poluentes Químicos da Água/análiseRESUMO
The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al2O3-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the thermophysical properties of the membrane. 5%-10% of Al2O3-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of Al2O3-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio. To determine the impact of Al2O3-NPs loading on the Response Surface Method (RSM), DOE, ANOVA, ANN, MLP, and NSGA-II, the number of aluminum oxide nanoparticles (Al2O3-NPs), temperature, and water/ethylene glycol (EG) on membranes in PC ultrafiltration are evaluated. Based on the Relative Thermal Conductivity Model (RSM), the regression coefficient of Al2O3 in water and EG was 0.9244 and 0.9170 with adjusted regression coefficients. A higher concentration of EG enhances the thermal conductivity of the membrane when the effective parameters are considered. The effect of temperature on the relative viscosity of the membrane led to the conclusion that Al2O3 water/EG can cool at high temperatures while providing no viscosity change. When Al2O3 is dissolved in water and EG, more EG is necessary to optimize the mode of reactivity. Using the MLP model, the calculated R-value is 0.9468, the MSE is 0.001752989 (mean square error), and the MAE is 0.01768558 (mean absolute error). RSM predicted the average thermal conductivity behavior of nanofluid better. The ANN model, however, has proven to be more effective than the RSM in simulating the relative viscosity of nanofluids. The NSGA-II optimized results showed that the minimum relative viscosity and maximum coefficient of thermal conductivity occurred at the lowest water ratio and maximum temperature.