Machine learning modeling of polycarbonate ultrafiltration membranes at different temperatures, Al2O3 nanoparticle volumes, and water ratios

Generation of bio-energy after optimization and controlling fluctuations using various sludge activated microbial fuel cell

An aerobic microbial fuel cell (MFC) was designed to produce bio-electricity using cow manure-pretreated slurry (CM) and sewage sludge (SS). A comparative study of parametric effects on power generation for various parameters like feed ratio of wastes, pH of anode media, and electrode depth was conducted. This experiment aimed to identify the most important system parameters and optimize them to develop a suitable controller for a stable output. Power production reached its maximum at an electrode depth of 7 cm, a pH of 6, and a feed ratio of 2:1 in the CM + SS system before applying the controller. Response surface methodology (RSM) was practiced to explore the relationships between various parameters and the response using MINITAB software. The regression equation of the most productive system deduced from the RSM result has an R2 value of 85.3%. The results show that an ON/OFF controller works satisfactorily in this study. The highest energy-generating setup has a chemical oxygen demand (COD) removal efficiency of 45%. The morphology and content of the used wastes indicate that they can be recycled in other applications.

Curve fitting model of Polycarbonate Al2O3-nanoparticle membranes for removing emerging contaminants from wastewater: Effect of temperature and nanoparticles

The purpose of this paper is to demonstrate the use of the phase separation procedure in order to synthesize ultrafiltration polycarbonate containing aluminum oxide (Al₂O₃) nanoparticles (NPs) to remove emerging contaminants from wastewater at varying temperatures and nanoparticle contents. In the membrane structure, Al₂O₃-NPs are loaded at rates of 0≤φ≤1% volume. Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were used to characterize the fabricated membrane containing Al₂O₃-NPs. Nevertheless, volume fractions ranged from 0 to 1% during the experiment, which was conducted between 15 and 55 °C. An analysis of the ultrafiltration results was conducted by using a curve-fitting model to determine the interaction between these parameters and the effect of all independent factors on the emerging containment removal. Shear stress and shear rate for this nanofluid are nonlinear at different temperatures and volume fractions. Viscosity decreases with increasing temperature at a specific volume fraction. In order to remove emerging contaminants, a decrease in viscosity at a relative level fluctuates, resulting in more porosity in the membrane. NPs become more viscous with an increasing volume fraction at any given temperature on the membrane. For example, a maximum relative viscosity increases of 34.97% is observed for a 1% volume fraction at 55 °C. A novel model is then used to measure the viscosity of nanofluid. This indicates that the results and experimental data are in very close agreement, as the maximum deviation is 2.6%.