This paper first describes a slow catalytic pyrolysis process used for synthesizing biodiesel from waste cooking oil (WCO) as a feedstock. The influence of variations in the catalyst type (sodium hydroxide and potassium hydroxide), and catalyst concentration (0.5, 1.0, 3.0, 5.0, 7.0 and 10.0% by weight) on both the pyrolysis temperature range and biodiesel yield were investigated. The results suggested that sodium hydroxide (NaOH) was more effective than potassium hydroxide (KOH) as catalysts and that the highest yield (around 70 wt.%) was observed for a NaOH concentration of about 1 wt.% The resultant pyrolysis temperature range was also significantly lower for NaOH catalyst, thus suggesting overall lower energy consumption. Compared to conventional diesel, the synthesized biodiesel exhibited relatively similar physical properties and calorific value. The biodiesel was subsequently blended with diesel fuel in different blend ratios of 0, 20, 40, 60, 80 and 100% by volume of biodiesel and were later tested in a compression ignition engine. Brake thermal efficiency and specific fuel consumption were observed to be worse with biodiesel fuel blends particularly at higher engine load above 50%. However, NOx emission generally decreased with increasing blend ratio across all engine load, with greater reduction observed at higher engine load. Similar observation can also be concluded for CO emission. In contrast, lower hydrocarbon (HC) emission from the biodiesel fuel blends was only observed for blend ratios no higher than 40%. Particulate emission from the biodiesel fuel blends did not pose an issue given its comparable smoke opacity to diesel observed during the engine test. The in-cylinder peak pressures, temperature and heat release rate of biodiesel fuel blends were lower than diesel. Overall, biodiesel fuel blends exhibited shorter ignition delays when compared to diesel fuel.

Multifunctional membrane technology has gained tremendous attention in wastewater treatment, including oil/water separation and photocatalytic activity. In the present study, a multifunctional composite nanofiber membrane is capable of removing dyes and separating oil from wastewater, as well as having antibacterial activity. The composite nanofiber membrane is composed of cellulose acetate (CA) filled with zinc oxide nanoparticles (ZnO NPs) in a polymer matrix and dipped into a solution of titanium dioxide nanoparticles (TiO₂ NPs). Membrane characterization was performed using transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR), and water contact angle (WCA) studies were utilized to evaluate the introduced membranes. Results showed that membranes have adequate wettability for the separation process and antibacterial activity, which is beneficial for water disinfection from living organisms. A remarkable result of the membranes’ analysis was that methylene blue (MB) dye removal occurred through the photocatalysis process with an efficiency of ~20%. Additionally, it exhibits a high separation efficiency of 45% for removing oil from a mixture of oil–water and water flux of 20.7 L.m⁻² h⁻¹ after 1 h. The developed membranes have multifunctional properties and are expected to provide numerous merits for treating complex wastewater.

Large-eddy simulation (LES) of wind farms is often limited by the computational cost required to represent many turbine rows and to obtain statistically converged wake and power statistics. Here, we present LES of an extended wind-farm configuration using the PALM model system, where cyclic lateral boundary conditions are employed to emulate interior-farm interaction in an idealized neutral boundary layer. The setup consists of nine identical horizontal-axis wind turbines arranged in a staggered array within the computational domain. Time-averaged hub-height fields show coherent wake corridors with a mean inflow-speed reduction of $23.7$% (array-mean across turbines) relative to an undisturbed background wind speed, and peak wake deficits reaching $71.4$% in the near-wake region. Turbulence levels increase markedly in the wake shear layers, with hub-height turbulence intensity enhanced by $32.2$% in the rotor region compared to background conditions; correspondingly, the peak hub-height SGS-TKE increases by a factor of 6.74 relative to background. Vertically averaged profiles indicate a momentum deficit within the turbine layer and gradual recovery aloft; the streamwise turbulent momentum flux remains predominantly negative, demonstrating the downward transport of higher-momentum air from above as a key recovery mechanism. Turbine rotor-power statistics show an initial adjustment followed by a quasi-stationary regime, with a farm-mean rotor power of $1.93$ MW and persistent inter-turbine variability characterized by a mean coefficient of variation of $61.2$%. Overall, the results demonstrate that the proposed extended-farm LES approach enables computationally efficient quantification of wake dynamics, vertical momentum transport, and their impact on power variability under idealized neutral wind-farm conditions.