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This article presents a modeling and analysis approach for a hybrid photovoltaic wind turbine (PV-WT) hydrogen production system. This study uses the TRNSYS simulation platform to evaluate the system under coastal climate conditions in Perth, Australia. The system encapsulates an advanced alkaline electrolyzer (ELE) and an alkaline fuel cell (AFC). A comprehensive 4E (energy, exergy, economic, and environmental) assessment is conducted. The analysis is based on hourly dynamic simulations over a full year. Key performance metrics include hydrogen production, energy and exergy efficiencies, carbon emission reduction, levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The TRNSYS model is validated against the existing literature data. The results show that the system performance is highly sensitive to ambient conditions. A sensitivity analysis reveals an energy efficiency of 7.3% and an exergy efficiency of 5.2%. The system has an entropy generation of 6.22 kW/K and a sustainability index of 1.055. The hybrid PV-WT system generates 1898.426 MWh of renewable electricity annually. This quantity corresponds to 252.7 metric tons of hydrogen production per year. The validated model shows a stable LCOE of 0.102 USD/kWh, an LCOH of 4.94 USD/kg, an energy payback time (EPBT) of 5.61 years, and cut CO2 emissions of 55,777.13 tons. This research provides a thorough analysis for developing green hydrogen systems using hybrid renewables. This study also offers a robust prediction model, enabling further enhancements in hybrid renewable hydrogen production.

Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.