The purpose of this research is to investigate different milling parameters for optimization to achieve the maximum rate of material removal with the minimum tool wear and surface roughness. In this study, a tool wear factor is specified to investigate tool wear parameters and the amount of material removed during machining, simultaneously. The second output parameter is surface roughness. The DOE technique is used to design the experiments and applied to the milling machine. The practical data is used to develop different mathematical models. In addition, a single-objective genetic algorithm (GA) is applied to numerate the optimal hyperparameters of the proposed adaptive network-based fuzzy inference system (ANFIS) to achieve the best possible efficiency. Afterwards, the multi-objective GA is employed to extract the optimum cutting parameters to reach the specified tool wear and the least surface roughness. The proposed method is developed under MATLAB using the practically extracted dataset and neural network. The optimization results revealed that optimum values for feed rate, cutting speed, and depth of cut vary from 252.6 to 256.9 (m/min), 0.1005 to 0.1431 (mm/rev tooth), and from 1.2735 to 1.3108 (mm), respectively.

In recent years, the focus of research is mainly on the twisted pipe and ducts, as it can significantly improve heat transfer characteristics of thermal exchangers in compact and highly effective heating systems. The conducted research seeks the impact of different twisting angles (90°, 180° and 360°) on surface temperature and Nusselt number, which allow describing effectiveness of heat transfer by convection. The analysis reveals that twisted ducts generate the secondary flows that displace the thermal boundary layer and result in higher heat transfer rates. Even 90° of twisting offer an impressive enhancement when compared to plain ducts, while 180° and 360° of twisting lead to the best results.

Hydrogen plays a vital role as an energy carrier in the global effort to combat climate change, with significant applications in sectors such as transportation and ammonia production. However, traditional hydrogen production methods are heavily carbon-intensive, with over 98% of hydrogen derived from fossil fuels. This primarily occurs through steam methane reforming (76%) and coal gasification (22%). While steam methane reforming is cost-effective, it generates approximately 9 kg of CO₂ per kg of hydrogen. Consequently, advancing green hydrogen production methods has become a critical area of research. This study explores and compares various green hydrogen production techniques powered by renewable energy sources, including solar, wind, hydro, biomass, and hybrid systems. Production methods such as electrolysis, thermal, chemical, photonic, and biological processes are evaluated using a Pugh matrix, accounting for factors including efficiency, hydrogen yield, resource availability, operating conditions, cost, and greenhouse gas emissions. The findings indicate that alkaline electrolysis currently represents the most viable option for green hydrogen production. These findings affirm alkaline electrolysis as the most appropriate near-term technology for large-scale green hydrogen implementation in Oman and the GCC, while also advocating for the ongoing development of PEM and emerging pathways to ensure long-term diversification. Ultimately, this study provides a clear and practical decision-support framework for the strategic selection of hydrogen technologies in renewable-rich arid regions.