Efficient design and operation of HVAC (Heating, Ventilation, and Air Conditioning) systems are crucial for energy savings and comfort in buildings. Head loss through duct fittings plays a significant role in the overall efficiency of conditioned air distribution systems, as it can increase fan energy consumption and reduce system performance. This study focuses on the statistical modeling of head loss in duct fittings, with the goal of optimizing HVAC system design. Using data from a series of experiments and simulations, we developed predictive models that estimate head loss in various duct fittings (e.g., elbows, tees, dampers) based on factors such as flow velocity, duct size, and fitting geometry. The study employs regression analysis and machine learning techniques to analyze the relationships between these variables and the resulting head loss. Results show that the proposed statistical models provide accurate and reliable estimates of head loss, offering insights for improving HVAC system design by selecting more efficient fittings and minimizing energy losses. The findings contribute to the development of more energy-efficient and cost-effective HVAC solutions, with implications for building energy management and sustainability.

The construction of road structures on soft soils is prone to structural damage due to the low bearing capacity of the soil under the load imposed by vehicles. Chemical stabilization is a popular method used to increase the bearing capacity of soft soils. This study aims to examine the effectiveness of soft soil stabilization using a mixture of lime, nickel slag, and aluminium hydroxide to enhance soil bearing capacity. The addition of lime as a binding agent is expected to reduce plasticity and increase soil strength, while nickel slag and aluminium hydroxide serve as additives that improve overall stabilization performance. The California Bearing Ratio (CBR) laboratory test was conducted by varying the proportions of stabilizing materials relative to the weight of the soft soil at its optimum moisture content. The lime addition variations used in this study were 2%, 4%, and 6%. The results showed that the lime, nickel slag, and aluminium hydroxide stabilization mixture significantly improved the soil's bearing capacity compared to untreated soil or soil stabilized only with nickel slag. The CBR value for soil stabilized with nickel slag, aluminium hydroxide, and lime reached 37.78% after 28 days of curing. This value is 7.6 times higher than that of natural soil and 1.3 times higher than soil stabilized with nickel slag alone. Thus, the use of a mixture of lime, nickel slag, and aluminium hydroxide is an effective method for increasing the bearing capacity of soft soils, making it applicable for road construction on soft soils requiring enhanced load-bearing capacity.

Optimal Power Flow (OPF) is a critical problem in power system operation and planning, aimed at determining the most efficient operational conditions of the system while respecting various operational constraints. Genetic Algorithms (GA), with their ability to solve complex optimization problems, have been increasingly employed to address the OPF problem. This study focuses on performing a parametric analysis to investigate the impact of selecting appropriate control and state variables on the efficiency and effectiveness of GA-based OPF solutions. Various combinations of control variables (such as generator voltages, active power generation, and reactive power generation) and state variables (such as bus voltages and branch power flows) are analyzed in this study. The results highlight how the selection of control and state variables influences the convergence rate, computational time, and solution accuracy of the genetic algorithm. A series of parametric studies are conducted to optimize the parameters of the genetic algorithm, including population size, crossover rate, and mutation rate, to improve the overall performance of the OPF model. The study demonstrates the significance of variable selection in achieving more efficient and practical solutions for power system optimization. The findings suggest that the choice of control and state variables plays a crucial role in balancing the trade-offs between solution quality and computational efficiency.