Journal of Engineering in Industrial Research

Abstract The polluting gas flow and emission levels from Al-Khums electric power generating plant as well as material and energy balance were estimated using the Aspen HYSYS V9.0 simulator. These model simulations were performed under the actual operating conditions of the power plant and at steady state condition for each unit in the power plant.  The power plant units targeted for the simulator are: air compressor, gas compressor, pressure release valve, combustion chamber, and the gas turbine. The concentration levels of the two major pollutants under investigation, namely, carbon dioxide (CO₂) and nitrogen oxides (NO_x) were estimated and quantified using the well-known air dispersion model (Gaussian plume type model). Air dispersion model results revealed that the concentration of NO_x and CO₂ are equal to 2367.79 mg/m³ and 28,683.18 mg/m³, respectively. These values, when compared with the allowable international standards, were found far to exceed the limits for air pollutants emitted from power generating plants. These values are specific to the meteorological conditions of the site location under the investigation. They may change once the site conditions are changed. The air dispersion model was also used to determine the concentration and the horizontal distance a pollutant can reach from the point source. The results of this air dispersion model, which is highly dependent on the climate and meteorological conditions of the region where the power generating plant is located, revealed that high level concentrations of pollutants can reach a horizontal distance of 22 km from the point source of pollution. According to the World Health Organization (WHO) and United States Environmental Protection Agency (USEPA), these higher concentration levels and this specific horizontal distance may adversely affect the air quality of the environment and cause health hazards to inhabitants in the region.

Abstract This study aims to identify a baseline model for optimizing deep neural network (DNN) models for deployment in resource-constrained environments (RCE). Although DNNs are excellent in many applications, their deployment on devices like wearables and mobile phones presents significant challenges. The study investigates six popular DNN models, including MobileNet (V1 and V2), ResNet50, InceptionV3, DenseNet121, and EfficientNetB1. To assess each model's advantages, disadvantages, usability, and effectiveness in RCE scenarios, a comprehensive review and empirical analysis were conducted. The analysis focuses on optimizing these models to function effectively given the limited computational power and memory of RCE devices. Key factors such as model size, computational complexity, and inference speed are examined to uncover performance trade-offs between accuracy and resource efficiency. The findings suggest that MobileNetV1 should serve as baseline models for building efficiency-focused DNN models for image classification on RCE devices. This recommendation is based on MobileNetV1's balance between performance and efficiency, making it an ideal starting point for further optimization.

Abstract Energy management changes the electricity consumption pattern of customers. This change is done to achieve the optimal consumption curve. Using energy management, by reducing consumption in periods, besides the appropriate load curve, it reduces the cost of operation and planning. Electricity supply in traditional networks is done by large power plants that are concentrated in certain places. The generated energy must be transferred to consumption points by transmission and distribution networks. The above-mentioned power system has many problems, among which we can mention the decrease in reliability and availability due to the wear and tear of the electrical system infrastructures and the imposition of high costs of losses in the energy transmission to the load points. In other words, management includes a set of activities that affect the pattern and amount of consumer load. Basically, consumption management programs aim to achieve various goals, the most important of which are improving the efficiency of energy systems, increasing the load factor, reducing the need for investment to build and postponing the construction of new power plants, reducing the effects harming the environment, reducing the cost of electricity supply to customers, compensating for the shortage of supply and reducing the excess demand for electricity, improving the reliability and quality of power, promoting the development of energy economy, creating a culture of saving and effective support for customers pointed out.

Abstract In the current study, the investigation of the secure communication network for the reliable use of micro-grids in the power grid has been investigated. Micro grid will have many benefits for both consumers and electricity companies. From the consumer's viewpoint, the micro grid has the ability to provide electricity and heat at the same time, increase reliability, reduce greenhouse gas emissions, improve quality, and from the viewpoint of power companies, the use of micro grids reduces consumption demand and therefore reduces the facilities for the development of transmission lines and in addition to That factor will be the removal of peak consumption points, which will also reduce network losses. On the other hand, the existence of distribution networks that are fed only through these transmission networks always exposes these networks to blackout and instability. One of the upcoming solutions to overcome these problems is the use of scattered energy sources. The limitation of fossil fuels and air pollution is one of the main incentives for the development of this technology. Producing electricity near the place of consumption, in addition to reducing losses in the system, can create more flexibility to provide various services to consumers. One of the methods of aggregating scattered production resources is a new concept called micro grid. During disruption and chaos in the network, the micro grid is separated from the distribution network and the island resulting from the disturbance in the power network is isolated.

Abstract This study examined the effects of joint design and post-weld heat treatment (PWHT) on the microstructural features and mechanical properties of low-carbon steel welded using the shielded metal arc welding (SMAW) process. Three joint configurations—bevel, butt, and half-lap—were welded under consistent parameters, with a subset of samples subjected to PWHT involving quenching in either used or unused oil baths. Mechanical testing, including tensile, hardness, and impact toughness tests, was conducted on both as-welded (AW) and PWHT-treated samples. Results showed that bevel joints exhibited the highest tensile, yield, and impact strengths, while butt joints demonstrated superior hardness. PWHT, particularly quenching in unused oil, significantly enhanced tensile strength, yield strength, and hardness, whereas quenching in used oil achieved the highest impact strength. Microstructural analysis of the bevel joints after quenching in unused oil, along with mechanical testing for tensile strength, yield strength, hardness, and impact toughness, revealed that PWHT notably improved mechanical performance compared to AW conditions. The bevel joints exhibited significantly higher tensile and yield strengths, as well as enhanced hardness, indicating the formation of a refined martensitic structure due to the quenching process. The study concluded that the choice of joint design and quenching medium can optimize welded joint properties for specific industrial applications. Bevel joints were recommended for high tensile and yield strength, whereas butt joints were preferable when hardness and toughness were essential.