Journal of University of Babylon

Combined power plants are more efficient in converting thermal energy into electrical ‎energy due to the advancement of technology in the world. A simple gas turbine unit is ‎integrated with a steam unit to form a combined cycle unit, utilizing a Heat Recovery Steam ‎Generator (HRSG) to recover waste heat from the gas turbine exhaust and generate additional ‎power without additional fuel consumption The present study aims to model a simple gas turbine ‎power generation plant along with a single-pressure Heat Recovery Steam Generator (HRSG) ‎and conduct a thermal analysis under varying operating conditions, specifically ambient ‎temperature and atmospheric pressure (1 bar). The study focuses on the gas power generation ‎plant in the Al-Qayyarah district, south of Mosul, which is designed with a generation capacity ‎of up to (125 MW). The objective is to determine the operational parameters of the single-‎pressure HRSG in relation to its engineering design, should the gas turbine plant be converted ‎into a combined cycle unit. This analysis considers energy balance, the mass flow rate of exhaust ‎gases from the gas turbine, and the steam production rate from the HRSG. The results indicate ‎that an increase in ambient temperature at constant atmospheric pressure negatively impacts the ‎performance of the simple gas turbine unit. Specifically, thermal efficiency, power generation ‎capacity, and exhaust gas mass flow rate decrease, while exhaust gas temperature and fuel ‎consumption rate increase. The results also showed that increasing ambient temperature ‎negatively affects the performance of the combined cycle power generation unit by reducing the ‎gas turbine power output due to lower air density. However, it leads to an increase in exhaust gas ‎temperature, which may slightly improve HRSG effectiveness and steam production but ‎generally results in a decline in overall cycle efficiency and generating capacity. Analysis of the ‎results indicates that a significant amount of thermal energy, approximately 60%, is lost with the ‎exhaust gases from the combustion process in the gas turbine unit. However, this waste heat can ‎be recovered and utilized to generate additional power and improve overall thermal efficiency by ‎integrating the gas turbine with a steam unit through a Heat Recovery Steam Generator (HRSG), ‎forming a combined cycle power generation system.‎

Heavy metals found in industrial effluent can pose serious threats to the human health ‎and environment, Because of their ecological durability and toxicity. One crucial stage in the ‎wastewater treatment is the removal of these contaminants. Of the several methods employed, ‎adsorption is found to be a cost‏ ‏effective‏ ‏method for heavy metals removal. This procedure ‎depends on sorbents—natural or synthetic—that can attach to metal ions and extract them from ‎water. This study focused on reviewing the role of researchers in finding inexpensive adsorbent ‎materials, environmentally appropriate sorbents, particularly for natural materials and agricultural ‎and industrial wastes. These materials' availability, affordability, and high adsorption efficiency ‎of define them.‎‏ ‏Demonstrating their capacity to extract heavy metals from effluent from ‎industries. pH, contact time, concentration of metals, temperature, and the adsorbent dose ‎amount were among the influencing parameters that were examined. Every one of these elements ‎is essential in establishing the total adsorption capacity. Among the models most commonly used ‎by researchers to study adsorption behavior, the Langmuir and Freundlich models are the most ‎popular and widely used in environmental and industrial applications. The Langmuir model is ‎characterized by its ability to describe adsorption on a homogeneous surface in a single layer ‎formation, making it ideal for determining the maximum adsorption capacity, which is suitable ‎for many materials with specific adsorption sites. For the reundlich model, is characterized by its ‎flexibility in dealing with heterogeneous surfaces and multilayer adsorption interactions, making ‎it suitable for systems of a complex nature. The popularity of these two models is due to their ‎simplicity and accuracy in representing experimental data in a wide range of applications. ‎

Microgrids improve economic benefits and environmental sustainability by utilizing ‎distributed generation (DG) from renewable sources like wind and solar. However, there are ‎technological difficulties in integrating these technologies, especially when it comes to energy ‎quality and voltage regulation. Careful management is necessary to address problems including ‎voltage instability, inverter harmonics, and frequency oscillations brought on by low inertia in ‎renewable DG systems. Stable operation can be ensured by addressing these issues with ‎advanced technologies like as FACTS devices, harmonic filters, and Dynamic Voltage Restorer ‎‎(DVR). Despite the unpredictability of renewable energy sources, real-world case studies from ‎California, Denmark, and India demonstrate how well these solutions work to ensure voltage and ‎frequency stability. Furthermore, energy storage systems (ESS) like supercapacitors and batteries ‎play a critical role in stabilizing voltage and frequency, ensuring reliable microgrid performance.‎

‎ Physical vapor deposition (PVD) methods have attracted more attention through recent ‎years. PVD methods have applied in various fields such as electronic devices, optical coatings, ‎biomedical implants, aerospace applications and decorative parts. The principle of PVD method ‎is depositing a thin film by intensifying material‏ ‏from the target on the substrate in vacuum or ‎inert gas. This article reviews PVD methods with respect to principles, types, characteristics, and ‎field of application. Three methods of PVD, such as thermal evaporation, sputtering, and pulsed ‎laser deposition will be demonstrated with details.‎

The dramatic evolution of limited-resource hardware such as microcontrollers in ‎conjunction with the development of machine learning algorithms has helped open doors to ‎building smart devices in industrial sectors. Predictive maintenance is one of these sectors that ‎uses smart devices efficiently. Vibration data such as data collected from accelerometers can ‎capture precisely any change in moving machine behavior due to mechanical wearing in moving ‎parts such as bearings. In this work, a deep learning one-dimensional convolutional neural ‎network (1DCNN) classifier is built as a fault detector and tuned to enhance detection ‎performance. ‎‏ ‏This classifier is tested using a publicly available vibration dataset for three types ‎of rotating bearing status (healthy, inner race fault, and outer race fault). The classifier is ‎designed using a tiny machine-learning framework, which can be implemented using a ‎microcontroller with limited resources. The accelerometer data is preprocessed using a ‎spectrogram of a vibration signal to extract frequency-time-related features to enhance classifier ‎performance. Moreover, the classifier is quantized using an eight-bit integer to reduce calculation ‎time and required memory. TinyML framework environment handles the building of the ternary ‎classifier and helps to implement this classifier on limited resources hardware. This ternary ‎classifier achieves accurate results with an accuracy of 98.64% and F1 score of 0.99. However, ‎these accurate results were achieved using minimal resources of an Arduino microcontroller with ‎a RAM of 8.3kb and a latency of 20ms. ‎