Engineering Innovations Vol. 18

Abstract: The formation of high-quality ohmic contacts on the backside of thinned SiC wafers is critical for vertical power devices to minimize specific on-resistance and enhance energy efficiency. Conventional green laser (532 nm) annealing for Ni-based backside metallization faces challenges such as severe carbon out-diffusion, interfacial voids, and high contact resistivity. This work introduces a Ni/Ti composite metallization scheme combined with 355 nm ultraviolet laser annealing (UV-LA) to address these limitations. By replacing the Ni single-layer with a Ni/Ti stack layer, the reflectivity at 355 nm UV laser annealing is reduced, enabling efficient energy absorption and localized alloying. Ti acts as a diffusion barrier, suppressing Kirkendall void formation and immobilizing carbon through in-situ TiC formation, as confirmed by XRD analyses. Additionally, UV-LA at 4.2 J/cm² with Ni/Ti composite metallization optimizes reaction kinetics, achieving a 69% reduction in void density and a 65% improvement in alloy layer flatness compared to Ni alloy layer. The results validate Ni/Ti-UV-LA as a scalable solution for high-reliability SiC backside metallization, paving the way for next-generation power devices.

Abstract: This research focuses on the development of a 3-in-1 personal protective equipment (PPE) design, which includes a safety helmet, visor, and earplugs, specifically aimed at mining applications. The objective of this study is to identify and address unique safety risks in mining environments, such as impact hazards, high noise levels, and particle exposure. The research methodology involves ergonomic dan analysis HIRADC (Hazard Identification, Risk Assessment, and Determining Control) to identify potential failures and risks in the design. Additionally, field surveys and interviews with stakeholders, including miners and safety experts, were conducted to understand the needs and challenges faced on-site. The results yield a 3-in-1 PPE design that integrates safety, functionality, and comfort, focusing on comprehensive impact protection, incorporating a scratch-resistant visor and adjustable earplugs. Performance evaluation was carried out through simulation testing and field prototypes. Overall, this design development aims to enhance the effectiveness of worker protection in mining environments while considering ergonomic aspects for user comfort. Furthermore, this research contributes positively to the innovation of PPE in the mining sector, aligning with Sustainable Development Goals (SDGs) related to industry, innovation, and infrastructure (SDG 9), by creating more efficient and safe solutions to protect workers from potential risks in high-risk workplaces.

Abstract: Environmental concerns have driven the quest for clean energy solutions, with green hydrogen emerging as promising choice. This paper underscores various production methods for green hydrogen, examining their relevance and providing an overview of the utilization of Morocco's renewable energies in its production. Key challenges will be given, including water scarcity, storage, and transportation. Overall, this paper delivers a comprehensive assessment of the role of green hydrogen in Morocco’s energy transformation.

Abstract: The precision in temperature estimation plays a pivotal role in the design and operational efficiency of CubeSats. This study leverages the capabilities of COMSOL MULTIPHYSICS to model the thermal behavior of a 1U CubeSat, with a focus on evaluating the impact of orientation and beta angle on heat transfer dynamics and the resultant temperature distribution throughout the satellite. By conducting an extensive range of simulations that explore beta angles from 0° to 90° across four distinct satellite orientations, this research uncovers critical insights into the heat transfer mechanisms within the CubeSat framework. These findings illuminate the substantial influence of orientation and beta angle on the satellite's thermal state, highlighting the necessity of incorporating these factors into any comprehensive thermal analysis of spacecraft. The outcomes of this investigation not only contribute to a deeper understanding of CubeSat thermal management but also underscore the importance of meticulous design and analysis practices to optimize satellite performance in the challenging space environment.

Abstract: Across the globe, the energy landscape is fast changing due to advancements in semiconductor materials, widespread application of artificial intelligence, wireless communication systems, and cybersecurity protocols. These infrastructures have provided leverage for the transition of present microgrid systems to emerging cyber-physical microgrids. It is on these premises that this present work examines AI-driven inverter systems enhanced with a wireless communication system and advanced control strategies. Also, following a careful assessment of the evolutionary trends and inverter topologies, an architecture for an AI-driven inverter system was proposed, which provided a platform for establishing a linkage between cybersecurity concepts and AI-driven inverter systems. Furthermore, premium emphasis was equally placed on the possible mitigating strategies for cyber threats that could result from adopting wireless communication techniques for data transmission in AI-driven inverters to the distributed energy resources in the microgrid systems. Also, the potential impacts and future outlooks towards the development of AI-assisted inverter systems and cyber-physical microgrid systems for sustainable power supply were comprehensively discussed. The potential impacts of emerging cyber-physical microgrid systems and AI-driven inverter systems on sustainable power supply have been extensively discussed, which is one of the key contributions of this work.

Abstract: The foundation of power system reliability is voltage stability, which is required to promise a secure and stable supply of electricity. Insufficient generating capacity, a timeworn transmission facility, inadequate reactive power compensation, and the increasing integration of renewable energy sources are the main foundations of the existing voltage instability of Nigeria's power grid, specifically in the northern regions. Through evaluating contemporary disturbances such as smart grid technology and intelligent machine learning (ML) drives up for real-time voltage security evaluation and predictive analytics, this study provides a technically enhanced examination of these effects. Contrasting machine learning models, such as deep learning (DL), supervised learning, unsupervised learning, and reinforcement learning (RL), are explored for their capabilities in time-varying voltage prediction, robotic grid control, and anomaly detection. Also, it highlights the transformative impact of machine learning in improving voltage stability management and outlines strategic recommendations relating to guiding principle reforms and infrastructure transformation. The article intends to provide a forward-looking structure for deploying adaptive Machine stakeholder engagement e-learning-powered solutions to achieve resilient and voltage security in the Nigerian power system that structures long-term economic sustainability.

Abstract: This work determines the reliability indices of the Nigeria 330kV electric power network, which is susceptible to disturbances. Besides, the network configuration is inadequate as it is vulnerable, resulting in transmission line outages. The cardinal object, therefore, is to benchmark the indices against established standards to enable effective operational improvement planning. First, a simulation was conducted using the Electrical Transient Analyzer Program (ETAP) and validated with the Power System Simulator for Engineering (PSS/E) software to assess bus voltages, line flows, and system losses. Subsequently, the ETAP software was applied to determine reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Energy Not Supplied (AENS) and Expected Energy Not Supplied (EENS). The simulation results obtained for SAIFI, SAIDI, CAIDI, and ASAI on the test network are 3.2684 f/customer. yr, 9.4140 hours per customer in a year, 2.880 hours per customer interruption, and 0.9989 respectively. Likewise, the AENS with gave a high value of 1360.9340 MWh/customer. yr indicating that on the average, customer is are expected to lose access to 1360.9340 MWh of energy annually. Furthermore, the high value of EENS estimated at 55,798.300 MWh/yr means that the power system is expected to fail to supply 55,798.300 MWh of electricity in one year due to various incidents of failure. These values were compared with the standard IEEE values and were found to be outside the threshold; thus, making it imperative that the indices be utilized to undertake further work that would result in improved and efficient operation of the national grid.

Abstract: This paper presents a theoretical framework for managing spare parts inventory for aging rotating equipment in the oil and gas industry, focusing on challenges such as unpredictable demand, long lead times, and obsolescence risks. Traditional inventory methods, such as ABC analysis and Economic Order Quantity (EOQ), are insufficient for handling these complexities. The framework integrates demand characteristics, such as usage frequency and criticality, with predictive maintenance and continuous review policies. Data from maintenance management systems also play a critical role in developing the spare parts control policy. Based on interviews with maintenance experts and inventory analysts, the study findings confirm that unpredictable demand and long lead times are significant challenges. Additionally, flexible contracts and integrated planning between maintenance, inventory control, and suppliers are paramount. Future research should explore dynamic sourcing strategies and machine learning to enhance forecast accuracy and process automation in spare parts management.