FACULTY OF ENGINEERING

Access to reliable and sustainable electricity remains a significant challenge in many rural areas worldwide. In addressing this issue, off-grid photovoltaic (PV) systems have emerged as a promising solution due to their environmental friendliness, scalability, and decreasing costs. This paper presents the design and implementation of an off-grid PV system tailored for optimal utilization in rural areas. With the aim of addressing the energy needs of a modern 2-bedroom apartment located in a rural community, this study emphasizes the development of a self-sustaining PV system capable of providing reliable electricity access independent of the traditional grid infrastructure. The design process begins with a comprehensive assessment of the energy needs and resource availability of the target rural community and in this case a 2-bedroom apartment. This assessment includes factors such as household electricity consumption patterns, local climate conditions, solar irradiance data, and geographical characteristics. Utilizing this data, the system's components are sized appropriately to meet the community's energy demands reliably. Since, our system will be independent of the traditional grid infrastructure, we made sure that our system will be able to sustain the energy requirement of the two-bedroom residential for an average of 19 hours, this led us to the following system requirement of 15,975W solar panel capacity, 915.2Ah battery capacity and a 4KVA inverter capacity, this translates to using 32 solar panels with each rated 500W, 16 batteries each 12V. The core components of the PV system include photovoltaic panels, charge controllers, batteries, inverters, and distribution systems. Each component is carefully selected based on efficiency, durability, and compatibility to ensure maximum system performance and longevity in rural settings. Furthermore, the implementation phase involves the installation and integration of the PV system into the 2-bedroom apartment. Monitoring and evaluation mechanisms were established to track the performance and impact of the PV system over time. In line with our study, we came to discover that the optimal tilt angle of solar panel in the University of Benin, Ugbowo campus (test location) was 20° which is in line with the reference angle of 0o to 42˚. A DC load test was carried out on the implemented PV system, resulting in a short circuit current of 1.45A and an open circuit voltage of 13.75V. Additionally, a charging test revealed that it took 6.86 hours to charge a 100Ah, 12V battery using a 200W solar panel operating at a voltage of 13.72V. Simulation of the proposed PV system was obtained using the PVSYST simulation software, from the simulation we discovered that the system produces an annual energy of 14,341kWh/year with a performance ratio of 0.515, the daily input/output graph also show variability which is connected to the irradiance and seasonal variations.

Induction motors, though robust, are prone to electrical and thermal stresses that can cause costly failures, while traditional protection devices are either too slow, prone to nuisance trips, or too expensive for small industries. The problem therefore lies in
the lack of an affordable, reliable, and adaptable protection system that integrates both electrical and thermal monitoring. The aim of the project is to design a micro- controller-based protection system for three-phase induction motors to detect faults
such as single-phasing, under voltage, and overheating. A functional protection system was built using the PIC16F877A micro-controller to achieve real-time monitoring and automatic motor isolation. The design employed ZMPT101B voltage sensors, an ACS712 current sensor, a DS18B20 temperature sensor, LM7805 regulator, ULN2003 driver, relay/contractor, and a 16×2 LCD. The
methodology involved circuit design and simulation, hardware assembly, and programming in Embedded C to process sensor data, and control the relay for fault response for phase failure or for temperature above 60 degrees. The performance of the system was rigorously evaluated through testing in both faulty and normal operating conditions. During fault simulation, the system
accurately identified phase loss, displaying "Phase Failure" on the LCD followed by the specific faulty phase voltages. When the motor temperature exceeded 60°C, the display indicated "Over Temp" and subsequently showed the actual real-time temperature measurement. Conversely, once the faults were cleared and the system was restored to normal operation (with phases at 220V and temperature below 60°C), the LCD confirmed that the Relay was switched ON, reconnecting the motor to the power source. Following this restoration, the system resumed its standard monitoring mode, displaying the actual temperature and operational parameters, thereby proving the system’s reliability in managing transitions between fault detection and safe recovery.

An Automated Residential Gate project aims to enhance security, convenience, and energy efficiency through the integration of automation and solar power technology. Traditional manual gates require significant human effort and are often inconvenient, especially for large or heavy gates. To address these issues, this project involves designing an automated sliding gate system controlled by remote access, keypads, and IOT connectivity. The system incorporates a D5V6 Smart Centurion Machine, a 60W solar panel, a 30A charge controller, and a deep-cycle battery to ensure uninterrupted operation, even during power outages. The design includes a 0.37 kW motor with a gearbox to enhance torque efficiency, along with infrared sensors for obstacle detection and limit switches for precise movement control. Safety features such as emergency manual release and predictive maintenance alerts further improve usability and reliability. Structural materials such as steel and corrosion-resistant components ensure durability under various environmental conditions. Through performance testing, the system demonstrated smooth operation, energy efficiency, and enhanced security compared to conventional gates. The solar-powered system effectively reduces reliance on grid electricity, making it a cost-effective and sustainable solution. Future improvements may include AI-driven security enhancements and higher-efficiency solar panels to further optimize performance.

Fire exposure destroys concrete structures, and the cooling methods significantly impacts residual strength Rapid cooling, especially with water, may cause additional damage due to thermal shock, yet limited studies compare air- and water- cooling effects. In order to determine which cooling technique best maintains structural integrity, this study will examine how various techniques affect the breaking strength of Grade 30 concrete exposed to temperatures of 200°C, 400°C, and 600°C. This study involves the preparation of Grade 10 concrete specimens, which were cured for 28 days before being subjected to elevated temperatures of 2000C 400C and 600°C in a controlled furnace. After exposure, the specimens were cooled using air and water to compare the effects of each method on compressive strength. The compressive strength of all samples was tested using a compression testing machine, and the results were analyzed through tabular and graphical comparisons to evaluate strength reduction trends. The study revealed that compressive strength decreased with increasing temperature, with watercooled samples experiencing greater strength loss than air-cooled due to rapid thermal shock. At 600°C, Average water-cooled samples record 26.561 N/mm², while air-cooled samples record 28.014 N/mm², confirming that gradual cooling helps to retain more structural integrity. Based on these findings, air cooling is recommended as a safer and more effective method for post- fire concrete recovery. Further research should explore advanced cooling techniques to enhance fire resistance and durability.

This study examines how curing reinforced concrete in a urea-salt solution, which simulates urine, affects its compressive strength and durability. This is compared to concrete cured in fresh water. The research addresses concerns about the decline of concrete structures in environments that are biologically or chemically harsh, such as areas often contaminated by urine. Understanding the impact of such exposure on concrete performance is important for improving the design and maintenance of durable structures under these conditions. The experimental work involved casting twenty concrete cubes, each measuring 100 × 100 × 100 mm, using a mix ratio of 1:2:4 and a water-cement ratio of 0.5. Ten cubes were cured in fresh water, while the other ten were cured in a urea-salt solution made with 10 g of urea and 2 g of sodium chloride (NaCl) per liter of water. The cubes were tested for compressive strength after 14 and 28 days of curing using a compression testing machine. The data gathered were analyzed and compared to evaluate the impact of the urea-salt solution on concrete performance. The average compressive strengths were 18.81 N/mm² and 23.17 N/mm² for the 14- and 28- day fresh-water samples, and 18.15 N/mm² and 17.81 N/mm² for the urea-salt-cured samples which indicates that concrete cured in fresh water showed normal strength growth with age. In contrast, the concrete cured in the urea-salt solution had a slight decrease in compressive strength over time. It was concluded that exposure to the urea-salt solution restricts full hydration and weakens concrete durability with extended contact. It is advised that structures in areas prone to urine contamination be shielded from direct exposure.

This study presents the design and fabrication of a Smart Internet of Thing (IoT)-based feul monitoring system for agricultural tractors. The system aims to improve operational efficiency, minimize fuel theft, and enhance real-time decision-making in mechanized farming. It integrates an ultrasonic fuel level sensor, NodeMCU V3 microcontroller, GPS, and GSM modules to provide continuous fuel data and location tracking. Using Blynk and Thing Speak IoT platforms, real-time fuel levels, consumption trends, and geographic positions were displayed through web and mobile interfaces. Calibration and testing revealed that the system achieved high measurement accuracy with an error margin of less than ±5%, Wi-Fi data transmission latency between 6–8 seconds, and SMS alert delay of 7–12 seconds. The prototype demonstrated effective performance under field conditions, withstanding vibration, heat, and moisture without data loss. Results confirm that the developed IoT-based system is affordable, reliable, and user-friendly for small- and medium-scale farmers. It enables efficient monitoring of fuel resources, enhances accountability, and supports preventive maintenance through analytics and alert mechanisms. Overall, the system bridges the technological gap in fuel management for agricultural operations in developing regions and contributes to sustainable mechanization practices.

This study investigates the environmental impact of the Ekosodin dumpsite in Benin City, Edo State, on surrounding groundwater quality, specifically addressing the risks of leachate infiltration. The research aim was to evaluate twenty-two physicochemical and microbial parameters across eight sampling locations to determine the spatial extent of contamination and assess the suitability of local water resources for domestic use. By benchmarking these parameters against World Health Organization (WHO) and Nigerian Industrial Standards (NIS), the study provides a comprehensive overview of how inadequate waste management practices threaten the availability of safe potable water for the community. The methodology integrated systematic laboratory analysis with advanced geospatial modeling using ArcGIS 10.8. Groundwater samples were collected from eight borehole locations and analyzed for various physical, chemical, and biological properties, including heavy metals like Lead (Pb) and Cadmium (Cd). A Water Quality Index (WQI) was calculated for each site to classify water quality, while Inverse Distance Weighting (IDW) interpolation was applied to map the spatial distribution of pollutants. Furthermore, a Multiple Linear Regression (MLR) model was developed to quantify the relationship between five key parameters—including Electrical Conductivity (EC) and Iron (Fe)—and the calculated WQI, achieving a high predictive accuracy with an R 2 value of 0.9983. Results revealed a significant degradation gradient, with WQI values ranging from 27.20% to 130.05% (and up to 945.24% in specific computations), indicating that boreholes closest to the dumpsite possess very poor water quality unsuitable for drinking. Spatial analysis confirmed the dumpsite as the primary source of elevated heavy metals and organic contaminants, though quality generally improves as the distance from the waste source increases. The study concludes that leachate from the Ekosodin dumpsite severely impairs groundwater safety, leading to the recommendation that future boreholes be sited at least 400 meters away from disposal areas. These findings emphasize the urgent need for modernized waste management strategies and continuous groundwater monitoring to protect public health and ensure a sustainable water supply.

Post-harvest losses remain a major challenge for fish and agricultural product processors in developing countries due to limited access to efficient drying and preservation technologies. Traditional methods such as open-sun drying expose products to contamination, weather variability, and non-uniform drying, resulting in significant quality degradation and economic loss. This project addresses these challenges through the design, fabrication, and performance evaluation of a hybrid gas and charcoal oven capable of drying fish and selected agricultural produce efficiently and hygienically. The hybrid system integrates two energy sources—liquefied petroleum gas (LPG) and charcoal—to provide operational flexibility, continuous heat supply, and improved temperature control. Locally available materials including mild steel, galvanized sheet metal, wire mesh trays, and glass wool insulation were used to ensure cost effectiveness, durability, and maintainability. The oven was tested with products such as catfish, pepper, and cassava chips. Performance parameters evaluated include temperature distribution, moisture reduction, drying efficiency, fuel consumption, and final product quality. Results revealed that the oven achieved a drying temperature range of 60–65°C with uniform heat distribution across trays. Moisture reduction from 72% to 12% for fish was attained within 6–7 hours under hybrid mode, compared to 10–12 hours in traditional charcoal dryers. A drying efficiency of approximately 60.7% was recorded, demonstrating significant improvement over conventional drying systems. The dried products showed enhanced sensory and hygienic quality with minimized contamination and discoloration. The study concludes that the hybrid oven is a practical, reliable, and sustainable technology for small- and medium-scale food processors, contributing to food security, reduction of postharvest losses, and socio-economic development. Further improvements such as automation of temperature control and incorporation of forced convection are recommended to enhance performance and commercial viability. Keywords: Hybrid oven, drying efficiency, fish preservation, agricultural products, gas and charcoal heating, post-harvest losses, food safety.

Solar photovoltaic (PV) technology is a critical low-carbon solution, but its performance is severely compromised by shading. This study addresses the persistent problem of partial shading, which causes disproportionate power losses and creates thermal stress risks like hot spots. This research aims to quantify the effect of shading on PV panel voltage, current, and power output under controlled laboratory conditions. The methodology employed an experimental approach using an SES TPS- 3720 Solar Energy Trainer. Experiments measured performance under 0% (baseline), 50% (partial), and 100% (full) shading. The study also evaluated the impact of shading material optical properties by testing opaque (wood), semi-opaque (paper), and translucent (plastic film) materials. Measurements were recorded across five irradiance levels using both LED lamp and DC motor loads.Key findings demonstrate a highly non-linear performance degradation. Partial shading covering 50% of the panel area resulted in a 65-70% power loss, far exceeding a proportional reduction.Full shading with opaque (wood) or semi-opaque (paper) materials caused a 100% power loss, eliminating all usable current. Translucent plastic film caused the least degradation (approx. 23% power loss). The results confirm that a material's optical transmittance, not its physical density, is the dominant factor determining shading severity. These findings validate established photovoltaic theory and highlight the critical importance of shadow avoidance in system design. The study reinforces the necessity of mitigation strategies such as bypass diodes and module-level power electronics (MLPE) in shade-prone installations.

Manual pavement inspection methods are slow, subjective, and often inconsistent, leading to delayed maintenance and increased road deterioration. This study was carried out to develop an automated, image-based system capable of detecting and classifying visible defects in flexible pavements using machine learning. The objectives of the study were to review existing pavement inspection techniques, collect and preprocess pavement image data, and design and train a model capable of identifying pavement failures accurately. The study was with the aim of improving the speed, objectivity, and reliability of pavement condition assessments. A dataset of pavement images was obtained from the Edo State Ministry of Works, field surveys, and public sources. The images were annotated in YOLO format and augmented by flipping, rotation, cropping, and brightness adjustment. The YOLOv8 object detection model, implemented in Python using TensorFlow, PyTorch, and OpenCV, was trained on Google Colab with an NVIDIA T4 GPU. Training was performed at varying epochs (50, 100, and 200) and hyperparameters to optimize detection performance. The model’s accuracy was evaluated using mean Average Precision (mAP) and recall metrics to assess its ability to detect cracks, potholes, and rutting in flexible pavements. Results showed that the model achieved a mean Average Precision (mAP₅₀) of 0.68 and recall above 0.80 for visible defects such as potholes and alligator cracking, at a confidence level of 0.5. The model was less effective in detecting faint, low-contrast linear cracks. This study concluded that YOLOv8-based models can effectively automate pavement distress detection, providing a faster and more reliable alternative to manual inspection. It is recommended that future work expand the dataset and explore enhanced training strategies to improve the detection of subtle linear cracks.