FACULTY OF ENGINEERING

The global shift toward sustainable energy has intensified interest in biodiesel as an environmentally friendly alternative to fossil fuels. This study investigates the production of biodiesel from waste cooking oil (WCO) using calcined heterogeneous catalysts derived from waste materials, including pumpkin pods and turkey bones, and its subsequent preservation using natural extracts from Azadirachta indica (neem) and Ocimum gratissimum (African basil). Transesterification was carried out in a pilot-scale reactor at 60 °C for 75 minutes. Characterization of the WCO prior to transesterification showed an acid value of 2.41 mg KOH/g and a free fatty acid (FFA) content of 1.21%, indicating moderate degradation from repeated frying. Post-treatment analysis revealed a slight increase in acid value to 3.07 mg KOH/g, while other parameters, including saponification value (251.83 mg KOH/g), density (0.8948 g/cm³), and viscosity (9.50 mPa·s), remained within acceptable ranges for biodiesel feedstock. The produced biodiesel met key ASTM D6751 specifications, with a density of 0.87 g/cm³, acid value of 0.34 mg KOH/g, viscosity of 4.62 mm²/s, and a flash point of 220 °C, confirming its suitability for use as a fuel or in blends. GC-MS analysis revealed a fatty acid methyl ester (FAME) composition dominated by methyl oleate (48.68%) and methyl palmitate (37.48%). FTIR spectroscopy further confirmed successful transesterification through the presence of a characteristic ester carbonyl absorption peak at 1742.81 cm⁻¹. Stability studies conducted over six weeks showed that the combined treatment of neem and basil extracts (30 ml) resulted in the lowest final acid value of 0.78 mg KOH/g, indicating improved oxidative stability. This study demonstrates that waste cooking oil is a viable and cost-effective feedstock for biodiesel production. Furthermore, the use of locally sourced plant extracts as natural stabilizers offers a sustainable and biodegradable approach to enhancing biodiesel storage stability, supporting the advancement of renewable energy technologies in Nigeria.

Modern power generation facilities depend heavily on auxiliary components such as centrifugal pumps, which ensure effective cooling and stable operation of gas turbines. The literature reviewed shows that conventional maintenance strategies reactive and preventive—are often costly and inefficient, leading to unexpected failures and operational losses. Predictive maintenance (PdM) has emerged as a superior, data-driven alternative that uses statistical and sensor-based models to forecast equipment failure. The review further highlighted the growing adoption of PdM techniques in African power systems, where the need for reliability and cost optimization remains high. This study focuses on developing a predictive maintenance model for the cooling water centrifugal pump at the Azura-Edo Independent Power Plant, using statistical trend and regression analysis to predict performance degradation. The research employed an analytical and quantitative design, utilizing two years (2023–2024) of historical operational data from Azura-Edo IPP. Key parameters included ambient temperature, discharge pressure, gas turbine active power, and vibration readings from different pump locations. Microsoft Excel served as the main analytical tool for data cleaning, descriptive statistics, correlation testing, and multiple regression modeling. The regression model related vibration amplitude to operating parameters, producing a mathematical expression capable of estimating degradation levels. A control chart was also developed to monitor vibration stability using calculated upper and lower control limits, forming an early warning system for predictive maintenance intervention. Results from the analysis revealed moderate variability among parameters, with vibration showing the strongest correlation to discharge pressure and turbine power. The developed regression model effectively predicted vibration trends with reasonable accuracy, confirming its suitability for maintenance forecasting. The study concluded that predictive maintenance can significantly improve pump reliability, reduce unplanned downtime, and optimize maintenance scheduling at Azura-Edo IPP. It is recommended that the model be integrated into the plant’s SCADA system for real-time monitoring, with periodic updates to ensure adaptive accuracy and sustainable performance.

The aim of this project was the design, fabrication, and testing of an easy-to-operate and affordable small-scale palm oil clarifier fit for farm use. This was accomplished by the design and selection of materials for the manufacture of the individual components of the clarifier, the production of the working drawings, and fabrication. A performance test, in terms of oil recovery rate, was carried out on the clarifier. On average, we had 91.30% and 91.54% oil recovery rates. Comparatively, these rates are within the range of the results from industrial and more automated systems with large-scale farms, which typically strive for recovery rates between 90% and 95%

As the world continues to embrace cleaner and smarter energy solutions, there's a growing need for tools that not only design microgrids but also make them smarter, more responsive, and easier to manage. This project introduces an Intelligent Microgrid Management and Optimization System — a desktop application built with Python — designed to help users plan, optimize, and monitor solar-powered microgrid systems more efficiently. What sets this tool apart is its ability to pull live weather data (like sunlight levels and temperature) using the OpenWeatherMap API. With this, it can predict how much energy your solar panels
might generate and how much power you’ll need, thanks to built-in machine learning models. The system then uses a genetic algorithm to figure out the best combination of solar panel size and battery capacity to meet your energy needs while keeping costs low.
The application runs through a simple and responsive user interface (built with PyQt6), offering features like real-time graphs, a weather dashboard, and system control panels. It also supports SCADA-style monitoring, so users can see power generation, battery status, and energy demand in real time. Overall, this tool is designed to be both smart and user-friendly, making it useful not
just for engineers and developers, but also for students, researchers, and organizations working on renewable energy solutions.

The increasing demand for efficiency and transparency in Nigeria’s livestock sector has highlighted the need for digital solutions that connect farmers, buyers, and agricultural experts in real time. This project, titled “Design and Development of a Web-Based Livestock Management and Sales Platform” focuses on creating a functional and user-friendly online platform that digitises livestock management, marketing, and consultancy services. The system was developed using a combination of HTML, CSS, and JavaScript for the frontend design, while Django (Python) served as the backend framework and PostgreSQL as the database management system. The platform allows farmers (administrators) to list available livestock, manage bookings, update records, and provide consultancy advice, while buyers (users) can browse livestock listings, add animals to cart, view detailed information, and request consultations. Additional modules, such as a blog for farming guides and a testimonial section, were integrated to enhance engagement and credibility. An Agile development methodology was adopted to ensure iterative design, testing, and improvement throughout the development cycle. The system was deployed on the Render cloud hosting platform, providing online accessibility and scalability for real-world application. Testing results confirmed that the system effectively supports livestock transactions, simplifies communication between stakeholders, and provides a reliable database for record management. Overall, the project demonstrates how information and communication technology (ICT) can enhance market access, transparency, and knowledge sharing in Nigeria’s livestock industry. It contributes to digital agriculture research and provides a scalable model for future integration of payment gateways, analytics, and mobile platforms. Keywords: Web-based system, Livestock management, Digital agriculture, Django, ICT, E-commerce, Render hosting.

This project presents the optimization of solar inverter efficiency using machine learning algorithms to improve power generation accuracy and system reliability under varying environmental conditions. Traditional solar inverter systems and Maximum Power Point Tracking (MPPT) methods often experience limitations in adapting to fluctuations in solar irradiance, temperature, and shading conditions, leading to reduced efficiency and energy loss. To address these challenges, this study developed and evaluated machine learning models capable of predicting and optimizing inverter performance in real time. Environmental and operational data including irradiance, temperature, day, hour, and inverter performance metrics were collected from the NASA and NSRDB datasets for the University of Benin region. Data preprocessing techniques such as normalization, interpolation, and feature engineering were applied before model training. Three machine learning models — Random Forest (RF), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN) — were implemented and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Results showed that the ANN model outperformed the other models with an MAE of 0.019, RMSE of 0.029, and R² value of 0.962. The optimized system achieved an efficiency improvement of 8.3% compared to conventional MPPT methods. The study further demonstrated the capability of machine learning algorithms to adapt to changing environmental conditions and improve solar inverter performance. The developed model was deployed using Django REST Framework for real-time prediction and monitoring. This research confirms that machine learning-based optimization can significantly enhance solar inverter efficiency, reduce energy losses, and contribute to sustainable and intelligent renewable energy systems.

This report presents a comprehensive design and SolidWorks-based modeling of a conceptual container vessel equipped with an integrated robotic cargo-handling crane. The project combines core naval architecture principles with advanced parametric modeling techniques to develop a structurally coherent, hydrodynamically efficient, and operationally automated vessel concept. The aim of the study is to demonstrate how modern CAD tools can be applied to marine engineering design while integrating automation systems that enhance vessel functionality and operational efficiency. The modeling process covers the systematic construction of the hull, deck arrangement, superstructure, bulwarks, and the robotic crane system using sketches, extrusions, lofts, reference planes, and surface features. Material properties such as mild steel, aluminum alloy, and anti-fouling coatings were applied to approximate real marine construction and enhance visualization accuracy. Design considerations including hydrodynamic efficiency, vessel stability, structural integrity, and safety guided all modeling decisions, particularly the placement and structural support of the onboard robotic crane. The inclusion of the robotic crane demonstrates the potential for automated cargo operations, reduced human involvement, and improved port efficiency. Rendering and surface finishing techniques further enhance presentation quality, making the model suitable for academic, industrial, and concept-evaluation purposes. Overall, the project showcases the practical application of CAD tools in modern marine engineering and highlights the relevance of integrating advanced automation technologies in contemporary vessel design.

his project focuses on the design and construction of a smart electricity meter using Internet of Things (IoT) technology to enable efficient energy monitoring and management. The system is built around the ESP32 micro\controller, which controls data acquisition, processing, and wireless transmission to the ThingSpeak cloud platform. The PZEM-004T measurement module is employed to accurately measure voltage, current, power, and energy consumption in real time. A DC-DC buck converter provides a regulated power supply, ensuring stable operation of the ESP32 and peripheral components. Data collected by the meter are uploaded to ThingSpeak, where users can visualize live readings, generate graphical trends, and analyze consumption patterns through an interactive dashboard. This allows for remote monitoring, fault detection, and informed decision-making regarding energy usage. The prototype demonstrates reliable performance, high accuracy, and cost-effectiveness compared to conventional meters. By integrating embedded systems with IoT-based cloud services, the developed smart meter promotes efficient power utilization, user awareness, and modern smartgrid compatibility. Overall, the project highlights a practical approach to advancing energy management through low-cost IoT solutions.

This study investigates the potential of eggshell waste as a reinforcement material in aluminum matrices for kitchenware applications, aiming to enhance material properties. Composites were fabricated with 7%, 10%, and 13% eggshell reinforcement and subjected to tensile testing, Brinell hardness testing, Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and Energy-Dispersive Spectroscopy (EDS) to assess mechanical, thermal, and microstructural properties. Tensile testing revealed a significant increase in Ultimate Tensile Strength (UTS) with 13% reinforcement, reaching 134.29 MPa, though ductility was reduced. SEM analysis of the 10wt% composite showed a finer textured structure but non-uniform particle distribution. EDS confirmed calcium presence, and showed reduced oxygen content. Brinell hardness exhibited a positive correlation between the weight percentage of eggshell in the aluminum composite, which showed that higher eggshell content within the tested range leads to increased hardness. DSC indicated that eggshell addition altered thermal characteristics, with the 13wt% composite showing a slightly higher melting temperature and changes in heat of fusion. These results demonstrate that eggshell reinforcement enhances the tensile strength, hardness and modifies the thermal behavior of aluminum.

Androgenetic alopecia (AGA), a gradual form of hair loss driven by oxidative stress and hormonal imbalance, remains a major dermatological issue. Conventional synthetic treatments often lead to undesirable side effects, creating the need for safer, plant-based alternatives rich in bioactive compounds that can promote hair regrowth and scalp health. This study aimed to investigate the extraction, optimization, and characterization of bioactive compounds from Syzygium aromaticum (clove), Rosmarinus officinalis (rosemary), and Moringa oleifera (moringa) for potential application in natural formulations targeting androgenetic alopecia.Extraction was optimized using a mixture design approach in Design Expert® software, where the proportions of the three plant materials were systematically varied to maximize total phytochemical yield. The experimental data were fitted into a quadratic model that exhibited strong predictive accuracy with an R² value of 0.9633, while the predicted and adjusted R² values were closely aligned, 0.9268 and 0.9560 respectively, confirming the model’s reliability and significance (p < 0.0001). Optimization results showed that the best formulation was gotten using 3.553 g of cloves, 2.389 g of rosemary, and 4.057 g of moringa, yielding 15.108 with a maximum desirability value of 1.000. Phytochemical screening and quantitative analysis revealed that the optimized blend possessed very high concentrations of phenols, flavonoids, terpenoids, and steroids. Fourier Transform Infrared (FTIR) spectroscopy further confirmed the presence of crucial functional groups such as hydroxyl (–OH), carbonyl (C=O), and C–O linkages, characteristic of polyphenolic and terpenoid compounds.The results indicated that the combined extract showed synergistic phytochemical enrichment, suggesting improved bioactive potency. The dominance of phenolic and flavonoid compounds implies strong antioxidant and 5α-reductase inhibitory potential, thereby reducing dihydrotestosterone (DHT)-induced follicular shrinkage. Terpenoids and steroids were also found to contribute to follicular nourishment and stimulation of keratinocyte activity, enhancing overall hair growth. In conclusion, the optimized mixture of Syzygium aromaticum, Rosmarinus officinalis, and Moringa oleifera extracts exhibited promising bioactive and functional properties, supporting its potential as a natural therapeutic formulation against androgenetic alopecia.