FACULTY OF ENGINEERING

This report details the design and implementation of an electric-solar vehicle,
focusing on the fabrication and testing of its inverter. The inverter, a crucial
component for efficient power conversion, was developed to optimize the
integration of solar energy with an electric motor drive. This report focuses on the
practical aspects of the inverter's construction and performance evaluation.
The design considerations are outlined, followed by a detailed description of the
component selection, PCB fabrication, and assembly. Performance testing results,
demonstrating the inverter's efficiency and suitability for the vehicle, are also
included.

This project addresses the critical need for efficient and accessible water pumping solutions in various applications, particularly in contexts with limited access to conventional power sources. Water pumping systems are integral to industries ranging from agriculture to disaster relief. However, challenges such as power dependency, infrastructure limitations, and environmental concerns persist. This project introduces a transformative approach by integrating a hand drill as the primary power source within a centrifugal pump system. This innovative solution leverages the portability, affordability, and versatility of this device, making it a practical and cost-effective alternative. The hand drill-powered system eliminates the reliance on electricity or fuel, enhancing accessibility in remote or emergency situations. In addition to its applicability in car washes, low volume irrigation, and medical microfluidics, this project extends its impact to various fields, including agriculture, disaster relief, and remote research stations. It offers a sustainable, portable, and environmentally responsible water pumping solution that aligns with modern needs. By combining the convenience of hand drills with the efficiency of centrifugal pumps, this project represents a significant advancement in water pumping technology.

This project addresses the critical need for efficient and accessible water pumping solutions in various applications, particularly in contexts with limited access to conventional power sources. Water pumping systems are integral to industries ranging from agriculture to disaster relief. However, challenges such as power dependency, infrastructure limitations, and environmental concerns persist. This project introduces a transformative approach by integrating a hand drill as the primary power source within a centrifugal pump system. This innovative solution leverages the portability, affordability, and versatility of this device, making it a practical and cost-effective alternative. The hand drill-powered system eliminates the reliance on electricity or fuel, enhancing accessibility in remote or emergency situations. In addition to its applicability in car washes, low volume irrigation, and medical microfluidics, this project extends its impact to various fields, including agriculture, disaster relief, and remote research stations. It offers a sustainable, portable, and environmentally responsible water pumping solution that aligns with modern needs. By combining the convenience of hand drills with the efficiency of centrifugal pumps, this project represents a significant advancement in water pumping technology

The components of bio-waste are particularly abundant in essential minerals like calcium and potassium, which are essential for the manufacture of effective biocatalysts for biodiesel. This study evaluated the potential of bio-based heterogeneous catalyst of fused cockle shells and watermelon peels for the transesterification of waste vegetable oil. At 900°C and 500℃, the waste materials were dried, calcined, and carbonized, respectively. In order to evaluate the compositional, morphological, structural, and thermal features of both the catalyst and the precursor materials, they were both characterized. The Box-Behnken design was utilized to generate 29 experimental runs to examine the impact of operational parameters such catalyst loading, temperature, methanol-to-oil molar ratio, and reaction time. The presence of basic (calcium and potassium) and acidic oxides (silicon and nickel) demonstrated that the catalyst was bi-functional. The catalyst's surface area (105.35 m2/g) and pore volume (0.60 cm3/g) obtained from the BET analysis contributed to a 91.77% biodiesel yield at 63.34 °C reaction temperature, 149.41 min reaction time, 1.05wt% catalyst loading, and a 14.45:1 methanol to oil ratio. The physicochemical parameters of the biodiesel produced were measured and determined to be acceptable according to the European National (EN) and American Society for Testing of Materials (ASTM) quality standards, demonstrating the product's suitability for use as fuel

The power supply in developing countries is practically low owing to the inability of public power plants to meet the demand of its population and this has brought in the need for an alternative source of electrical power. Where this is the case, a transfer switch is needed to transfer the supply of power from the different sources to the load. A manual transfer switch requires that a user effects the overall process of power changeover from the different supply sources to the load and this could become cumbersome hence, the need for an automatic transfer switch. The objective of this design centers on sensing the primary/main power supply source, to startup
the secondary power source (generator) when the main power supply source fails, shutdown the generator when the main power supply source is restored, to startup the secondary power source when power fluctuations from the main power supply source is detected and to automatically transfer the load to the available power source, thereby making the entire process easy and reliable. The design was carried out with low cost solid state electronic components such as; Relays, transformer, microcontroller, voltage regulator, resistors, capacitors, diodes

This study investigates the potential of okra (Abelmoschus esculentus) leaf extract as a green, eco-friendly corrosion inhibitor for low carbon steel in acidic environments. The research focuses on evaluating the inhibitory efficiency of the extract at different concentrations and exposure times using electrochemical methods, including potentiodynamic polarization and open circuit potential measurements. Surface characterization techniques, such as Scanning Electron Microscopy (SEM), were employed to analyze the steel surface morphology after exposure. Results indicated that the okra leaf extract significantly reduced the corrosion rate of low carbon steel, forming a protective layer on the metal surface. The inhibition efficiency increased with higher extract concentrations, demonstrating the potential of bioactive compounds in okra leaves to adsorb onto the steel surface and block corrosion sites. The study concluded that okra leaf extract can serve as an effective, environmentally safe corrosion inhibitor, providing a sustainable alternative to conventional chemical inhibitors. These findings highlight the applicability of plant-based extracts in corrosion control and open avenues for further research in green corrosion inhibition technologies

The accurate prediction of the Well roductivity Index (PI) is critical for reservoir management, production optimization, and forecasting. Traditional methods, such as analytical correlations and decline curve analysis, are often limited by simplifying assumptions that fail to capture the complexities of heterogeneous reservoirs like those in the Niger Delta. This research addresses
this limitation by developing and evaluating a data-driven framework for PI prediction using machine learning (ML) on historical production data. The study implements and compares three advanced ensemble regression algorithms—Random Forest, GBoost, and CatBoost—to predict PI from daily records of oil, gas, and water production rates and downhole pressures. A dataset approximately 7,000 daily records from five Niger Delta wells was utilized, with the PI target variable calculated using a proxy for reservoir pressure drawdown. A clear performance hierarchy was established among the models. Random Forest yielded the weakest performance (R² = 0.18, MAE = 65.62), while XGBoost showed substantial improvement (R² = 0.78, MAE = 34.14). CatBoost erged as the superior model, achieving exceptional predictive accuracy with an R² of 0.95, a Mean Absolute Error (MAE) of 18.96, and a Root Mean Squared Error (RMSE) of 21.02. Residual and temporal analyses confirmed that CatBoost produced unbiased, homoscedastic errors and effectively tracked the dynamic PI trends of individual wells over time. Interpretability analyses revealed that production rates (oil, gas, and water) were the most influential predictors, a finding consistent with reservoir engineering principles. However, this also highlights a methodological caveat regarding the mathematical coupling between the model's inputs and the PI target. The study concludes that CatBoost provides a robust and highly accurate model for PI prediction from routine field data, offering a significant advantage over traditional methods for well performance monitoring and screening in the Niger Delta context

This study evaluates the economic viability of solar farms as a sustainable energy solution for residential communities in Nigeria. The research aims to determine whether solar farms can provide a cost-effective alternative to the national grid by analyzing key economic factors, including initial investment, operational costs, and long-term financial benefits. The study also explores the environmental impact of solar farms, highlighting their potential to reduce carbon emissions and enhance energy security for households. By assessing various ownership models and financial incentives, the research provides insights into the feasibility of large-scale solar
adoption in residential areas.The methodology involves a detailed load analysis for a 100-household community, calculating daily energy consumption and peak load demand. The study designs a solar farm using 806 monocrystalline solar panels, a 400 kVA inverter, and necessary protection devices. Cost estimation covers component procurement, labor, land acquisition, and annual maintenance. Financial modeling incorporates revenue generation from surplus energy sales to the grid and cost comparisons with traditional electricity tariffs. A sensitivity analysis evaluates the impact
of rising grid electricity prices on the long-term economic benefits of solar farms.

The accurate prediction of the Well Productivity Index (PI) is critical for reservoir management, production optimization, and forecasting. Traditional methods, such as analytical correlations and decline curve analysis, are often limited by simplifying assumptions that fail to capture the complexities of heterogeneous reservoirs like those in the Niger Delta. This research addresses this limitation by developing and evaluating a data-driven framework for PI prediction using machine learning (ML) on historical production data. The study implements and compares three advanced ensemble regression algorithms—Random Forest, XGBoost, and CatBoost—to predict PI from daily records of oil, gas, and water production rates and downhole pressures. A dataset of approximately 7,000 daily records from five Niger Delta wells was utilized, with the PI target variable calculated using a proxy for reservoir pressure drawdown. A clear performance hierarchy was established among the models. Random Forest yielded the weakest performance (R² = 0.18, MAE = 65.62), while XGBoost showed substantial improvement (R² = 0.78, MAE = 34.14). CatBoost emerged as the superior model, achieving exceptional predictive accuracy with an R² of 0.95, a Mean Absolute Error (MAE) of 18.96, and a Root Mean Squared Error (RMSE) of 21.02. Residual and temporal analyses confirmed that CatBoost produced unbiased, homoscedastic errors and effectively tracked the dynamic PI trends of individual wells over time. Interpretability analyses revealed that production rates (oil, gas, and water) were the most influential predictors, a finding consistent with reservoir engineering principles. However, this also highlights a methodological caveat regarding the mathematical coupling between the model's inputs and the PI target. The study concludes that CatBoost provides a robust and highly accurate model for PI prediction from routine field data, offering a significant advantage over
traditional methods for well performance monitoring and screening in the Niger Delta context.

Tight reservoirs contain a large amount of hydrocarbon resources, but producing oil from them is often difficult because of their very low permeability and complex pore structure. Conventional water flooding is commonly used to maintain reservoir pressure and displace oil; however, in tight formations it usually results in low oil recovery due to restricted fluid flow and strong capillary forces. Because of this limitation, there is growing interest in improved water flooding techniques such as smart water injection. This study evaluates the impact of smart water injection on oil recovery in a tight sandstone reservoir using numerical reservoir simulation. A synthetic reservoir model representing a typical tight sandstone formation in the Niger Delta was developed using the Computer Modelling Group (CMG) GEM simulator. Two injection scenarios were considered under the same reservoir conditions: conventional high-salinity water flooding and low-salinity smart water flooding. The smart water case involved reducing the salinity of the injected brine in order to examine its effect on oil displacement and reservoir performance. The performance of both injection strategies was analyzed by comparing cumulative oil production, oil recovery factor, water cut, and reservoir pressure over a production period of 45 years. The simulation results show that smart water injection produced a slightly higher oil recovery compared to conventional high-salinity flooding and also delayed the increase in water cut. However, the overall improvement in recovery was relatively small because fluid movement and ion transport are limited in tight sandstone formations. The results suggest that although smart water injection can improve oil recovery to some extent in tight reservoirs, its effectiveness may be limited when used alone. Combining smart water flooding with other enhanced oil recovery methods may provide better production performance in tight formations.