FACULTY OF ENGINEERING

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.

Biobutanol is a renewable biofuel characterized by high energy density, low volatility, and compatibility with existing petroleum infrastructure. Despite these advantages, its large-scale production remains limited by high feedstock costs, low microbial tolerance, and process inefficiencies. This study investigates the conversion of corn cob, an abundant lignocellulosic residue, into biobutanol through Simultaneous Saccharification and Fermentation (SSF) using Clostridium beijerinckii. The corn cob was pretreated with dilute sulfuric acid to enhance enzymatic accessibility, followed by detoxification and enzymatic hydrolysis using a cocktail of cellulase, β-glucosidase, and pectinase. The hydrolysate obtained served as the substrate for SSF, and the key operational parameters were optimized using Response Surface Methodology (RSM). Fourier Transform Infrared (FTIR) spectroscopy confirmed effective delignification and structural modification after pretreatment. Optimum conditions of pH 5.48, inoculum size 9.04% (v/v), and temperature 37.45 °C produced a maximum butanol concentration of 15.60 g/L. Kinetic modeling empirical (quadratic fits / RSM) kinetic analysis accurately described substrate utilization and solvent formation. The results demonstrate that corn cob is a viable low-cost feedstock for sustainable biobutanol production, and that the integrated SSF approach offers an efficient and environmentally responsible pathway for renewable fuel generation and agricultural waste valorization in Nigeria.

This study focuses on the production of biodiesel from waste cooking oil (WCO) using calcined cow bone as a heterogeneous catalyst through the transesterification process. The research aimed to promote sustainable energy production by converting waste oils into biodiesel while utilizing animal bone waste as a low-cost, environmentally friendly catalyst. The WCO was pretreated and characterized to determine its physiochemical properties, which included an acid value of 1.4025 mg KOH/g, free fatty acid (FFA) content of 0.7012%, peroxide value of 16 meq/kg, iodine value of 44.1 g I₂/100 g, viscosity at 40 °C of 53.5 cP, saponification value of 362.667 mg KOH/g, moisture content of 2.678%, and density of 0.9176 g/cm³. These results confirmed that the feedstock required pretreatment before transesterification to minimize soap formation and enhance biodiesel yield. Characterization of the catalyst was performed using analytical techniques such as X-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET) surface area analysis, and Fourier transform infrared spectroscopy (FTIR) to confirm the presence of CaO and evaluate its surface properties.The transesterification reaction was carried out using methanol and cow bone-derived catalyst under optimized conditions. The resulting biodiesel was washed, purified, and analyzed for key physiochemical properties. The biodiesel exhibited an acid value of 0.561 mg KOH/g, density of 0.901 g/cm³, viscosity at 40 °C of 8.86 cP, and a flash point of 115 °C. These results were within acceptable limits prescribed by ASTM D6751 and EN 14214 standards, indicating that the produced biodiesel possesses good fuel properties suitable for use in diesel engines. The study concludes that waste cooking oil can serve as an efficient feedstock for biodiesel production, and cow bone ash is a promising, sustainable, and economical catalyst. This dual utilization of waste materials not only reduces environmental pollution but also supports circular economy practices and sustainable energy development.

Palm Oil Mill Effluent (POME) is a wastewater byproduct of palm oil production, characterized by its high organic content and potential pollutant to water bodies and capable of causing significant environmental damage. This study therefore seeks to evaluate the treatment methods by coagulation and adsorption processes to remove suspended solids and pollutants, thereby purifying the wastewater for safe discharge or reuse. These methods are essential for environmental protection, resource recovery, and economic sustainability. The POME sample was collected, diluted, and analyzed to determine its physicochemical properties before treatment. Its pH was adjusted to both acidic and alkaline conditions using hydrochloric acid and sodium hydroxide, monitored with pH indicator paper. Processed periwinkle shell powder served as a natural coagulant and adsorbent. Standard laboratory instruments were used to assess parameters such as pH, turbidity, total dissolved solids, electrical conductivity, and salinity before and after treatment. The study evaluated the effects of coagulant dosage, contact time, and pH on the treatment of Palm Oil Mill Effluent (POME) using a periwinkle shell–chitosan composite. Significant reductions in total dissolved solids (TDS) and salinity were achieved at moderate dosages (0.55– 0.82 g/L), contact times of 105–150 minutes, and near-neutral pH (7–8.2), showing effective coagulation and adsorption. X-ray diffraction (XRD) analysis revealed crystalline peaks at 2θ values of 23.9°, 26.5°, 27.5°, 33.4°, 36.4°, 38.1°, 41.4°, 43.1°, 46.0°, 48.6°, 50.5°, and 53.1°, corresponding to aragonite, muscovite, quartz, and orthoclase phases. Crystallite sizes (111–702 Å) confirmed a fine heterogeneous structure with high surface activity, making the composite suitable for efficient and sustainable POME purification

This study aims to investigate the feasibility of using a blend of plantain and banana peel ash (PBPA) as a partial replacement for cement in concrete. The study seeks to evaluate the effects of PBPA on the workability, compressive strength, and flexural strength of concrete, with a view to reducing the environmental impact of concrete production.
The workability of the concrete mixtures was evaluated using the slump test, in accordance with ASTM C143/C143M-15a. The compressive strength was determined using the standard compressive strength test, as outlined in BS EN 12390-3:2019. The flexural strength was assessed using the modulus of rupture test, in line with ASTM C78/C78M-18. These tests enabled a comprehensive evaluation of the effects of PBPA on the mechanical properties of concrete. The results showed that 0% replacement of cement with PBPA and coarse aggregate produced a slump value of 40mm while 5 to 15% replacement produced slump values of 39.7mm,42.7mm, 51.3mm respectively. From the rate of decrease, this indicated that
increasing the PBPA content decreases the workability of the mix , while the compressive and flexural strengths were reduced by up to 20% at 28 days. However, the concrete mixtures with up to 10% PBPA replacement still met the strength requirements for grade M20 concrete. The findings suggest that PBPA can be used as a supplementary cementitious material to reduce the environmental impact of concrete production.