FACULTY OF ENGINEERING

This project focuses on the design and implementation of a controlled environment vertical farming system for tomato production in Benin City. The system integrates climate control, automated irrigation, and hydroponic nutrient delivery to optimize plant growth and resource efficiency. Key components include temperature and humidity sensors, an automated irrigation system, and a microcontroller-based control unit for real-time monitoring and adjustments. The vertical farming setup was designed to maximize space utilization while reducing water consumption and dependency on
chemical fertilizers. The implementation process involved system calibration, sensor integration, and performance evaluation to assess its impact on crop yield and sustainability. Results indicate that the controlled environment significantly enhanced tomato growth, minimized pest infestations, and improved overall yield compared to conventional soil- based farming methods. However, challenges such as high initial investment costs and power dependency were noted, necessitating the integration of renewable energy sources for long-term viability. This study demonstrates the potential of vertical farming as a sustainable and scalable solution for urban agriculture, addressing food security concerns while promoting resource-efficient farming practices. The findings suggest that further research into automation, AI-driven climate control, and localized material sourcing could enhance system performance and accessibility for wider adoption..

With the increasing global demand for sustainable and renewable energy, biodiesel has becomean essential alternative to traditional fossil fuels. This study looks into producing biodiesel fromwaste cooking oil (WCO) by using a unique catalyst made from calcined periwinkle shells. The WCO was characterized to uncover its main properties using ASTMD6751 standardmethodand the catalyst was produced through calcination at 900°C. The transesterification process wasoptimized using Response Surface Methodology (RSM) with a Box-Behnken design, usingfactors like catalyst loading (1–10 wt.%), reaction time (30–150 minutes), temperature(40–80°C), and the molar ratio of alcohol to oil (3:1–10:1). The result obtained from the characterization of the WCO are acid value of 6.17 mg KOH/g, a free fatty acid (FFA) content of 3.09%, a viscosity of 9.2 mPa.s at 30.08°C, a saponification value of 244.14 mg KOH/g, and a density of 956 kg/m³. The analysis of the calcined periwinkle shell show that it contains a high amount of calcium oxide (CaO) of about 97.08%, as revealed by Energy Dispersive X-ray (EDX) analysis. Additionally, Fourier Transform Infrared Spectroscopy (FTIR) confirmed the existence of functional groups necessary for biodiesel production, while Scanning Electron Microscopy (SEM) showed a highly porous structure, which significantly improved its catalytic efficiency. With the optimized conditions, a biodiesel
yield of over 90% was achieved. The final biodiesel product met industry standards and exhibited enhanced physicochemical properties.

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

Advancements in technology have led to a steady decline in the effort required to produce work, with better designs offering an effective and economical means to achieve desired outcomes with reduced effort. Power screws serve to transform rotary motion into translator y motion, as exemplified by the screw jack, where a minor horizontal force elevates or descends a substantial load. Operating akin to an inclined plane, the mechanical advantage of a screw jack is determined by the ratio of load to effort applied. Adjustment of the jack's height is facilitated by turning a lead screw, achievable either manually or through integration of an electric motor. The whole components is designed on a base plate of relatively good resistance to deformity and act as a support to weight balancing during the operation of the motor
This project analyzes the fabrication of a motorized screw jack by incorporating an electric motor in the screw in order to make load lifting easier. In this fabrication design, the power screw is rotated by connecting motor through universal joint and gear axle to power the screw jack plugged to the automobile 12 V battery source to generate power for the prime mover (motor), which transmits its rotating speed to the power screw to be rotated with required speed reduction and increased torque to drive the power screw. The significance of this project to design a car jack in form of motor operating condition in order to make the operation easier, safer and more reliable in order to reduce health risks especially back ache problems associated with doing work in a bent or squatting position for a long period of time. The motorized screw jack is easy to use by women or whoever had problem with the vehicle tyres along the road. It will also save time and requires less human energy to operate. Based on results, the project provide a motorized jack able to effectively lift a load of 750kg to 1000kg and also provide a safe usage during maintenance of vehicle.

The efficient execution of drilling operations hinges upon a comprehensive evaluation of surface facilities, encompassing an array of parameters and factors. This project delves into the intricate web of variables that influence drilling efficiency, wellbore stability, and equipment selection. Through meticulous analysis, it uncovers critical insights into mud weight control, pump displacement, pipe diameter considerations, and the deployment of solids control equipment such as desilters and desanders (hydrocyclone). These findings are poised to empower drilling engineers and operators with the knowledge needed to optimize surface facilities during drilling operations, ensuring a harmonious interplay of equipment, drilling fluid properties, and operational parameters. By bridging the gap between theory and practical application, this project not only contributes to the advancement of drilling engineering but also offers tangible recommendations to enhance drilling endeavours' efficiency, safety, and overall success

Although centralized institutions, like governments, can use blockchain technology to increase the safety and security of sensitive data, this technology also permits the emergence of decentralized business models. The proposal to create a car registration system in Nigeria that can enhance interoperability between governmental agencies and might be expanded to a cross-borders system is described in this project work and is based on the Ethereum blockchain network. The suggested system takes care of all car registration-related procedures, including changing a vehicle's ownership status and registering it. As the car registration information is supplied to each government agency in a single decentralized system, this approach can facilitate information interchange among several states

With Nigeria generating over 42 million tonnes of waste annually, improper disposal poses significant risks to soil health, groundwater, and public health. This study examines the contamination levels of heavy metals and the physicochemical properties of soil at a solid waste disposal site in Ovia Northeast, Edo State, Nigeria. Soil samples were collected at varying depths (10, 20, 30, and 40 cm) from a dumpsite and a control site, focusing on lead (Pb), iron (Fe), copper (Cu), and manganese (Mn), alongside properties such as pH, bulk density, porosity, organic matter, and electrical conductivity (EC). Results revealed elevated levels of heavy metals at the dumpsite compared to the control site, particularly in the top 10 cm of soil. For example, Pb concentrations reached 12.31 mg/kg at the dumpsite, nearly three times higher than the 4.24 mg/kg observed at the control. Similarly, copper (Cu) levels at the dumpsite peaked at 74.22 mg/kg, significantly higher than the control site’s 57.47 mg/kg. Physicochemical properties demonstrated a strong influence on metal mobility: soil pH at the dumpsite ranged from 7.12 to 7.62, slightly higher than the control’s 6.86 to 6.12. Organic matter content decreased with depth, from 8.74% at the surface to 3.15% at 40 cm in the dumpsite, compared to 9.07% to 2.54% in the control. EC values were markedly higher
at the dumpsite (252–290 µS/cm) compared to the control (144–168 µS/cm), reflecting leachate infiltration and ion enrichment. The findings underscore the environmental risks posed by heavy metal contamination, including soil degradation, reduced fertility, and potential bioaccumulation in the food chain. Elevated
metal concentrations exceeded WHO permissible limits, necessitating immediate remediation actions. Recommendations include the implementation of sustainable waste management
practices, soil remediation techniques such as phytoremediation, and ongoing monitoring to mitigate long-term environmental impacts.

The growing demand for renewable and sustainable fuels has led to increased research into biodiesel production from non-edible oils. This study aims to evaluate the techno economic feasibility of biodiesel production from a ternary blend of neem oil, castor oil, and waste vegetable oil. The research focuses on analyzing the economic viability through Aspen Plus simulation, with an emphasis on optimizing reaction parameters to achieve a high biodiesel yield while maintaining
cost-effectiveness. In this study, the acid values of the feedstocks were first determined through titration, revealing the need for pre-treatment via esterification before transesterification. The Aspen Plus process simulation was employed to model the transesterification reaction, incorporating key factors such as methanol-to-oil ratio, reaction temperature, and the flowrate. A techno-economic analysis was
conducted to determine capital investment, operating costs, net present value (NPV), internal rate of return (IRR), and payback period, providing insights into the financial viability of the biodiesel production process. The results indicate that biodiesel production from the ternary blend is economically feasible.
The total capital investment for the project was $7,020,220 (₦10,603,000,000), with an annual operating cost of $1,793,070 (₦2,710,000,000). The total revenue generated was $15,678,800 (₦23,678,000,000) per year, leading to an NPV of $78,295,380 (₦118,180,000,000) at a 10% interest rate. The internal rate of return (IRR) was 28.2%, demonstrating strong investment potential, while the payback period was approximately 0.51 years (~6 months), indicating rapid cost recovery. Additionally, the profit margin was 88.56%, confirming the economic viability of
the process

For its bio- and environmentally friendly properties, low cost, and relative abundance, clay has become increasingly relevant and used. Based on their components and layer patterns, clay minerals have a variety of morphological and physicochemical characteristics., in addition to its well-established uses in adsorbent development, water treatment, and construction. In order to determine whether clay samples from the Ikpeshi town in the Akoko-Edo LGA could be used in an industrial process, its physical and chemical characteristics were examined. The study involved the analysis of elemental content, mineral constituent, functional groups of compounds content, surface morphology, and thermal stability with EDXRF, XRD, FTIR, SEM, BET and TGA respectively. Results revealed that the sample was kaolinite with SiO2 45.116 wt%, and Al2O3 20.39 wt% as the most predominant elements. Wave numbers of 909.47043cm-1 to 998.92654cm-1 with bold peaks revealed the presence of SiO4-4. The overall study revealed
kaolinite characteristics and strong thermal stability thus possesses properties for clay suitable for lining furnace kilns.