FACULTY OF ENGINEERING

The growing need for sustainable petroleum production and greenhouse gas reduction has driven interest in carbon dioxide (CO₂)-based Enhanced Oil Recovery (EOR) as a dual-purpose technology for increasing oil recovery while enabling carbon sequestration. This study investigates the potential of CO₂ injection for enhanced oil recovery and geo-storage in tight sandstone reservoirs within the Niger Delta Basin, Nigeria. Laboratory-based simulations and analytical modeling were used to evaluate the influence of injection rate, permeability, wettability, and fracture dip angle on oil displacement efficiency and CO₂ storage performance. Results show that at lower injection rates (0.2 mL/min), delayed breakthrough (0.85 PV) was observed with a recovery factor of 88.2%, while moderate rates (0.5 mL/min) achieved optimal recovery (91.4%) and balanced CO₂ retention (58.1%). Higher rates (1.0 mL/min) led to early breakthrough (0.55 PV) and reduced storage efficiency (54.9%). Overall, findings demonstrate that CO₂ injection enhances oil recovery by 85–93% and achieves up to 65% storage efficiency, depending on reservoir conditions. The study confirms that CO₂-EOR is technically feasible for the Niger Delta Basin, offering both economic and environmental benefits by improving hydrocarbon recovery and reducing carbon emissions.

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

A remote monitoring and control system enables real-time monitoring and management of devices or processes remotely. It enhances operational efficiency through continuous data acquisition and automated action. The system uses communication technologies to enable simple interaction between users and remote assets. Its applications cross industries such as manufacturing, agriculture, energy, and smart homes. The aim of this project was to make a remote monitoring and control system that can control and measure power supply remotely from a mobile application that we will also be developing. This remote monitoring and control system was made with the ESP32 microcontroller, making use of its inbuilt Wi-Fi and Bluetooth modules, while the mobile application used to monitor, control, and configure the system was made with the react-native framework. The interface between the mobile application and the system was the Blynk.cloud library and API, which was used to handle the remote online connection, while the BLE (Bluetooth Low Energy) protocol was used to update the Wi-Fi credentials of the system through the mobile application. This project presented the results obtained with the development and testing of the system, as well as the advantages of using a Wi-Fi-enabled system in place of GSM device

Efficient management of oil reservoirs requires accurate prediction of reservoir performance and optimization of recovery strategies. This study focuses on the simulation and performance analysis of a waterflooded oil field using Computer Modelling Group (CMG) software. Waterflooding, a secondary recovery method, is applied to sustain reservoir pressure and enhance oil displacement efficiency after primary depletion. In this research, a threedimensional reservoir model was constructed using geological, petrophysical, and production data, incorporating rock and fluid properties such as porosity, permeability, viscosity, and relative permeability curves. The simulation was performed using CMG IMEX, a black-oil simulator, to evaluate reservoir behavior under both natural depletion and water injection scenarios. The results from the base case simulation indicated a gradual decline in oil production rate due to reservoir pressure depletion, while the waterflooded case demonstrated a significant improvement in oil recovery performance. The initial oil production rate before flooding was approximately 1500 STB/day, which declined to 500 STB/day before water injection. After waterflood initiation, oil production increased to about 1450 STB/day, accompanied by a gradual rise in water cut from 10% to 70% over the simulation period. Cumulative oil recovery improved from 25% under primary recovery to approximately 45% with waterflooding, indicating a 20% incremental recovery due to secondary recovery efforts. The study highlights the importance of reservoir simulation tools such as CMG in designing and optimizing waterflood operations, predicting production performance, and evaluating reservoir management strategies. It concludes that waterflooding remains one of the most efficient and economical methods of improving oil recovery in mature fields when properly designed using robust simulation techniques.

Queuing theory is essentially the study of waiting in line, including how people behave when they must queue up to make a purchase or receive a service, what types of queue organization move people through a line most efficiently, and how many people can a specific queuing arrangement process through the line within a given time frame. Operational efficiency has a major bearing on profitability, customer satisfaction, and business viability in the competitive dining industry. Queue theory, being a discipline of operations research that addresses mathematical waiting queue analysis, comes in handy here to offer mathematical models to help optimize restaurant delivery systems (Hwang and Lambert, 2009).Queuing theory was developed by A.K. Erlang in 1909 to study telephone network congestion but has since been used to manage complex service systems in various industries (Sztrik, 2012).

Automated gate systems have become essential in modern residential security due to the need for controlled access and reduced manual operation. Traditional manually operated gates often pose safety risks, increase security vulnerabilities, and require physical effort from users. This project presents the virtual design and simulation of an automated residential sliding gate using SolidWorks for mechanical modeling and Proteus for electronic control simulation. The system integrates key mechanical components such as the gate frame, rollers, track, and rack-and-pinion mechanism, alongside a microcontroller-based control circuit designed to operate the motor responsible for gate movement. The SolidWorks simulation was used to analyze the gate’s mechanical performance, focusing on linear motion, component alignment, and the conversion of rotational motor input into smooth sliding action. Proteus was employed to simulate the automation logic, including motor activation, direction control, and stopping at predefined limits. These simulations allowed full validation of system behavior without physical prototyping, reducing cost and eliminating real- world testing constraints. Results from both platforms confirmed that the gate moves smoothly, responds correctly to control inputs, and maintains proper synchronization between mechanical and electronic subsystems. The study demonstrates that virtual simulation tools provide an effective method for evaluating automated gate mechanisms before fabrication. The design also offers a foundation for future enhancements such as remote wireless control, improved safety features, and integration with smart-home systems.

The behaviour of shallow foundations constructed on lateritic soils is of significant importance in tropical regions where these soils occur extensively and are commonly used for civil engineering works. Lateritic soils are highly variable in nature, and their engineering performance is strongly influenced by factors such as mineral composition, moisture content, degree of compaction, and environmental conditions. This variability often leads to challenges in predicting foundation performance and ensuring structural safety. This study investigates the behaviour of shallow foundations on lateritic soils through a combination of field and laboratory investigations. Field studies include soil sampling an in-situ tests to assess the natural state of the lateritic deposits. Laboratory tests are conducted to determine the index properties, compaction characteristics, shear strength parameters, and bearing capacity of the soils. Model and empirical methods are employed to evaluate the load-bearing capacity and settlement behaviour of shallow foundations under different soil conditions. The results of the study establish relationships between key soil properties—such as moisture content, density, plasticity, and strength—and the performance of shallow foundations. The findings provide valuable insight into the load-bearing behaviour of lateritic soils and highlight the importance of proper soil characterization in foundation design. The study aims to contribute to safer and more economical design practices for shallow foundations in lateritic soil environments, particularly in tropical regions.

This study carried out an investigation on the permeability of grain sizes in soil in Ekosodin, Benin City, Edo State. Due to several collapse of structure in the geographical location and occurence of flooding during the past years, it was essential to investigate if the soil in the area is more or less permeable, To know the most approciate or suitable type of foumdation applicable and the right drainage system to be implemented. The permeability of the soil was tested for by the application of an empirical formula called the kozeny carman equation. This equation is composed of the kozeny carman constant ranging between (0.8 to 1.0), specific surface area, and void ratio. The specific gravity and particle size distribution test was carried out to determine the specific surface area, the moisture content test was also carried out to determine the void ratio of the soil, including the atterberg limit test, the results obtained from the test was used to determine the approximate kozeny carman constant to be applied which was done in Civil/Structural Engineering Laboratory in the University of Benin, Benin City, Edo State. The soil sample used for the various test was taken from Ekosodin with the application of the auger to extract soil sample from four boreholes at 0.5m to 1m respesctively. Through this application the permeability of the soil was determined. The results obtained for the permeability of the soil from the above tests listed, were 1.039×10⁻¹⁰m², 1.002×10⁻¹⁰m²,1.123×10⁻¹⁰m²,1.394×10⁻¹⁰m²,3.797×10⁻¹⁰m²,9.922×10⁻¹¹m², 2.476×10⁻¹¹m², and 6.024×10⁻¹¹m² respectively. Indicating that the soil has a very low permeability. Several mitigation and strategies are recommended, such as soil amendments with coarser materials like sand or organic matter to improve permeability, the installation of drainage systems such as perforated pipes or gravel trenches, and proper land management practices should be implemented in the area, Piles or piers, are strongly recommended to reach stable soil below the water table. Comprehensive drainage systems, including perimeter drains, free-draining backfill, and proper surface grading, are essential regardless of foundation type.

In a workplace environment, such as an academic department, knowing the availability of an office occupant remains a persistent challenge for staff and students. Traditional approaches, such as the use of indoor/outdoor tags, are outdated. This research focuses on the design and Implementation of a Real-Time Occupancy Detection and Interactive Staff Availability Display System for Smart Offices. The system uses a Passive Infrared (PIR) motion sensor to detect when the office occupant is seated. The system provides five distinct status messages that can be automatically broadcast using push buttons on the input unit of the device, and the status communicated are: "In a Meeting - Please Wait" - "Available But Busy","Available - Knock First", "In Class - Back Soon", "Unavailable" (Auto-triggered by inactivity or off-hours. Testing was done in different stages of the design process. After construction, the system was tested, and it worked satisfactorily.

Solar energy is a promising renewable energy source that can play a crucial role in addressing global energy challenges and mitigating climate change impacts. This research focuses on assessing the impact of climate change on solar energy potential, specifically in regions vulnerable to environmental shifts. The study employs a multi-faceted approach combining data analysis, modeling techniques, and machine learning algorithms to analyze solar radiation data under varying atmospheric conditions. The methodology involves collecting historical climate data, satellite-based solar radiation data, and ground-based measurements to create comprehensive datasets. Clear sky and all-sky solar radiation parameters such as Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) are analyzed using established models and algorithms. Machine learning techniques are utilized to develop predictive models for solar energy forecasting, considering factors like cloud cover variations, aerosol content, and long-term climate trends. The research aims to provide insights into how climate change trends impact solar energy resources, enabling better decision-making for solar energy infrastructure development and energy policy formulation. By understanding the complex interactions between climate dynamics and solar radiation, this study contributes to the advancement of sustainable energy practices and adaptation strategies in a changing climate scenario