FACULTY OF ENGINEERING,

This study investigates the technology employed in the production of Type 4 Liquefied
Petroleum Gas (LPG) composite cylinders and explores potential improvements to enhance
their performance, safety, and cost-effectiveness. Type 4 cylinders, composed of a polymer
liner fully wrapped with fiber-reinforced composites, represent the most advanced generation
of LPG storage vessels due to their lightweight structure, corrosion resistance, and superior
burst strength.
Data for the research were obtained through field observations at Don Mac Limited, review of
standard operating procedures (SOPs), and engineering simulations. The study analyzed
each stage of the production process—from liner molding and surface preparation to filament
winding, curing, testing, and inspection—based on ISO 11119-3 and EN 12245 standards.
Simulation results revealed that substituting high-density polyethylene (HDPE) liners with
polyamide (PA11) and E-glass fibers with hybrid carbon–glass reinforcements increased
burst pressure from 50 bar to 70 bar while maintaining a high factor of safety.
Identified challenges include high material costs, liner gas permeability, and limited local
production capacity. To address these, the research proposes the adoption of nano-filled
epoxy resins, automated filament winding systems, and locally sourced highperformance polymers to optimize strength and reduce cost. These improvements will
advance the development of composite cylinder manufacturing in Nigeria, enabling safer,
lighter, and more sustainable LPG storage solutions

Soil stabilization refers to the process of improving the engineering properties of soil through physical, chemical, or mechanical means to make it more suitable for construction purposes. In this study, the geotechnical properties of lateritic soil collected from the Ovia North East Local Government Area of Edo State were evaluated to determine the effect of rice husk ash (RHA) as a stabilizing agent. The study was prompted by observed pavement and foundation failures within the study area, which were attributed to the poor strength and instability of the underlying lateritic soils. Rice husk ash, an agricultural waste generated from the combustion of rice husks, was utilized in this research to promote sustainable waste management while improving soil performance. The soil samples were mixed with varying proportions of RHA at 2%, 4%, 6%, 8%, and 10% by dry weight of soil. Laboratory tests including sieve analysis, specific gravity, Atterberg limits, compaction, and California Bearing Ratio (CBR) tests were conducted to evaluate the impact of RHA on the soil’s mechanical and index properties. Results showed that the inclusion of RHA led to improvements in the maximum dry density (MDD) and optimum moisture content (OMC), with noticeable increases in CBR values compared to the untreated samples. However, the results also indicated that RHA alone exhibits limited potential as a stabilizing material at higher percentages. The optimum performance was recorded at 5% RHA content, which provided the best balance between strength and workability. Therefore, the use of 5% RHA is recommended for field stabilization of lateritic soils intended for pavement sub-base construction. Furthermore, to achieve better pozzolanic activity, it is advised that RHA used for stabilization be calcined at a controlled temperature range of 600°C to 700°C, as supported by Alabi et al. (2015)

The development of a solar-powered automated peanut coating machine addresses the need for an affordable, energy-efficient, and environmentally friendly solution for small- and medium-scale food processing enterprises. Traditional peanut coating methods are often labor-intensive, time-consuming, and dependent on unreliable grid electricity or fuel-powered systems, leading to increased production costs and inconsistent product quality. This project focuses on the design, construction, and evaluation of an automated peanut coating machine powered by a solar photovoltaic system. The machine integrates a mixing and coating chamber, an electric drive motor, a control unit, and a solar power supply with battery storage to ensure continuous operation. Automation of the coating process improves coating uniformity, reduces manual effort, enhances production efficiency, and minimizes product contamination. Performance evaluation is carried out based on coating efficiency, production capacity, energy consumption, and overall system reliability. The results are expected to demonstrate that the machine provides consistent coating quality while significantly reducing dependence on conventional energy sources and lowering operating costs. The developed system offers a sustainable and cost-effective solution for peanut processing, particularly in rural and off-grid communities where access to reliable electricity is limited. It also promotes the adoption of renewable energy technologies in agro-processing industries, contributing to increased productivity, food quality, and environmental sustainability.

This project focuses on the design and manufacture of an hexacopter drone capable of performing a wide range of applications, from aerial photography and surveillance to cargo delivery and environmental monitoring. The objective of this project is to develop a functional and reliable hexacopter that meets the necessary performance requirements, such as stability, payload capacity, flight time, and control accuracy. Design of this drone comprised of the assembling of different parts together. Such parts include: propellers, motors, hexacopter frame, lithium polymer battery and so on.

This project presents the developments of a vision-based system for aerial pipeline Rightof-Way surveillance using optical/Infrared sensors mounted on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly automated, on-board system for detecting and following the pipelines; while simultaneously detecting any third-party interference. The proposed approach of using a UAV platform could potentially reduce the cost of monitoring and surveying pipelines when compared to manned aircraft. To evaluate the performance of the system, the algorithms were coded using Python programming language. The pipeline endpoints are identified by transforming the 16-bits depth data of the explored environment into 3D point clouds world coordinates. Then, using the Random Sample Consensus (RANSAC) approach, the foreground and background are separated based on the transformed 3D point cloud to extract the plane that corresponds to the ground. Following that, these boundaries were filtered out, after being transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary point, relative to the plane of the ground extracted previously. The filtered boundaries were used to detect the straight lines of the object boundary (Hough lines), once transformed into 16-bit depth data, using a Hough transform method. The pipeline is verified by estimating a centre line segment, using a 3D point cloud of each pair of the Hough line segments, (transformed into 3D). Then, the corresponding linearity of the pipeline points cloud is filtered within the width of the pipeline using Euclidean distance in the foreground point cloud. Then, the segment length of the detected centre line is enhanced to match the exact pipeline segment by extending it along the filtered point cloud of the pipeline. The third-party interference is detected based on four parameters, namely: foreground depth

Most industrial heat exchanger devices, such as condensers, boilers, furnaces, and pipelines generate a certain amount of heat waste due to temperature gradient in the fluid. Leading to reduction of the efficiency, increased emissions, and higher fuel consumption. Thermoelectric generator (TEG) technology offers a sustainable solution by utilising the waste heat to generate electricity. This project focuses on the performance of the TEG process using the counter-flow heat exchanger and process control mechanism to monitor the results evaluated from the different thermal transfer conditions. The primary objective is to recover electricity from heat waste from industrial pipelines or heat exchanger devices, and to assess the feasibility and predict the performance of TEG for sustainable power generation. The experiment components are aluminium heat exchanger tubes, thermal paste, TEG modules (SP1848-27145), thermal paste, Arudino, Voltage and Current sensor, 16X2 LCD display, and Multimeter. The setup of the experiment utilised two aluminium heat tubes: the upper tubes flowed with Hot water, while the lower part circulated with cooling water. The five series modules were mounted between the surfaces of both heat exchanger tubes using thermal paste to ensure heat transfer. The reading of the voltage and current values was displayed using a 16X2 LCD with Arduino Nano or a Multimeter. Experimental results show that the system successfully converted a measurable fraction of waste heat into electrical energy. While the power output and efficiency were relatively low compared to the total heat transfer across the exchanger tubes, the system achieved a maximum output of 9.28 V and 3.17 W, with an efficiency of 14.5%. Based on the empirical model, 5000 thermoelectric modules (TEMs) could generate a maximum power output of 3.17 kW when properly installed, which can improve the overall efficiency in the industrial application.

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.

Lithium-ion batteries have completely changed our world, powering everything from the smartphones in our pockets to electric cars and solar power grids. However, these batteries have a major flaw: they are highly sensitive and can easily get damaged, fail early, or even catch fire if they get too hot, overcharge, or drain too deeply. Traditional battery systems usually react only after a problem has already happened, which is not safe enough for modern technology. The main goal of this project was to design and test a Smart Management System (SMS) that acts as an intelligent brain for lithium-ion battery packs, actively keeping them safe, helping them last longer, and making them work more efficiently. To achieve this, the project used MATLAB and Simulink software to build a detailed, realistic model of a lithium-ion battery pack. Inside this virtual setup, special control logic was built to constantly track individual battery cell voltages, current, and the overall State of Charge (how much energy is left) in real time. Safety boundaries were explicitly programmed into the system to flag an error the moment voltage crossed unsafe limits—specifically setting an upper overvoltage limit of 16.6 V and a lower under-voltage limit of 11.2 V. The simulation was run under different lifelike work situations and heavy loads to see how well the smart system would respond to sudden changes. The results clearly showed that the system works exactly as planned. The moment an unsafe voltage limit was crossed, the smart system reacted immediately, flagging the error and stabilizing the voltage safely within its target boundaries to prevent any damage to the battery cells. The system also smoothly tracked the steady drop in the battery's energy capacity as it discharged over time. Ultimately, this project proves that using an intelligent, software-based management system is a highly reliable and affordable way to protect modern energy systems before moving on to building expensive physical hardware.

A Battery Management System (BMS) is an electronic control system that monitors and manages rechargeable battery packs. Secondary batteries are commonly used as the storage of energy produced by solar panels. However, the utilization of a battery without proper management can cause damage due to overcharging and over-discharging.
The BMS continuously monitors cell voltages, current, temperature, and state of charge while protecting potentially damaging conditions such as overcharging, over-discharging, excessive current, and temperature extremes. Advanced systems incorporate cell balancing to maintain uniform charge distribution across multiple cells, thermal management to regulate operating temperatures, and sophisticated algorithms to estimate battery state of health and remaining useful life.
This study aims to design a battery management system (BMS) on a Valve Regulated LeadAcid (VRLA) battery. The method used was the battery State of Charge (SOC) estimation using Coulomb Counting (CC) method. The results showed that the BMS was successfully designed and implemented to automatically cut-off the current when the SOC value is 100% (charging limit) and 20% (discharging limit)

This project focuses on the design and implementation of an automatic changeover system integrated with contactors and an Automatic Voltage Regulator (AVR) for efficient management of a home solar power system. The system is designed to automatically transfer load supply between the solar inverter, utility grid, and generator in the event of power failure or voltage instability, ensuring uninterrupted power delivery to essential household appliances. The control unit employs electromechanical contactors to achieve seamless source selection, while an AVR maintains stable voltage output to prevent damage to sensitive equipment. A timer/delay relay is incorporated to coordinate the switching process, minimize transient currents, and delay the operation of the alarm siren to prevent false triggers during short interruptions. The project also integrates protective circuit breakers to safeguard the system from overloads and short circuits, improving safety and reliability. The overall design emphasizes efficiency, automation, and simplicity, eliminating the need for manual intervention during power transitions. Testing and evaluation were carried out under various load conditions to verify performance. Results confirmed that the system achieves reliable source transfer, stable voltage regulation, and reduced downtime during source changeovers. The project demonstrates a practical and cost-effective solution for domestic solar power management, promoting energy efficiency and dependable power supply in areas with unstable grid systems.