FACULTY OF ENGINEERING,

Mooring systems remain one of the most critical safety components in marine operations, yet failures continue to occur across ports and offshore environments. These failures often lead to equipment damage, operational disruptions, and, in severe cases, loss of life. This study investigates the major causes of mooring system failures and evaluates the associated risks, with a particular focus on mooring practices in port environments. The research combines a detailed review of mooring system fundamentals with an assessment of human, environmental, and equipment-related factors that influence failure. A structured questionnaire was used to obtain first-hand information from marine professionals, and the responses were analyzed using the Failure Mode and Effects Analysis (FMEA) technique. The findings reveal that human error, inadequate inspection routines, worn mooring lines, and environmental forces such as strong winds and currents are leading contributors to mooring failures. Several failure modes were identified, but the highest Risk Priority Numbers (RPNs) were associated with poor maintenance culture, deviation from safety procedures, and the use of degraded lines. These areas represent the most urgent risks requiring intervention. The study also highlights gaps in compliance with standard mooring system management practices, including inconsistent adherence to the Mooring System Management Plan (MSMP). Based on the results, the research recommends stricter enforcement of mooring safety procedures, regular condition monitoring of mooring equipment, improved crew training, and the adoption of structured risk-assessment tools such as FMEA during operations. Strengthening these areas will significantly reduce the likelihood of failures and enhance the overall safety and reliability of mooring operations in Nigerian port environments.

Rapid urbanization, particularly in places like Nigeria, intensifies waste management challenges due to the inefficiency and high cost of traditional collection. While current smart waste monitoring and management systems offer improvements through IoT monitoring, they often lack advanced sorting and comprehensive management features. This project
directly addresses these gaps by designing an innovative smart waste monitoring and management system that critically incorporates the automated segregation of metals and plastics, alongside enhanced automation and real-time data capabilities. Our prototype is a sophisticated solution that integrates various sensors—including ultrasonic and load sensors for fill-level monitoring and compaction, plus specialized inductive (for metals) and optical/capacitive (for plastics) detectors for sorting—with linear actuators for automated processes and a GSM/GPS module for wireless communication. This setup allows the system to not only monitor bin levels, automatically compact waste, and control the lid, but most importantly, to accurately sort plastic and metal materials at the source. This ensures more efficient resource recovery and recycling. Through rigorous testing of both hardware and software, we will verify the reliability of sensor performance, the effectiveness of automation, the accuracy of communication, and the precision of the waste segregation mechanism. The anticipated outcome is a fully integrated system that successfully demonstrates its capacity to accurately detect fill levels, initiate automated compaction, send prompt location-based alerts, and effectively sort waste. This proven dependability under real-world conditions highlights the system's potential to ignificantly alleviate urban waste management issues by reducing overflow, boosting collection efficiency, increasing recycling rates, and providing a scalable, sustainable solution for residential, commercial, and municipal settings. settings.

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, a major challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is through data compression, which reduces the amount of transmitted data while preserving essential information.
This project explores the integration of machine learning-based data compression techniques to improve energy-efficient transmission in WSNs. A hybrid approach is proposed, combining Run-Length Encoding (RLE) as a traditional lossless compression method with Principal Component Analysis (PCA) as a machine learning algorithm to reduce data redundancy while maintaining accuracy. The study focuses on temperature sensor datasets collected over a specified period, ensuring real-world applicability.
The methodology involves preprocessing raw temperature data, applying Run-Length Encoding (RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model is evaluated based on key metrics such as compression ratio, reconstruction accuracy, and energy savings. Performance comparisons are made with conventional lossless compression algorithms like Huffman Coding and Arithmetic Coding to assess improvements.
Preliminary results indicate that the combined approach achieves a higher compression ratio while preserving critical temperature variations, leading to significant energy savings in wireless
transmissions. This work contributes to advancing energy-efficient data handling in WSNs, making it highly relevant for resource-constrained environments. Future research directions include expanding the model to handle multi-sensor data streams and implementing real-time adaptive compression strategies

The prediction and control of emulsion tightness in oil fields is crucial for optimizing production processes and maintaining operational efficiency. This project focuses on developing a predictive and control model to assess and manage the tightness of emulsions in oil reservoirs. Emulsions, which are mixtures of oil and water, can significantly impact the efficiency of extraction and refining processes, leading to operational challenges and increased production costs. By employing a combination of empirical data analysis, computational modelling, and machine learning techniques, this research aims to predict emulsion behaviour under various reservoir conditions and control the factors influencing emulsion stability. The model incorporates reservoir characteristics, fluid properties, and production parameters to provide realtime insights and effective strategies for mitigating emulsion-related issues. Ultimately, the model aims to enhance oil recovery, reduce operational costs, and improve the overall efficiency of oil field operations.

This project presents a comprehensive study on the reverse engineering of a Pulse Width Modulation (PWM) Low-Frequency Hybrid Inverter used in renewable energy applications. The primary objective was to analyze, understand, and document the internal architecture, components, and operational principles of the inverter through a systematic disassembly and evaluation process. The study focused on identifying key functional sections namely, the oscillating stage, power stage, and transformer stage along with their respective roles in energy conversion and control.

This work is aimed at designing and implementing a Progressive Web App (PWA) based student information management system to improve the security features of the system The methodology used in carrying out the security feature includes, the Two-Factor authentication and hashing method. The major technologies used in the implementation of the system are React.js in designing the frontend for simplicity and modularity, Node.js for the backend to support multiple synchronous activities and MongoDB for the database. The system was designed to provide a more secured method in ensuring the user’s data is safe
from third-party intrusion. The results of the study showed that the PWA based student information management system
was better secured in terms of accessing and retrieving data from the system, without fear of an intruder. The system provided significant improvements in the security features which enabled effective and efficient use of the system.

The study focuses on the formulation and comparing efficiency of plant-based demulsifiers as sustainable substitutes of disintegrating water-in-oil emulsions during crude oil processing. The objective of the study is to determine the efficiency of the selected natural materials in the demulsification process like clove extract, coconut oil and orange and banana peels combined, besides evaluating the effect of external forces on the work of the demulsifier like diesel dilution and magnetic fields. Fourier Transform Infrared Spectroscopy (FTIR) was used to determine chemical composition of each bio-extract and functional groups to develop a relationship between the structure and activity in terms of emulsion destabilization. The quantitative information about the performance trends was gained with the help of experimental data during a
70-minute period of treatment, which was analyzed using the standard deviation statistics (which
are tabulated in the appendix). Findings showed that all the natural demulsifiers were highly
interfacially active due to the presence of surface-active compounds, including phenolics, flavonoids, terpenes, and fatty acids, which influence the interfacial activity of the natural demulsifiers through their ability to destabilize the interfacial films and induce droplet coalescence. The clove extract recorded the greatest demulsification efficiency among all samples that can be explained by its high phenolic concentration and good amphiphilicity. It was found that diesel dilution and magnetic treatment can affect, but not change much of the demulsification behavior, which proves that intrinsic chemical composition is a stronger factor, compared to extrinsic factors. All in all, the research proves that the natural plant demulsifiers have a promising potential to substitute the traditional chemical demulsifiers in the process of crude oil treatment and to provide similar or better results at less environmental and economic expenses. The results highlight the promise of green demulsification technologies as an important measure to achieve sustainable production and processing of petroleum.

Permeability is one of the most important properties in reservoir engineering because it controls how fluids move through rocks and strongly influences production forecasting, recovery efficiency, and field development planning. Conventional methods for estimating permeability depend on core measurements and empirical correlations with porosity and water saturation. While core analysis provides accurate results, it is expensive, time-consuming, and limited to specific depths. Empirical models such as those proposed by Timur, Coates and Dumanoir, and Tixier often fail to capture the complexity of heterogeneous formations like the Niger Delta. This
study develops an integrated framework that combines core

The relevance of communication in our world today cannot be overemphasized. This project is thus aimed at designing and implementing a GSM Network using OpenBTS software paired with an SDR (Software-defined Radio), imitating the GSM Network as we know it. This setup can be used in small-scale operations and can solve the major issue of connectivity in rural areas such as villages and small towns. This arrangement involved a USRP B210 and OpenBTS software running on the Ubuntu 24.04 the latest version of the operating system - as of the time of writing. At the end, the network was launched, subscribers were registered on the test network, and they were able to send messages, which is a supported feature on the GSM Network.

Climate change driven by increasing atmospheric CO₂ concentrations calls for urgent implementation of atmospheric CO2 reduction. However, adsorbents are mostly expensive and energy-intensive, especially for developing nations. Agricultural wastes, especially corn cobs, are a sustainable alternative due to their lignocellulosic composition, natural porosity, and abundance as underutilized biomass. This study investigated the CO₂ adsorption potential of chemically activated corn cob-derived adsorbent through packed bed column experiments. Corn cobs were collected, processed, and activated using potassium hydroxide (KOH) at temperatures between 400-600°C. CO₂ gas was generated in-situ via CaCO₃-HCl reaction and passed through glass columns (2.1 cm diameter, 5 cm bed height) at flow rates of 0.5-2.0 L/min. Four particle size ranges (100, 250, 500, and above 500 µm) were evaluated over 60- minute contact periods at ambient temperature (29±2°C). Characterization via SEM-EDS revealed highly porous morphology with 90.05% carbon content and oxygen-containing functional groups favorable for CO₂ binding. The 100 µm particle size achieved the highest equilibrium adsorption capacity of 5,459 ppm·L/g, while 250 µm particles demonstrated optimal removal efficiency of 48.0%. Breakthrough analysis indicated that smaller particles delayed saturation, with 100 µm maintaining effectiveness beyond 45 minutes compared to 25 minutes for above 500 µm particles. Flow rate influenced performance, with reduced rates (0.5 L/min) compensating for larger particle sizes by increasing contact time. These findings reveal that corn bobs are a viable solution for carbon capture