FACULTY OF ENGINEERING

This study investigates the impact of Nigeria’s tropical environment on the performance of marine diesel engines, focusing on how climatic factors such as air temperature, humidity, atmospheric pressure, and seawater temperature influence engine efficiency Nigeria’s coastal regions are characterized by consistently high temperatures, intense humidity, and seasonal rainfall variations all of which can affect combustion efficiency, cooling capacity, and fuel consumption in marine engines. In this study, an analysis was conducted on the thermodynamic effects of ambient air temperature, humidity, pressure, and seawater temperature on marine diesel engine performance. A simulation framework integrating ISO correction principles with OEM performance curves was developed and applied to model daily and seasonal variations in Nigeria’s tropical environment using meteorological data. The simulated results were validated against manufacturer reference conditions, and based on the findings, technical, operational, and maintenance strategies were proposed to enhance marine diesel engine
efficiency under tropical conditions. Overall, the analysis showed that Nigeria’s tropical climate caused a minor but consistent derating of marine diesel engine performance. Air temperatures between 32–34 °C and humidity above 75 % led to about a 2–3 % reduction in power and a 0.1–0.2 % increase in specific fuel oil consumption compared to ISO conditions. High ambient heat and warm seawater (around 30 °C) reduced air density and charge-air cooling efficiency, resulting in slightly higher fuel flow rates. Despite these effects, the Wärtsilä 8L32 demonstrated stable exhaust temperatures and strong load control, indicating good adaptability to tropical conditions.

This study presents the design and fabrication of a Smart Internet of Thing (IoT)-based feul monitoring system for agricultural tractors. The system aims to improve operational efficiency, minimize fuel theft, and enhance real-time decision-making in mechanized farming. It integrates an ultrasonic fuel level sensor, NodeMCUV3 microcontroller, GPS, and GSM modules to provide continuous fuel data and location tracking. Using Blynk and ThingSpeak IoT platforms, real-time fuel levels, consumption trends, and geographic positions were displayed through web and mobile interfaces. Calibration and testing revealed that the system achieved high measurement accuracy with an error margin of less than ±5%, Wi-Fi data transmission latency between 6–8 seconds, and SMS alert delay of 7–12 seconds. The prototype demonstrated effective performance under field conditions, withstanding vibration, heat, and moisture without data loss. Results confirm that the developed IoT-based system is affordable, reliable, and user-friendly for small- and medium-scale farmers. It enables efficient monitoring of fuel resources, enhances accountability, and supports preventive maintenance through analytics and alert mechanisms. Overall, the system bridges the technological gap in fuel management for agricultural operations in developing regions and contributes to sustainable mechanization practices.

The problem of electricity in Nigeria has become some sort of a nationwide pandemic that has plagued the country for years and continues to do so. With seemingly no end in sight to the electricity crisis, food storage has become very expensive as individuals as well as producers, need to pay a lot of money to run generators to power refrigerators. An alternative means to this would
be a more than welcome development. This project aims to reduce the cost encountered in refrigeration by using vapor absorption refrigeration, which is powered by solar energy.
The vapor absorption refrigerator uses water as its refrigerant, and zeolite is used as the absorbent. The compression system is a network of systems consisting of an absorber and a generator, aimed at compressing a liquid refrigerant-absorbent mixture that requires less work to compress than vapor. The temperature of the evaporator, generator, and condenser was measured and recorded periodically. The performance of the system is evaluated as the ratio of heat removed from the refrigerated space to the heat added to the system at the generator. The refrigerator proved quite functional, achieving a COP of 0.66. This validates the functionality of the system, but it was observed that it took 3 hours of heating to produce a 9°c drop (from 34.2°c to 25.2°c) in evaporator temperature. After 5 hours of heating, there was a 15°c drop (from 34.2°c to 19.2°c) in evaporator temperature. However, the atmospheric temperature was 27°c which means the cooling achieved was not appreciable. The system used in this project suffered from a lot of leakages and heat loss, which directly affected the performance of the system. We recommend
that further studies on techniques that would prevent heat loss, and a meticulous fabrication process to prevent leakages allow. Significant reduction in heat loss would greatly improve the
performance of the waste solar-powered VARS, thereby making it more viable and suitable for domestic and commercial usage

The conventional mechanical governor, while robust, suffers from limitations in dynamic response and optimal performance under varying operational conditions. This project presents the design and implementation of a Smart Mechanical Governor that leverages Machine Learning (ML) for automated, real-time performance tuning. By integrating sensors to monitor key operational parameters (such as speed, load, and fuel flow) and an actuation mechanism for adjustment, the system creates a closed-loop feedback environment. Supervised learning algorithms are trained on historical performance data to model thecomplex, non-linear relationship between governor settings and system output. This ML model subsequently predicts the optimal calibration settings to achieve target performance metrics, such as enhanced stability, reduced settling time, and improved fuel efficiency. The proposed system aims to overcome the static nature of traditional governors, enabling self-optimization that adapts to engine wear and changing environments. The results demonstrate that the ML-driven approach significantly outperforms static calibration, offering a transformative upgrade for internal combustion engines in automotive, aerospace, and industrial power generation applications.

Efficient fuel utilization in mechanized farming is a critical factor influencing both production costs and environmental sustainability. This study investigated the variability of tractor specific volumetric fuel consumption (SVFC) during ridging operations in two contrasting soil textures (loamy sand and clay loam). Field experiments were conducted at ridge heights of 10, 20, and 30 cm, and tractor forward speeds of 4, 6, and 8 km/h. Parameters such as bulk density, cone index, draught force, soil moisture content, fuel consumption rate, and power output were measured and analyzed. Results indicated that SVFC significantly varied with both ridge height and forward speed, showing lower values at higher speeds. In loamy sand soil, SVFC ranged from 0.34 to 0.85 L/kWh, while in clay loam, it varied between 0.27 and 0.66 L/kWh. Statistical analysis using ANOVA confirmed that soil texture, ridge height, and speed had significant effects (p < 0.05) on SVFC. Apairedt-test comparison between the two soil types showed significantly higher fuel consumption in loamy sand than in clay loam under similar operational conditions. These findings suggest that soil texture and ridge geometry play a vital role in determining energy efficiency during mechanized ridging. The study contributes to optimizing tractor operations, reducing fuel
costs, and enhancing sustainable mechanized farming practices in varying soil conditions.

This research was carried out to investigate the feasibility of using recycled waste glass as a partial replacement for coarse aggregate in concrete. The aim was to evaluate the mechanical,
physical and durability characteristics of concrete containing different proportions of crushed waste glass, thereby promoting sustainable construction practices. The methodology involved collecting, cleaning, crushing and sieving waste glass bottles into
particles of 10–20 mm in size. Five concrete mixes were prepared using a 1:2:4 mix ratio, with 0 %, 10 %, 20 %, 30 %, and 40 % waste glass as partial replacements for granite. All specimens were cured in water for 7, 14 and 28 days. Tests conducted included slump (for workability), compressive strength, density, setting time and water absorption capacity. The results showed that workability increased with higher waste glass content, with slump values ranging from 30mm for the control mix to 60mm at 40% replacement. The compressive strength of 30% replacement after 28 days was 20.30 Mpa, hence it was the optimum replacement level. The density of the concrete decreased slightly from 2.612g/cm3 (0%) to 2.391 g/cm3 (40%), indicating lighter concrete at higher glass content. The setting time test recorded an initial setting time of 65 minutes and a final setting time of 172 minutes, both within standard limits. Water absorption decreased from 1.6% at 0% replacement to 1.2% at 30%, showing improved durability and reduced porosity. From the findings, it was
concluded that waste glass could be used effectively as a partial replacement for coarse aggregate in concrete up to 20% without significant loss in strength or durability.

This project presents the design and implementation of an automated waste management system utilizing an Arduino Uno microcontroller, ultrasonic sensors, and a servo motor to enhance efficiency and hygiene in waste disposal. The system continuously monitors the fill level of a waste bin using an ultrasonic sensor, which provides real-time data to the Arduino. When the sensor detects that the bin is nearing capacity or a user is present, the Arduino activates a servo motor to automatically open andclose the bin lid, enabling touchless operation and reducing the risk of contamination. Powered by a 9V replaceable battery, the system is portable and well-suited for environments with unreliable electricity supply. Rapid lid response, with positive user feedback regarding convenience and hygiene. The project highlights the potential for scalable, low-cost smart waste solutions in both urban and rural settings, and lays the groundwork for future enhancements such as IoT connectivity, renewable energy integration,and automated waste sorting for improved sustainability and resource management.

The extraction of crude oil in the Niger Delta generates vast quantities of produced water (PW), a complex, highly saline wastewater containing hazardous heavy metals. Conventional treatment
methods are often prohibitively expensive and inefficient, necessitating the development of lowcost, sustainable alternatives. This research investigates the efficacy of two locally abundant waste
materials, thermally-chemically activated sawdust and thermally activated eggshell, as bioadsorbents for removing iron (Fe) and zinc (Zn) from real produced water sourced from an oil field within the Niger Delta. The study involved preparation and activation of adsorbents, followed by batch adsorption experiments under varying contact times. The produced water was characterized, and experimental data were analyzed using kinetic and isotherm models. Results demonstrated that both adsorbents effectively removed metals. Activated sawdust achieved removal efficiencies of 70.6% for Fe and 95.5% for Zn, while thermally activated eggshell removed 61.4% of Fe and 79.8% of Zn. Kinetic studies revealed that iron adsorption onto sawdust followed a physisorption-driven Pseudo-First-Order model, whereas adsorption onto eggshell followed a chemisorption-driven Pseudo-Second-Order model, indicative of ion exchange with calcium carbonate. Equilibrium isotherm analysis showed that the Freundlich model provided a better fit than Langmuir for both adsorbents, suggesting multilayer adsorption on heterogeneous surfaces. However, anomalous parameters in both models underscored the influence of the complex, multi-component nature of real produced water, causing deviation from ideal model behavior. The study concludes that both sawdust and eggshell are viable, low-cost, and sustainable materials for remediating heavy metals from produced water in the Niger Delta, validating a circular economy approach that transforms local waste into valuable resources for environmental cleanup.

The growing need for sustainable and environmentally friendly materials across various sectors has generated an increased interest in the extraction of bamboo fibres. The primary objective of This study is to tackle the obstacles linked with bamboo fibre extraction and analyze the effectiveness of sodium hydroxide (NaOH) solution in improving the quality of the extracted fibres. The endeavour was initiated to analyze the historical progression of bamboo fibre
extraction techniques, the significance of chemical treatments in fibre processing, and the specific impacts of NaOH treatment on fibre characteristics. The study encompassed an extensive review of pieces of literature to comprehend the context and importance of bamboo fibre extraction, encompassing both conventional techniques and contemporary advancements. The experimental arrangement for distilled water and NaOH treatment was formulated, encompassing variables such as concentration fluctuations at 5%,
10%, 15% and 20% NaOH. The weight of water used was also calculated to be 1000g, 950g, 900g, 850g, and 800g to form solutions respectively. The Fourier Transform Infrared (FTIR) spectroscopy was applied to investigate the chemical structure of fibres before and after treatment, providing important details on the success of the extraction method. Data collection techniques involved sample preparation, maintenance of controlled environmental conditions,
and statistical evaluation of experimental findings.
The outcomes showed that NaOH treatment efficiently dissolved lignin, cellulose, and hemicellulose constituents from the bamboo fibres, resulting in purer and cleaner fibre outputs. FTIR analysis corroborated the partial elimination of non-cellulosic components, with heightened NaOH concentration correlating with increased removal of lignin and hemicellulose. The discoveries from this investigation enhance comprehension of the chemical composition of
bamboo fibres and underscore the potential of NaOH treatment in refining extraction processes. Additionally, the implications of the outcomes for industrial utilization, alongside suggestions for
future research, are comprehensively deliberated

Flow assurance has become one of the most pressing challenges in onshore oil and gas production. It refers to the ability to transport hydrocarbons from the reservoir through pipelines and surface facilities to the point of sale without blockages or interruptions. While the concept first gained traction in offshore systems, onshore operations face their own unique and complex issues. These challenges are linked to aging infrastructure, climatic variations, and the exploitation of marginal and mature fields, which often present high water cuts and unstable emulsions. This study provides a systematic review of the major flow assurance problems in onshore environments, focusing on wax deposition, hydrate formation, asphaltene precipitation, mineral scale, emulsions, and corrosion. Each mechanism was examined in terms of its underlying chemistry and physics, its operational impact, and the mitigation strategies commonly applied. Traditional solutions such as thermal treatments, chemical inhibitors, pigging, and water management remain central, but they are often costly, environmentally intensive, and sometimes unreliable under harsh conditions. The review also highlights the increasing use of innovative technologies, including nanomaterial-based inhibitors, environmentally friendly chemical alternatives, advanced coatings, and digital monitoring supported by artificial intelligence and machine learning. These emerging approaches show promise in reducing chemical volumes, lowering costs, and improving predictive control, although many remain at laboratory or pilot scale. The findings demonstrate that no single strategy is universally effective. Instead, integrated approaches tailored to field-specific conditions provide the best outcomes. For instance, thermal and pigging strategies remain practical in wax-prone pipelines, while low-dosage hydrate inhibitors and AI-based prediction models are more suited for hydrate management in colder climates.