FACULTY OF ENGINEERING

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

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