FACULTY OF ENGINEERING

The Internet of Things (IoT) describes a kind of network which interconnects various devices with the help of internet. IoT assists to transmit data with among devices, tracing and monitoring devices and other things. IoT make objects 'smart' by allowing them to transmit
data and automating of tasks, without human interference. A health tracking wearable device is an example of simple effortless IoT in our life. A smart city with sensors covering all its regions using diverse tangible gadgets and objects connected with the help of internet is
another example. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. This project presents a simple method for developing Wi-Fi and Bluetooth Home Automation System that monitors the electrical energy consumption of our houses with realtime tracking. A custom node microcontroller unit (ESP) serves as the main control unit. It is interfaced with sensors that give the real-time status of the surroundings and also monitor
various appliances like lights, fans, etc. Light bulbs and sockets are used to represent the house hold appliances. Communication between human and electrical devices is orchestrated via an android application namely Blynk. Test results of the designed system show that the electrical appliances can be turned On and Off by the Smart arrangemen

In the contemporary landscape of business intelligence (BI), the choice of interactive dashboard tools plays a pivotal role in enabling data-driven decision-making processes within organizations. This study conducts a comprehensive comparative analysis of three leading BI tools: Looker, Tableau, and Power BI, with a specific focus on their interactive dashboard capabilities. The research aims to provide i sights into the strengths and weaknesses of each tool, aiding organizations in making informed decisions regarding their BI tool selection. Key aspects examined include user interface and ease of use, data connectivity, visualization capabilities, data modeling and transformation features, collaboration and sharing functionalities, integration with advanced analytics and machine learning, cost considerations, customization and extensibility options, as well as support and community resources

This research aimed to develop a sustainable and efficient method for making biodiesel from a mix of neem and yellow oleander oils, using a catalyst made from chicken bones. The oils' properties were examined, created and tested the catalyst, optimized the transesterification process, and checked that the biodiesel meets ASTM D6751 and EN14214 standards. The oil analysis looked at free fatty acids (FFA), viscosity, density, iodine value, and fatty acid profiles. Neem oil had an FFA of 5.2%, viscosity of 5.93 mm²/s, and an iodine value of 76.4; yellow oleander oil had an FFA of 3.8%, viscosity of 4.02 mm²/s, and iodine value of 73.86. The catalyst was prepared by calcining chicken bones at 800°C for 3 hours, resulting in calcium oxide with a surface area of 154 m²/g. Tests with SEM, XRD, XRF, FTIR, and BET confirmed it was effective and stable. By optimizing the transesterification process through Response Surface Methodology (RSM), a biodiesel yield of 88.46% was achieved. The optimal conditions identified were a methanol-to- oil ratio of 14:1, a reaction duration of 180 minutes, a catalyst loading of 6% by weight, all maintained at a steady temperature of 65°C

This research presents a comprehensive systematic review of artificial intelligence (AI) techniques and algorithms employed in predictive analytics for drilling hazard management, specifically focusing on stuck pipe incidents, lost circulation events, and wellbore instability. Drilling hazards collectively account for 30-40% of non productive time (NPT) in global drilling operations, costing the oil and gas industry approximately $8-12 billion annually. Traditional monitoring systems rely on reactive, empirical approaches that fail to provide early warnings, while modern drilling operations generate 1-2 terabytes of data per well, creating opportunities for AI-based predictive solutions. Through systematic analysis of 78 peer-viewed research papers published between 2010-2024, this study evaluates the performance characteristics, implementation challenges, and economic viability of various AI algorithms including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, ensemble methods, and deep learning approaches. The research reveals a clear performance hierarchy among AI methods, with deep learning achieving the highest accuracy rates (90-97%) but requiring substantial computational resources and datasets exceeding 50,000 examples. Traditional neural networks demonstrate optimal balance between performance (88-94% accuracy) and practicality, making them the most widely adopted approach in commercial implementations.

The influence of dye concentration, adsorbent dosage, and contact time on the % removal of methylene blue dye (textile effluent) from aqueous solution was optimized and evaluated using a three-variable Box-Behnken design (BBD) in combination with response surface methodology (RSM). Coconut shell was utilized to make the adsorbent, which was then activated with H3PO4 after being carbonized at 600°C for an hour. Three variables dye concentration (50–200 mg/l), adsorbent dosage (g/100 ml), and contact time (10–60 mins), were varied to treat the dye solution. The responses of the linear and quadratic models that were developed for % dye removal from aqueous solution were significantly influenced by all three parameters, according to a statistical analysis of the data with p < 0.0001, the models were significant and demonstrated a strong fit with the experimental data. The adsorbent dosage and contact time had a positive impact on the percentage of dye removal. The process was optimized, and the maximum dye removal of 82% was attained at optimum dye concentration, adsorbent dosage, and contact time of 125 mg/l, 0.55 g/100 ml, and 35 min

This project centers on devising a cost-efficient and highly productive solution for the palm oil industry, with a specific focus on alleviating the labor-intensive palm kernel cracking process. The proposed machine incorporates cutting-edge design principles and advanced materials to elevate its performance and reliability. By subjecting the project to rigorous engineering analysis and prototyping, the primary objective is to attain the utmost efficiency in kernel cracking while
concurrently minimizing waste and energy consumption. Furthermore, the design prioritizes safety, environmental consciousness, and scalability. The successful execution of this project holds the potential to transform the palm oil industry by simplifying the kernel extraction procedure, ultimately ushering in heightened productivity and sustainability within the sector.

The growing incidence of zoonotic diseases and other health risks underscores the pressing necessity for integrated surveillance systems that connect the sectors of human, animal, and environmental health. This initiative centers on the creation and deployment of a Stakeholder Portal for a Digital One Health Surveillance System, which aims to improve real-time data exchange, collaboration across sectors, and informed decision-making. The portal is built using React for the frontend and Python for its backend functionalities, and it features an AI-driven chatbot powered by the Gemini API to enable automatic responses and stakeholder interaction. Notable attributes include secure user authentication with role-based access, engaging data visualization dashboards, and management of real-time surveillance data. Health personnel have unique access to enhanced dashboard features, while general users can interact with publicly available information. The system guarantees data privacy and security by adhering to global standards, offering a scalable, user-friendly platform that can be customized to fit various healthcare settings. This effort plays a crucial role in bolstering public health resilience by promoting effective communication and proactive measures against emerging health threats within the One Health approach

Contamination and ineffective testing methods of drinking water remain a major problem worldwide, increasing the risk of inadequate water quality especially in developing and hard to reach communities. This is because conventional methods of testing water may be either, very costly, time-consuming or complicated for general application. This project aims at finding solution for these challenges by designing a portable handheld water quality monitoring device that measures water pH, turbidity and temperature as well as total dissolved solids (TDS). It combines sensors with an ESP32 microcontroller and the outcomes are displayed on an LCD screen, and a smartphone using BLYNK. Experiments conducted under experimental conditions proved that the proposed system has minimum deviations and MAE values thus giving us consistent and accurate measurements. Overall, the device can be called practically convenient for its intended purposes, however, certain aspects, such as the need to improve power consumption, are still suboptimal, as well as the lack ofpossibilities to fine-tune interface to the individual necessities. This project provides a tangible solution and it’s feasible in real time water quality monitoring thus promoting safer drinking water and better health outcome among the community.

Conventional heat exchangers face a fundamental trade-off between thermal
effectiveness and hydraulic performance. Triply Periodic Minimal Surfaces (TPMS),
enabled by Additive Manufacturing, present a promising solution, offering high
surface-to-volume ratios and complex internal geometries that promote enhanced flow
mixing and heat transfer. This research details the design and numerical analysis of a
heat exchanger utilizing a Gyroid TPMS core. The primary objective was to assess its
thermal-hydraulic performance using Computational Fluid Dynamics (CFD) and
benchmark it against a conventional plate-type exchanger.
The methodology employed a novel computational workflow, beginning with the
generation of the complex implicit geometry in nTopology. This model was then
exported to Ansys Fluent for simulation. A full-scale Conjugate Heat Transfer (CHT)
analysis was conducted, using the k-ω SST turbulence model to accurately resolve the
flow and thermal coupling. The intricate geometry's meshing challenge was overcome
using the Fault-Tolerant Meshing (FTM) workflow.
The validated simulation results demonstrated the superior hydrodynamic efficiency of
the Gyroid TPMS design with a 1370% lower pressure drop and 570% less pumping
power. This presented a clear trade-off, as the conventional plate-type transferred 2.4
times more heat but at a substantial pressure cost. This study successfully validates a
robust computational workflow for analysing complex TPMS geometries and
concludes that these architectures provide a viable path toward developing more
compact, lightweight, and thermally efficient heat exchangers.

The degradation of vegetated lands due to modernization, agricultural expansion, and climate change has become a growing environmental concern in Edo State, Nigeria. Vegetation plays a critical role in sustaining biodiversity, regulating local climate, reducing soil erosion, and supporting livelihoods through agriculture and forest resources. However, rapid population growth, increasing demand for land, and infrastructural development have intensified pressure on natural vegetation across the state. This study aims to map and monitor the spatio-temporal dynamics of vegetated lands in Edo State using satellite remote sensing data within the Google Earth Engine (GEE) platform. Multi-temporal satellite imagery of Edo State was acquired and preprocessed using Moderate Resolution Imaging Spectroradiometer (MODIS) data as the primary source. Vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI), were computed to classify and map vegetated areas and to evaluate vegetation health and density over time. Time-series analysis and pixel-based classification techniques were applied to assess vegetation patterns and to detect changes in vegetation cover between 2015, 2020, and 2025. The NDVI-derived vegetation classes were categorized into dense vegetation, sparse vegetation, and non-vegetated or built-up surfaces to enable clearer interpretation of vegetation transformation.