DEPARTMENT OF INDUSTRIAL ENGINEERING

The provision of safe drinking water remains a critical global challenge, particularly in remote, underserved, or disaster-stricken areas. This project presents the comprehensive design and theoretical analysis of a miniaturized water treatment plant and integrated dispenser system. The design methodology involved a systematic selection and integration of treatment stages: a polypropylene sediment pre-filter for large particulates, a granular activated carbon (GAC) filter for chlorine and organic compounds, a thin-film composite reverse osmosis (RO) membrane for dissolved solids and heavy metals, and a final UV-C chamber for pathogen inactivation. The integrated dispenser system features a storage tank and a manual or electric pump to deliver water on demand. Critical design parameters, including flow rate, recovery ratio, pump specifications, and component sizing, were calculated to optimize the balance between treatment efficacy, energy consumption, and portability. The results of the design analysis confirm that the system is capable of producing water that complies with established drinking water quality parameters. This miniaturized water treatment plants and integrated dispenser system offers a promising, sustainable solution for decentralized water purification, with significant potential for deployment in off-grid and emergency situations

Efficient route planning plays a crucial role in logistics management because it directly affects transportation cost, delivery time, and operational efficiency. Many delivery operations experience inefficiencies due to poorly structured routing strategies, particularly when multiple locations must be served from a single depot. This study focuses on optimizing delivery routes for Viboi Ventures, a Coca Cola distribution depot located in Benin City, Nigeria. The primary aim of the research is to determine the most efficient delivery sequence that minimizes total travel distance while ensuring that each destination is visited exactly once. To achieve this aim, the study obtained delivery location data, determined inter-location distances using Google Maps, and developed an optimization model to evaluate alternative delivery routes. The study adopts a quantitative modelling approach based on the Travelling Salesman Problem (TSP), a well established optimization model used to determine the shortest possible route through multiple locations. Distance data between the depot and nine delivery points were collected using Google Maps and organized into a distance matrix. Manual computations were first conducted to evaluate feasible route combinations. To enhance the reliability of the results, the Branch and Bound optimization algorithm was subsequently applied to systematically evaluate route alternatives and validate the optimal solution. The analysis showed that the manually derived route produced a total travel distance of 13.7 km, while the algorithm based solution generated a shorter distance of 12.2 km both was significantly lower than that of the company’s existing delivery route which is 18.7km. This result demonstrates the effectiveness of algorithmic optimization in improving delivery efficiency and reducing travel distance. The study concludes that applying computational optimization techniques such as the Branch and Bound algorithm, combined with real world geographic data, can significantly improve logistics planning and operational performance for delivery based businesses.

Welding is a vital manufacturing process used in several industries, including aerospace, automotive, and construction. However, residual and induced stresses that develop during welding due to rapid heating and cooling cycles often affect the structural integrity of the weldment thereby reducing the integrity of the structure. The study investigates the application of Artificial Neural Networks (ANN) in predicting the actual maximum stress in Tungsten Inert Gas (TIG) weldments and develop a predictive model capable of accurately estimating the actual maximum stress in TIG welded joints based on key process parameters such as welding current, voltage, and gas flow rate. Twenty (20) experimental runs as generated by the Central Composite Design (CCD) was used to carry out TIG welding on mild steel plates. A Universal Stress Testing Machine was used to measure the actual maximum stress in the weldment and the result was recorded for each experimental run. This experimental result was then analyzed using ANN. ANN trained trained the neural network with fourteen (14) of the observations and use three for network validation and another three for network testing. The best validation performance value of 80.6689 was observed at epoch 5 with an overall performance value of 0.96864. ANN predicted response values was compared with the experimental result and it showed a meritorious correlation with the experimental result trend. The results revealed that the developed ANN model achieved high prediction accuracy with minimal error, confirming its capability to learn and represent the complex nonlinear relationship between the welding input parameters and the resulting actual maximum stress

This project aimed to design and implement UnibenEngVault, a web-based platform created to provide engineering students of the University of Benin with centralized and convenient access to academic resources such as lecture notes, past questions, and tutorial materials. The initiative was motivated by the persistent difficulty students face in sourcing relevant study materials dueto the lack of a unified and structured digital repository within the Faculty of Engineering. The development followed a structured methodology consisting of project planning and approval, problem analysis, system design, development, and deployment. The UI/UX design was created using Figma to ensure an intuitive and visually appealing interface. The frontend was developed with ReactJS, while the backend was implemented using Python (Flask) and PostgreSQL for database management. AWS S3 was utilized for cloud storage, and deployment was achieved through Docker, GitHub Actions (CI/CD), and DigitalOcean Droplets to ensure scalability and reliability. Security measures such as session authentication, CORS configuration, and bcrypt password hashing were implemented, alongside thorough unit, integration, and user testing. The final outcome is a secure, functional, and user-friendly platform that enables students to easily access department- and level-specific academic materials. UnibenEngVault enhances academic collaboration, improves resource accessibility, and provides a sustainable technological solution to the long-standing challenge of academic material distribution within the Faculty of Engineering