DEPARTMENT OF INDUSTRIAL ENGINEERING

The study optimizes inventory management in a roofing sheet production company using the Economic Order Quantity (EOQ) model to minimize costs and enhance operational efficiency, with a specific focus on 0.4mm Aluminium coil. In the Nigerian manufacturing sector, where rapid urbanization and construction demand drive material needs, inefficiencies in inventory practices,often result in tied-up capital, production delays, and reduced profitability. These challenges are increased by volatile raw material prices and supply chain disruptions common in developing economies like Nigeria. The primary problem addressed is the lack of a data-driven approach tobalance ordering costs (e.g., procurement and logistics fees) and holding costs (e.g., storage, insurance, and opportunity costs of capital), which undermines financial performance in an industry reliant on standardized products with relatively stable but seasonally influenced demand. The aim is to apply the EOQ model to determine optimal order quantities, evaluate cost savings compared to current practices, analyze improvements in inventory turnover, and assess the impact of key variables like demand, ordering costs, and holding costs. This contributes to sustainable operations in construction-driven markets by demonstrating EOQ as a practical tool for decision making. The findings affirm the EOQ model's effectiveness in manufacturing contexts with predictable demand, such as roofing sheets. By aligning procurement with economic principles, it supports cost efficiency, better cash flow, and competitiveness in Nigeria's construction sector, where Aluminium imports and local production face ongoing challenges. Limitations include the single product focus and exclusion of factors like quantity discounts or demand variability, suggesting avenues for future research integrating advanced EOQ variations. Overall, adopting EOQ can drive operational sustainability and profitability for similar industries.

This study focuses on the design, fabrication, and performance evaluation of a solar thermal dryer developed using locally available materials to enhance the drying of agricultural products in both rural and urban environments. Traditional open-air drying methods commonly practiced in Nigeria are fraught with challenges such as contamination, theft, unfavorable weather, and inconsistent drying rates, leading to significant post-harvest losses. To mitigate these limitations, a solarpowered thermal drying system was developed to harness the abundant solar energy available in tropical regions. The research methodology involved a comprehensive review of literature on solar energy utilization, the development of suitable design concepts, determination of relevant design and environmental parameters, material selection, fabrication, and experimental testing. The solar dryer design integrates a photovoltaic-powered air blower and a thermally heated chamber, providing controlled airflow and consistent heat distribution for effective moisture removal. Findings from related studies and prototype evaluations revealed that solar thermal dryers offer improved drying efficiency, reduced drying time, and enhanced product quality compared to conventional methods. The project demonstrates the potential for affordable, energy-efficient, and environmentally sustainable drying technology adaptable for small-scale farmers and households. Furthermore, the design can be scaled up for commercial use, promoting local manufacturing, job creation, and the wider adoption of renewable energy technologies in Nigeria. Keywords: Solar energy, solar thermal dryer, agricultural drying, photovoltaic blower, renewable energy, moisture removal

This study was undertaken to design and evaluate the performance of an electric motor-driven pellet mill intended for animal feed production. The developed pellet mill comprises several key components, including a feed hopper, pelleting chamber, pellet roll, die plate, and frame. It is powered by a 5 horsepower electric motor and operates utilizing a roll-type extrusion press to
expel the formulated feeds through the die plate. As the pellet rolls rotate, they apply pressure that facilitates the rearrangement of particles, thereby filling the voids of the die plate. During the compression phase, the pressure increases, prompting brittle particles to fracture and malleable particles to deform, which allows them to be processed through the die and emerge as pellets. The pellets subsequently fall due to the impact generated by the rotating die plate. This apparatus is capable of producing pellets with a diameter of 5 mm and a length of 25 mm. An investment of #323,000 is necessary for the procurement of the pellet mill and the construction of its housing. Financial analysis indicates that utilizing the CPU-CARES Formulated Starter Mash for pelleting feeds would be a profitable endeavor, yielding a rate of return of 423% on the capital invested. Furthermore, the benefits realized amount to 16% of the incurred costs, and the initial investment, inclusive of housing, can be recouped in less than three months. Based on these findings, the pellet mill has demonstrated its ability to transform dusty mashed feeds into pellets, producing a considerable quantity of pellets daily.

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 U niversal 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 the neural network using 70%(14) of the observations and used 15%(3) for network validation and another 15%(3) for network testing. The best validation performance value of 80.6689 was observed at epoch 5 with an overall performance value of 0.96864. 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. ANN predicted response values was compared with the experimental result and it showed a meritorious correlation with the experimental result as well as the experimentation trend

Plaster of Paris (POP), known for its affordability and accessibility, exhibits potential as a phase change material (PCM) due to its capacity to store and release thermal energy during phase transitions. However, its efficacy hinges on various factors including particle size, mixing ratio, curing time, and temperature. This study delves into optimizing POP process parameters to enhance its suitability as a PCM, with a focus on thermal resistivity properties. Through systematic experimentation and analysis, we aim to pinpoint the ideal combination of process parameters to bolster thermal resistivity for applications in thermal energy management. Our research commenced with the fabrication of POP molds, where we tailored diverse formulations by adjusting ratios of POP cement, fiber, and water to achieve specific attributes. Subsequent exposure to controlled heat allowed us to meticulously gauge thermal resistivity using precise thermotesting equipment. Analysis of these data enabled us to derive meaningful
insights into material performance under varying conditions. By leveraging response surface methodology and statistical analysis, our investigation pinpointed the optimal blend of water, fiber, and POP cement for maximizing thermal resistance
in fiber-reinforced POP cast mixtures. The development of a predictive mathematical model facilitates accurate thermal performance forecasting across different process parameter configurations, facilitating informed decision-making in material selection and application.

The development and improvement of organic brake pads is the main goal of this project, which
makes use of a special composite made of coconut shells. cow horn, graphite. and epoxy resin. Minitab 17 sonware is used for the optimization process to develop tests that will improve the brake pad's performance characteristics considering the composite matrix. The project begins with the collection and preparation of coconut shells, followed by the incorporation of bone ash. graphite, and epoxy resin into a matrix. The synthesized material is then subjected to a series of comprehensive tests to evaluate its performance. The optimization phase of this projeet employs Minitab 17 software for designing experiments that systematically to maximize performance. The software facilitates the identification of optimal combinations of coconut shell. bone ash, graphite, and epoxy resin proportions, as well as curing conditions, to achieve superior hardness, frictional
properties. and resistance to water absorption. The results shown that the optima component show consist of 36% coconut shell, 25% cow horn. 22% graphite, and 17% epoxy which would give friction coefficient of 0.5 and water absorption
rate of 0.02 having an overall desirability of 0.99. Here, the composite desirability (0.9963) is close to 1, which indicates the settings seem to achieve favorable results for all responses as a whole. The outcomes of this study have the potential to revolutionize the automotive industry by providing a sustainable and high- performance alternative to conventional brake pad materials, characterized by improved hardness, frictional propertics, and moisture resistance.

This project focuses on the design, fabrication, and performance evaluation of a kitchen heat extractor constructed using locally sourced materials. The increasing discomfort and health risks associated with heat and smoke accumulation in domestic kitchens prompted the development of a simple, affordable, and efficient heat extraction system suitable for local households. The design involved a detailed study of ventilation and heat transfer principles to determine the appropriate fan capacity, duct dimensions, and materials that could efficiently extract hot air and cooking fumes from the kitchen environment. Mild steel sheet metal was selected for the main body due to its durability, ease of fabrication, and resistance to heat, while a locally available electric fan served as the suction unit. Other components such as the filter mesh, exhaust vent, and protective casing were carefully assembled to enhance performance and safety. During fabrication, basic workshop processes such as cutting, welding, drilling, and fitting were employed. After The system was assembled and tested for functionality, suction efficiency, noise level, and overall performance under varying kitchen conditions. The performance evaluation showed that the extractor effectively reduced kitchen temperature and smoke concentration within a short period of operation, demonstrating reliable efficiency comparable to imported models but at a significantly lower cost. The project therefore proves that locally sourced materials can be efficiently utilized to design and fabricate a functional kitchen heat extractor, promoting self-reliance, cost-effectiveness, and sustainable domestic technology.