R. O. EDOKPIA

Modern power generation facilities depend heavily on auxiliary components such as centrifugal pumps, which ensure effective cooling and stable operation of gas turbines. The literature reviewed shows that conventional maintenance strategies reactive and preventive—are often costly and inefficient, leading to unexpected failures and operational losses. Predictive maintenance (PdM) has emerged as a superior, data-driven alternative that uses statistical and sensor-based models to forecast equipment failure. The review further highlighted the growing adoption of PdM techniques in African power systems, where the need for reliability and cost optimization remains high. This study focuses on developing a predictive maintenance model for the cooling water centrifugal pump at the Azura-Edo Independent Power Plant, using statistical trend and regression analysis to predict performance degradation. The research employed an analytical and quantitative design, utilizing two years (2023–2024) of historical operational data from Azura-Edo IPP. Key parameters included ambient temperature, discharge pressure, gas turbine active power, and vibration readings from different pump locations. Microsoft Excel served as the main analytical tool for data cleaning, descriptive statistics, correlation testing, and multiple regression modeling. The regression model related vibration amplitude to operating parameters, producing a mathematical expression capable of estimating degradation levels. A control chart was also developed to monitor vibration stability using calculated upper and lower control limits, forming an early warning system for predictive maintenance intervention. Results from the analysis revealed moderate variability among parameters, with vibration showing the strongest correlation to discharge pressure and turbine power. The developed regression model effectively predicted vibration trends with reasonable accuracy, confirming its suitability for maintenance forecasting. The study concluded that predictive maintenance can significantly improve pump reliability, reduce unplanned downtime, and optimize maintenance scheduling at Azura-Edo IPP. It is recommended that the model be integrated into the plant’s SCADA system for real-time monitoring, with periodic updates to ensure adaptive accuracy and sustainable performance.

Advancements in technology have led to a steady decline in the effort required to produce work, with better designs offering an effective and economical means to achieve desired outcomes with reduced effort. Power screws serve to transform rotary motion into translator y motion, as exemplified by the screw jack, where a minor horizontal force elevates or descends a substantial load. Operating akin to an inclined plane, the mechanical advantage of a screw jack is determined by the ratio of load to effort applied. Adjustment of the jack's height is facilitated by turning a lead screw, achievable either manually or through integration of an electric motor. The whole components is designed on a base plate of relatively good resistance to deformity and act as a support to weight balancing during the operation of the motor
This project analyzes the fabrication of a motorized screw jack by incorporating an electric motor in the screw in order to make load lifting easier. In this fabrication design, the power screw is rotated by connecting motor through universal joint and gear axle to power the screw jack plugged to the automobile 12 V battery source to generate power for the prime mover (motor), which transmits its rotating speed to the power screw to be rotated with required speed reduction and increased torque to drive the power screw. The significance of this project to design a car jack in form of motor operating condition in order to make the operation easier, safer and more reliable in order to reduce health risks especially back ache problems associated with doing work in a bent or squatting position for a long period of time. The motorized screw jack is easy to use by women or whoever had problem with the vehicle tyres along the road. It will also save time and requires less human energy to operate. Based on results, the project provide a motorized jack able to effectively lift a load of 750kg to 1000kg and also provide a safe usage during maintenance of vehicle.

This project explores the quality assurance (QA) practices in Prime Feed and Flour Mill
Limited, aiming to improve product safety, ensure regulatory compliance, and enhance
customer satisfaction. With the increasing demand for high-quality food and animal feed,
alongside the public health threats posed by contaminated or inferior products, it is crucial to
maintain consistent quality throughout production processes. The research investigates the
existing QA frameworks utilized by the quality control sector, assesses their conformity with
national regulatory standards established by NAFDAC and SON, and identifies prevalent
challenges in their implementation. Data was gathered through document analyses from the
laboratory results and log book.
The results recognize that various factors affect quality standards, including insufficient staff
training, inadequate documentation, limited QA infrastructure, and weak enforcement
mechanisms. The study emphasizes key quality indicators such as moisture content, aflatoxin
levels, microbial load, and packaging integrity as vital control points.
Recommendations include regular QA training for personnel, strengthening internal audits,
improving record-keeping systems, and adopting technology for process monitoring. The
study concludes that a more structured and proactive quality assurance system is necessary to
meet both regulatory and consumer expectations in the feed and flour production sector in
Nigeria.

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.