DEPARTMENT OF INDUSTRIAL ENGINEERING

This study investigated the impact of lean manufacturing practices on waste reduction in selected building block industries in Benin City, Edo State. The research was motivated by the persistent challenge of material wastage, process inefficiency, and delays that hinder productivity in the Nigerian building block sector. The main objective was to evaluate how the implementation of lean manufacturing principles such as 5S (Workshop Organization), Kaizen (Continuous Improvement), Total Productive Maintenance (TPM), and Just-In-Time (JIT) influences the level of waste reduction in block production operations. A descriptive survey research design was adopted, and data were collected using structured questionnaires administered to eighty (30) respondents drawn from production, maintenance, and quality control departments across selected block industries. The data obtained were analyzed using the Statistical Package for the Social Sciences (SPSS) and Microsoft Excel to ensure accuracy and robustness. Both descriptive statistics (percentages and mean scores) and inferential statistics (OneWay ANOVA, correlation, and regression analyses) were employed to test the hypotheses and establish relationships between variables. The results revealed that Total Productive Maintenance (TPM) has the strongest and most significant positive relationship accounting for 54% of material waste reduction, 5s showed a moderate positive but marginally significant relationship accounting about 35% of waste reduction, while Kaizen and JIT exhibited weak and insignificant correlations. The ANOVA result with a p-value of <0.001 further demonstrated that the difference in waste reduction across the lean tools and waste reduction are statistically significant. The study concludes that lean manufacturing practices have a moderate but significant impact on waste reduction with TPM emerging as the most influential tool. It recommends that building block production firms strengthens their lean adaptation strategies so as to achieve a higher level of productivity and sustainability.

Many warehouses face inefficiencies due to poor product arrangement and lack of strategic slotting. While studies exist on warehouse optimization, limited research has addressed practical slotting applications in beverage distribution warehouses like CWAY Benin. This study investigates how product slotting can enhance storage efficiency and operations. A descriptive and analytical research design was used, combining primary data from warehouse observations and staff interactions with secondary data from company records and literature. A simulation model analyzed eight representative SKUs using the ABC classification and slotting technique to optimize product placement. Product slotting reduced total daily picking time from 21.5 to 10.2 hours, a 52.6% improvement. Strategic placement based on demand frequency enhanced accessibility, minimized labor time, and optimized warehouse space. The study recommends implementing formal slotting policies and periodic reviews to sustain efficiency.

Post-harvest losses remain a major challenge for fish and agricultural product processors in developing countries due to limited access to efficient drying and preservation technologies. Traditional methods such as open-sun drying expose products to contamination, weather variability, and non-uniform drying, resulting in significant quality degradation and economic loss. This project addresses these challenges through the design, fabrication, and performance evaluation of a hybrid gas and charcoal oven capable of drying fish and selected agricultural produce efficiently and hygienically. The hybrid system integrates two energy sources—liquefied petroleum gas (LPG) and charcoal—to provide operational flexibility, continuous heat supply, and improved temperature control. Locally available materials including mild steel, galvanized sheet metal, wire mesh trays, and glass wool insulation were used to ensure cost effectiveness, durability, and maintainability. The oven was tested with products such as catfish, pepper, and cassava chips. Performance parameters evaluated include temperature distribution, moisture reduction, drying efficiency, fuel consumption, and final product quality. Results revealed that the oven achieved a drying temperature range of 60–65°C with uniform heat distribution across trays. Moisture reduction from 72% to 12% for fish was attained within 6–7 hours under hybrid mode, compared to 10–12 hours in traditional charcoal dryers. A drying efficiency of approximately 60.7% was recorded, demonstrating significant improvement over conventional drying systems. The dried products showed enhanced sensory and hygienic quality with minimized contamination and discoloration. The study concludes that the hybrid oven is a practical, reliable, and sustainable technology for small- and medium-scale food processors, contributing to food security, reduction of postharvest losses, and socio-economic development. Further improvements such as automation of temperature control and incorporation of forced convection are recommended to enhance performance and commercial viability. Keywords: Hybrid oven, drying efficiency, fish preservation, agricultural products, gas and charcoal heating, post-harvest losses, food safety.

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.

This project focuses on the upgrade of an electric bicycle to enhance its performance and range. The objective is to improve the distance travelled and charging power of the bicycle through technical enhancements and component upgrades. Methods include the integration of advanced battery technology, indication upgrade, and enhancements to the control systems. Results demonstrate significant improvements in speed, range, and overall user experience. The findings of this project contribute to the advancement of electric bicycle technology, offering insights into potential upgrades for future models

This study investigates the effect of increasing temperature on the rheological properties of water based drilling mud, which is critical for optimizing drilling operations. The rheological properties analyzed include plastic viscosity (PV), yield point (YP), and gel strength (GS), which play a vital role in the performance and efficiency of drilling fluids. As temperature can significantly influence the flow behavior and stability of the drilling mud, understanding these changes is essential for ensuring the mud’s ability to suspend drill cuttings, maintain wellbore stability, and reduce friction during drilling operations. The study examines a temperature range from 100°F to 160°F, reflecting typical downhole temperature conditions encountered during drilling operations. To achieve this, laboratory experiments were conducted on formulated WBM samples subjected to a controlled temperature range. Rheological measurements were taken using a viscometer at various rotational speeds (600, 300, 200, 100, 6, and 3 RPM) to calculate PV, YP, and gel strengths. The results were analyzed using regression analysis in Microsoft Excel to assess the relationship between temperature and each rheological parameter. Data trends and correlation coefficients were used to determine the degree of influence temperature has on each property. The results revealed that increasing temperature had a notable effect on the rheological behavior of the mud. Plastic viscosity showed a decreasing trend with rising temperature, indicating reduced fluid resistance, while the yield point and gel strengths generally declined, suggesting weakened structural integrity of the mud. The regression models demonstrated strong correlations, supporting the reliability of the findings. The study concluded that temperature significantly influences WBM rheology, which must be accounted for in high-temperature drilling environments to maintain mud performance and ensure operational efficiency.

The growing demand for reliable and sustainable energy solutions in Nigeria has driven the need to investigate renewable energy sources for various agricultural uses. This project centers on designing, analyzing, and implementing a 3.5 kVA solar photovoltaic (PV) system to power essential equipment in a small-scale fish farming operation. The study aims to provide an environmentally friendly and cost-effective alternative to conventional power sources such as fossil fuel generators, which are often expensive to maintain and environmentally harmful. The system was designed to power critical loads, including surface and submersible water pumps, lighting systems, and aeration devices, all vital for maintaining healthy aquatic conditions and ensuring efficient fish production. The design process involved detailed load estimation, component selection, and sizing calculations for the solar panels, inverter, charge controller, and batteries. The selected system components included six 350 W solar panels, a 3.5 kVA inverter, two 12 V tubular batteries (250 Ah each), and an MPPT charge controller. Upon installation and testing, results showed that the system provided a stable power output with an average efficiency of 85%, maintaining continuous operation of the fish farm equipment for over 10 hours daily

This project focuses on the design and building of a solar inverter with a 3.5KVA capacity. Solar inverters convert the variable direct current (DV) output of a photovoltaic (PV) solar panel into utility-frequency alternating current (AC), ready for connection to a home's electrical system. It is essential to solar systems since it permits the use of common AC-powered devices. Solar panels in solar inverters produce direct electricity by moving electrons from a negative to a positive direction. Most home appliances run on alternating current. This AC continuously fluctuates between negative and positive elections. You can adjust the voltage in the AC power according to the equipment's intended use. Solar inverters convert DC to AC because solar panels can only provide direct current.We created a 3.5KVA electrical inverter for this project. Two 22Ah wet cell batteries, a 220V/24-0-24V center-tapped inverter, an MPPT charge controller, and six 300W solar panels make up the architecture of the inverting circuitry assembly. The design provided power for a television, refrigerator (200 watts), air conditioner (1120 watts), and other devices totaling 2465 watts. The system operated at peak efficiency for almost 12 hours while under full load.

In order to produce high-quality products, companies have to engage in a variety of activities, one of which is overseeing every stage of the production process which involves the use of some Statistical Process Control (SPC) techniques, with the aim of locating weaknesses and optimizing outcomes. To reduce operating expenses in production, a corporation must implement quality control through careful monitoring and exacting standards to ensure that the production activity fulfills its objectives efficiently. An effective quality control regime will help the manufacturing workflow proceed without unnecessary disruptions. Hence, this research investigates the application of control chart (P-chart) in monitoring and managing the cutting process during metal sheets designs in God's time engineering LTD, Benin City.. The study aims to establish control limits and identify potential sources of variation in key
parameters like edge quality and Surface finish. By implementing control charts (P-Chart), this research seeks to improve the consistency and reliability of God's time engineering metal sheets design and cutting process, ensuring product quality and minimizing defects. By constructing the P-Chart, this study aims to establish control limits, detect deviations from the desired process parameters, and ultimately enhance the overall quality control system for God's time engineering metal sheets design and cutting process. The findings of this research will provide valuable insights for optimizing the production process, reducing waste (offcuts), and maintaining high standards of product quality

The demand for sustainable and eco-friendly materials in the construction and furniture industries has led to a growing interest in composite materials derived from natural fibres. Bamboo and coir fibres, in particular have shown significant potential due to their renewable nature, low cost, and good mechanical and physical properties.
Fresh bamboo culms were processed and delignified using 0.1M sodium hydroxide solution. Powdered fibre was produced from the delignified and dried bamboo. Experimental composite samples were produced from the bamboo fibre combined with coir fibre and epoxy matrix. A mixture experimental design with three variables serving as mixture components was adopted in the study to plan the experiments and optimize the operating conditions (that is factor levels
of the input variable) of the produced composite with respect to the predicted response parameters. Models were formulated to predict tensile stress, modulus of elasticity, thickness swelling and water absorption. From the results obtained, the optimum percentage mixture of the composite produced were 42.2%bamboo fibre, 35% epoxy and 22.8% coir fibre. With this mix, the values of tensile stress, modulus of elasticity, thickness swelling and water absorption for the composite
produced were obtained as 1.068MPa, 694.450MPa, 1.850% and 20.800% respectively. The composite possessed an overall desirable mechanical and physical properties and it is therefore recommended for deployment.