Nnamdi Azikiwe University Journals
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Cascade Organic Rankine Cycle (ORC) Power Production Systems Using a Low-Temperature Heat Source
No full textThe Organic Rankine Cycle (ORC) is a thermodynamic cycle that uses organic working fluids to transform heat into electricity through mechanical work. The performance of the ORC employing five working fluids namely, R123, R245ca, R601a, R601 and R610 was analyzed. It was demonstrated that the selected organic fluids could produce power from a low-temperature heat source. These working fluids were selected for solar heat usage based on their boiling points which were carefully selected to fall close to the reference ambient temperature of 25 oC-32 oC to avoid problems with condensation. The system performance indices (system efficiency, work output, and power output) were calculated using a commercial software named IPSEpro, which adopted an expander model and took pipe and heat exchanger pressure drops into account. R123 as the only working fluid in the three-stage system gave a power output 33.3% higher than that of the two-stage cycle which was 26.7% higher than the single-cycle system, this was the general trend for the two-stage and the three-stage systems. For the four-stage system, however, there was a significant drop in power output The highest power output of 1.68 KW came from the three-stage cascade cycle using R245ca as the only working fluid. The best working fluid combinations for two-stage and three-stage systems were shown to be R123+R245ca and R123+R245ca+R601a respectively
Leveraging Machine Learning and Data Analytics for Predictive Maintenance of Catalytic Converters for Optimal Emission Reduction Performances
No full textCatalytic Converters (CCs) play a crucial role in reducing harmful emissions and ensuring compliance with environmental regulations. Their efficiency impacts vehicle performance, operational costs, and emission standards. However, failures lead to increased pollution and maintenance expenses. This study addresses the challenge of optimizing CC maintenance using predictive maintenance (PdM) powered by machine learning (ML) and data analytics (DA). The objective is to develop ML-driven techniques to predict CC failures and optimize maintenance schedules. Key performance indicators (KPIs) such as exhaust gas composition, temperature, and pressure are monitored through embedded sensors. Collected data is analyzed using statistical methods like regression and clustering to model the relationships between KPIs and CC performance. Machine learning algorithms, including decision trees, random forests, and neural networks, predict degradation and failures. These models are trained on extensive datasets and validated with real-time inputs to enhance forecasting accuracy. Tools like MATLAB, Python, R, and Apache Spark facilitate statistical analysis and ML implementation, handling large-scale data efficiently. Results indicate that predictive models enable timely maintenance, reducing downtime and repair costs while enhancing CC lifespan and emission control. However, challenges include ensuring data accuracy, robustness, and system integration. Future work should focus on improving sensor reliability, refining hybrid modeling approaches, and enhancing real-time analytics to support sustainable automotive maintenance solutions
Improving the operational mechanism of stepper motor, single period induction motor and reluctance motor using fuzzy based system
No full textSome industries have reduced their production capacity as a result of poor performance of stepper motor, single phase induction motor and reluctance motor. This was outwitted by introducing improving the operational mechanism of stepper motor, single period induction motor and reluctance motor using fuzzy based system. This was achieved in this manner, characterizing and establishing the causes of inadequate principle of operations and construction of stepper motor, single phase induction motor and reluctance motor, designing fuzzy rule base that will minimize the causes of inadequate principle of operations and construction of stepper motor, single phase induction motor and reluctance motor, developing an algorithm that will implement the process, designing a SIMULINK model for improving principle of operations and construction of stepper motor, single phase induction motor and reluctance motor using fuzzy based system and validating and justifying the percentage improvement in the reduction of the causes of inadequate principle of operations and construction of stepper motor, single phase induction motor and reluctance motor. The results obtained were the conventional material quality causes of inadequate principle of operations and construction of stepper motor, the conventional manufacturing defects causes of inadequate principle of operations and construction of stepper motor, single phase induction motor and reluctance motor was 10%. Meanwhile, when Fuzzy based system was imbibed in the system, it drastically reduced it to 8.7%. Finally, the percentage improvement in principle of operations and construction of stepper motor, single phase induction motor and reluctance motor was 1.3%. single phase induction motor and reluctance motor was 25%. On the other hand, when Fuzzy based system was incorporated in the system, it automatically reduced it to 21.7%
Development of a Force Table Apparatus for Laboratory Use
No full textThis study presents the design, development, and evaluation of a low-cost, adjustable force table aimed at improving physics education and applied mechanics in resource-constrained settings. Conventional force tables are often expensive, non-portable, ergonomically rigid, and limited in angular accuracy. This project developed a functionally comparable alternative using locally available materials, 3D-printed pulleys, a laminated angular scale, and a height-adjustable tripod stand. The apparatus was tested using repeated three-force equilibrium experiments and benchmarked against a commercial (foreign) model under identical conditions. Performance validation employed statistical tools, including Root Mean Square Deviation (RMSD), standard deviation, paired t-tests, and relative error analysis. Results showed minor deviations from theoretical values, but no statistically significant differences between models at the 95% confidence level (p > 0.05). The observed relative error (~5.17%) and angular deviation (≤0.5°) were within acceptable educational limits. Instructional suitability was evaluated based on the device’s ability to support hands-on demonstrations of vector addition and equilibrium, promote repeatable student-led experiments, and reinforce Newtonian mechanics through physical validation. Its ergonomic design accommodated different user heights and classroom setups. Preliminary feedback from users (via a structured Likert survey) highlighted strong pedagogical value and ease of use, with recommendations for future digital enhancements. Overall, the study confirms that a validated, affordable, and adaptable force table can effectively support experiential learning in mechanics education
Multi-objective optimization of silicon carbide-reinforced 7075 aluminium composite for military-grade firing pin applications
No full textThis research investigates the development and performance optimization of silicon carbide (SiC)-reinforced 7075 aluminium matrix composites for high-stress military applications, with specific interest in general-purpose machine gun (GPMG) firing pins. Building upon the previously optimized base alloy (Al7: 6.1% Zn, 2.9% Mg, 1.2% Cu, 0.18% Cr), SiC particles were introduced at varying weight fractions (0–10 wt.%) and processed via squeeze casting to produce dense, high-integrity composite samples. A four-factor, four-level Taguchi L16 orthogonal array was employed to investigate the influence of process parameters, stirring speed, squeeze pressure, reinforcement preheat temperature, and SiC content on mechanical and tribological properties. Grey Relational Analysis (GRA) was used to perform multi-response optimization with a focus on maximizing hardness, tensile strength, and wear resistance. Results show that the addition of 10 wt.% SiC under optimized processing conditions significantly improved Brinell hardness (127.4 BHN), tensile strength (206.1 MPa), and wear resistance (reduced from 0.6334 mm³/min in the unreinforced alloy to 0.417 mm³/min in the composite). ANOVA results confirmed that stirring speed was the most influential factor, followed by squeeze pressure. Microstructural analysis using optical microscopy and SEM revealed uniform particle dispersion and a refined grain structure with reduced porosity. The optimized composite was used to fabricate a prototype firing pin, which was then subjected to dimensional analysis and preliminary functional assessment. This study demonstrates the viability of low-cost, high-performance aluminium matrix composites (AMCs) for critical defense applications and offers a replicable framework for the development of custom-engineered components in resource-constrained settings
A Comparative Performance Evaluation of SQLite, MySQL, and Firebase for Modern Application Development Using a Parallel Execution Approach
No full textThis study conducted a performance evaluation of SQLite, MySQL, and Firebase (Firestore) databases using a parallel-execution approach. The rationale for the methodology was based on the necessity to maintain identical network and hardware conditions for the execution of queries across the various databases. By using this method, the performance metric concentrated exclusively on the execution time measured during different operations of Create, Read, Update, and Delete which was performed on both text and image data of varying payload sizes ranging from 50KB to 730KB. Results reveal several performance differences for different operations. For text-create and delete operations, Firebase recorded fastest average time (207 ms and 29.3 ms), while SQLite led in read operation (10.8 ms). The same trend was also recorded in working with image data of the same payload sizes. Firebase demonstrated the fastest create (32.3 ms) and delete (5.7 ms) times, while SQLite still attained the fasted read time (11,5 ms). Generally, the results of the experiment carried out on the Spring Boot framework reveals that application developed on top of firebase will perform faster than those developed on top of MySQL and SQLite in terms of data access time
CFD-Informed Machine Learning Prediction of Internal Corrosion Rates in Buried Gas Pipelines Under Turbulent Flow Conditions
No full textInternal corrosion remains a critical integrity challenge in gas transmission pipelines, particularly under turbulent flow conditions where complex fluid–structure interactions accelerate material degradation. This study presents a CFD-informed machine learning framework for predicting internal corrosion growth rates in a buried, unprotected gas pipeline, using a representative segment of the Ajaokuta–Kaduna–Kano (AKK) pipeline system in Nigeria as a case study. Computational Fluid Dynamics simulations were performed using ANSYS Fluent to characterize key flow-induced parameters, including velocity, pressure, temperature, wall shear stress, and turbulence intensity under turbulent operating conditions. These CFD-derived features were subsequently employed as inputs to supervised machine learning models—Artificial Neural Network, Support Vector Machine, Random Forest, eXtreme Gradient Boosting, and linear regression, to predict internal corrosion rates. Model performance was evaluated using standard statistical metrics, including the coefficient of determination (R²), mean absolute error, and root mean square error. Among the evaluated models, the XGBoost algorithm demonstrated the best predictive performance, achieving an R² value of 0.95 with low prediction error. Feature-importance analysis revealed turbulence intensity and flow velocity as the most influential parameters governing internal corrosion development, consistent with established corrosion and flow-accelerated degradation mechanisms. While the high prediction accuracy reflects the effectiveness of combining CFD-derived features with data-driven learning, the study acknowledges the limitations associated with simulated datasets and the absence of detailed corrosion chemistry and inhibitor effects. The proposed framework offers a practical, data-driven tool for corrosion risk assessment and predictive maintenance planning in Nigerian gas pipeline infrastructure. It provides a foundation for future integration with field inspection data and experimental validation to support real-time pipeline integrity management
Comparative Performance Analysis of Smart Drip Irrigation System (SDIS) and Conventional Irrigation System (CIS) for Sustainable Agricultural Production
No full textSustainable agricultural production relies heavily on efficient water management and precise irrigation control. Adaptability with respect to the soil moisture target range, describes the extent to which an irrigation system respond to variations in soil water content to maintain optimal conditions for crop growth. This study presents a comparative performance analysis of a Smart Drip Irrigation System (SDIS) and a Conventional Irrigation System (CIS) with the aim of evaluating their effectiveness in promoting sustainable crop production. In this study, tomato crops were used for the experiment and the experiment lasted for two (2) months (March to April, 2025). The SDIS integrated smart sensors, microcontrollers, and automated control algorithms to monitor soil moisture, regulate water flow, and minimize wastage, while the CIS operates on manual or time-based irrigation scheduling. Experimental data were obtained under similar environmental and soil conditions to assess parameters such as soil moisture stability and percentage improvement in adaptability. The result revealed that SDIS maintained optimal soil conditions of 57.14% more adaptable compared to CIS. This means that the SDIS significantly improved soil moisture uniformity compared to the CIS. Furthermore, the smart system achieved higher irrigation precision and percentage improvement in adaptability, highlighting its potential for improving sustainability in water-scarce agricultural regions. The findings demonstrated that adopting smart irrigation technologies such as SDIS can play a vital role in achieving soil optimal conditions for precision irrigation management and sustainable agricultural production
Comparative analysis of the efficiencies of dye sensitized solar cells from leaf extracts as dye sensitizers
No full textThis review summarizes recent Dye Sensitized Solar Cells (DSSCs) studies using a systematic literature survey and comparative data-table analysis covering natural, synthetic, and metal-based dyes. Reported efficiencies varied widely, with the highest values including 7.19% for astaxanthin, 4.87% for engineered hibiscus, and 4.6% for chlorophyll-rich seaweed extracts. Co-sensitized systems also showed improvements, reaching 3.73%. Overall, natural dyes performed lower than synthetic and metal complexes, but clear trends emerged among dye classes. The general efficiency hierarchy observed is: carotenoids > chlorophyll dyes > betalains > anthocyanins. These trends agree well with existing literature
Development of a Universal Performance Correlation for Low-Cost Ceramic Membranes in Wastewater Microfiltration Using Buckingham Pi Theorem
No full textThe high cost of conventional ceramic membranes limits their widespread adoption in wastewater treatment, despite their excellent performance. While low-cost alternatives derived from natural and waste materials have been explored, existing performance correlations remain empirical and scale-dependent, hindering reliable scale-up. This study addresses this gap by applying the Buckingham Pi Theorem to develop the first universal, scale-independent performance correlation for ceramic microfiltration membranes fabricated from indigenous Nigerian materials (clay, kaolin, rice husk ash, sawdust, and snail shell). The membranes were designed using Response Surface Methodology (Central Composite Design) and evaluated for the removal of Pb²⁺, Cd²⁺, Disperse Yellow 7, and Trypan Blue from synthetic wastewater. Dimensional analysis transformed critical performance data into novel dimensionless groups, generating predictive master curves for permeate flux, pore size, porosity, and flexural strength. The derived correlations demonstrated excellent predictive power, with R² values ranging from 0.85 to 0.96. An optimized membrane formulation achieved a pure water flux of 445 L/m²·h, a flexural strength of 32.1 MPa, a porosity of 49.8%, and a pollutant rejection rate exceeding 97%. This work demonstrates that low-cost, locally sourced precursors can yield high-performance membranes without compromising efficiency. The established dimensionless correlations offer a rigorous and generalizable tool for the rational design and scale-up of sustainable ceramic membrane systems in resource-limited settings