Progress in Engineering, Material Science and Industrial Systems

Spatio-temporal analysis of the impact of quarry material extraction on land surface temperature: a case study of Anambra East L.G.A

Arinze Christian Iguocha — 2026-04-03

Quarrying has emerged as a leading land-use in the Anambra East Local Government Area (LGA) following the massive urbanization and infrastructural development in the state of Anambra. Although economically important, the activities have caused serious ecological changes especially in terms of thermal environment of land surface. This research paper presents a spatio-temporal assessment of the effect of the quarrying on the Land Surface Temperature (LST) in Anambra East LGA. It will measure the increase in the size of the quarrying locations and determine the thermal reaction of the landscape that follows. The study makes use of multi-temporal satellite images of the Landsat 5 TM, 7 ETM+ and 8/9 OLI/TIRS sensors. The Maximum Likelihood Algorithm was applied to find the change between vegetation and quarrying/bare land by means of the Land Use Land Cover (LULC) classification. The Thermal Infrared (TIR) bands were used to retrieve LST and it was corrected by the land surface emissivity. The correlation analysis was done statistically to establish how the relationship existed between the Normalized Difference Vegetation Index (NDVI) and surface temperature. A preliminary survey shows that there is a substantial increase in the quarrying operations, which are mostly located in the Aguleri and Umueri axes. There is a high positive relationship between the transformation of land to quarrying locations and the appearance of the thermal hotspots. Statistics indicate that LST in quarrying areas is always greater (around 4 ^oC to 8 ^oC) than in the adjacent vegetation cover. The loss of the green lung in the LGA has resulted in the emergence of localized Surface Urban Heat Island (SUHI) effect, which is decreased evapotranspiration and high sensible heat flux. The paper shows that localized climate warming is mostly caused by unregulated quarrying in Anambra East. These results underscore sustainable mining and compulsory reclamation of land and inclusion of thermal environmental monitoring into the environmental impact assessment (EIA) systems of the states as the most appropriate action to curb the ecological degradation in the long term.

Comparative evaluation of drip and sprinkler irrigation systems under varying soil moisture depletion levels for cucumber production

Chike Pius Nwachukwu — 2026-04-11

Climate variability continues to pose significant challenges to agricultural productivity, particularly in water-dependent crops such as cucumber (Cucumis sativus L.). Efficient irrigation management is therefore essential to enhance crop yield under changing environmental conditions. This study evaluated the effects of irrigation method (drip and sprinkler) and management allowable depletion (MAD) levels (50% and 70%) on cucumber yield parameters at the experimental farm of the Department of Agricultural and Bioresources Engineering, Nnamdi Azikiwe University, Awka, Nigeria, in 2025. A 2 × 2 factorial experiments arranged in a Completely Randomized Design (CRD) with three replications was used. Data collected included average fruit weight and fruit length, and were analyzed using two-way analysis of variance (ANOVA) at p ≤ 0.05. Results showed that both fruit weight and fruit length increased as MAD decreased from 70% to 50%, indicating improved yield performance under higher soil moisture availability. Drip irrigation consistently produced higher values of fruit weight and fruit length compared to sprinkler irrigation across all MAD levels. Statistical analysis revealed that MAD had a significant effect on both fruit weight and fruit length (p < 0.05), while irrigation method was not significant for fruit weight but showed a marginal effect on fruit length. The interaction between irrigation method and MAD was not significant for both parameters. Overall, the study demonstrates that maintaining lower soil moisture depletion levels (50% MAD) significantly enhances cucumber yield, with drip irrigation offering superior performance in optimizing water use efficiency and crop development.

Construction of a Low-Cost Electric Resistance Furnace for Simple Casting Operations

Oluwaseyi Omotayo Taiwo — 2026-04-20

This study presents the design, construction, and performance evaluation of a low-cost electric resistance furnace tailored for simple casting operations in educational and small industrial settings. A 4 kg capacity furnace was developed, focusing on energy efficiency, temperature control, and affordability. The design process included the simulation of key furnace parameters using ANSYS Fluent 14.5 to optimize factors such as furnace chamber volume, power supply, wall thickness, and heat resistance wire. The furnace incorporated nichrome wire for the heating element, light refractory bricks for insulation, and a 230V single-phase power source. Performance testing revealed that the furnace could reach melting temperatures of up to 1180°C, with a melting time of approximately 45 minutes for aluminum. The energy consumption was recorded at 1.5 kWh per hour, with minimal heat loss (below 10%) and operational durability for 30 continuous hours. The results indicate that the furnace is a viable, cost-effective solution for small-scale casting operations, offering an efficient alternative to more expensive industrial furnaces. Future improvements could include the integration of automated temperature control and further compact design optimization.

Modelling the Inhibitory Effect of Moringa oleifera on Mild Steel Corrosion Using Nonlinear Adsorption, Dose–Response, and Multivariate Statistical Approaches

Godspower Onyekachukwu Ekwueme — 2026-04-23

Mild steel corrosion is one of the key problems of the industry as it has an economic effect and can easily be deteriorated in acidic conditions, which requires efficient and sustainable inhibitory measures. The application of synthetic inhibitors is, however, restricted by toxicity, environmental issues and cost, and available research on plant-based inhibitors is largely experimental and has not extensively incorporated the use of sophisticated statistical modelling. This knowledge gap explains why a full modelling framework is necessary to integrate experimental evidence and sound analytical methods to enhance predictions and mechanistic insights. The study purposes were to determine the inhibitory activity of the Moringa oleifera extract on mild steel corrosion by establishing the rate of corrosion, weight loss, the efficiency of the inhibitor, and surface coverage; modeling the adsorption behaviour by employing the Langmuir isotherm; examining the relationships between concentration and response; and selecting the most appropriate modelling framework. Primary experimental data collection was performed by gravimetric and gasometric methods, under controlled laboratory conditions. R version 4.4.3 was used to perform data analysis with linear regression, nonlinear least squares (NLS), log-logistic dose response modelling, Principal Component Analysis (PCA), and Akaike Information Criterion (AIC) to evaluate the model. Results indicate strong inhibition performance, with a significant adsorption constant (K = 1.1916, p = 0.0011) and excellent regression fit (R² = 0.98; F = 366.70; p = 1.31 × 10⁻⁶). In comparison to the linear model, the Langmuir model was better (AIC = -13.9402 vs 46.9929) and PCA showed that 99.40 percent of the variance is described by a single dominant factor. The study concludes that Moringa oleifera is a promising green corrosion inhibitor and the combined modelling method has a strong potential of offering an effective framework of optimising corrosion control measures

Expert Method Application for the Prediction and Optimization of the Percentage Elongation of Mild Steel Weldment

Ogochukwu Chinedum Chukwunedum — 2026-04-25

This research study is centered on the optimization and prediction of Percentage Elongation of Mild steel weld metal using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) from Tungsten Inert Gas (TIG) welding process. Welding Current, Welding Voltage and Gas Flow Rate are the process input parameters and the response variable is Percentage Elongation. The final solution of the optimization process is to determine the most appropriate percentage combination of the Percentage Elongation with the optimum values of Current, Voltage and the Gas Flow Rate that will adequately optimize (maximize) the Percentage Elongation of the Mild Steel weld metal. Percent elongation is a mechanical property of a metal that indicates the degree to which a metal may be bent, stretched or compressed before it ruptures. It’s an important quality for metals used in welding, as they need to be able to withstand the high temperatures and stresses involved in the welding process. Optimizing this process is one sure way of producing a quality weld. The RSM model produced the numerical optimal solution for the weldment of Mid Steel (MS). The model Coefficient of Determination (R²) and Adjusted R2 for Percentage Elongation are 93.48% and 87.61% respectively. The Optimal Solutions for the input parameters are; Welding Current, 180.00Amps, Welding Voltage, 21.672Volts and Gas Flow Rate, 15.504L/min. The Optimal Solution for the response variable, Percentage Elongation is 22.111%. From the analysis of variance (ANOVA), it was observed that Gas Flow Rate (GFR) input parameter has more significant effect on the Percentage Elongation response variable. The ANN analysis predicted an optimal solution for the Percentage Elongation response variable to be 18.5044%, with an overall strong correlation (R) between the input factors and the response variable to be 99.89%. Therefore, it is advised that the models be used to navigate the design space.

Engineering Geological Properties of Sandstone and Clay Formations in Tropical Sedimentary Terrains: Implications for Infrastructure Development

Charles Onyeka Nwamekwe — 2026-05-23

Infrastructure development in tropical sedimentary terrains is increasingly constrained by weak subgrades, rapid pavement deterioration, slope instability, gully erosion, foundation settlement, and weathering-driven degradation of sandstone-clay sequences. These failures persist because engineering designs often treat lithology, geotechnics, groundwater, geomorphology, and climate as separate variables rather than interacting controls. This review addresses this limitation by evaluating how sandstone and clay formations influence infrastructure stability in tropical sedimentary environments. A structured review synthesis was conducted on studies dealing with sandstone durability, clay plasticity, grain-size distribution, Atterberg limits, ferruginization, groundwater effects, slope processes, pavement failure, quarry suitability, and sustainable geotechnical planning. The review integrated evidence from engineering geology, sedimentology, geotechnical testing, geomorphology, GIS-based hazard mapping, remote sensing, and emerging machine-learning approaches. Particular attention was given to index properties, strength behaviour, drainage response, weathering intensity, structural discontinuities, and climate-induced deterioration. The findings show that well-cemented quartz-rich sandstones can provide competent foundation and aggregate materials, whereas poorly cemented sandstone promotes infiltration, piping, erosion, and slope weakening. Clay-rich and shale-derived formations are the most critical engineering constraint because high fines, plasticity, low permeability, and moisture sensitivity reduce bearing performance and increase deformation. Reported evidence includes clay fractions of 50-70%, plasticity indices of 21-28%, soaked CBR as low as 1.03-1.60%, slope safety factors below unity, and rainfall-triggered failures after intense antecedent wetness. Ferruginization improves hardness but may redirect runoff when ferricrete restricts infiltration. The review therefore proposes integrated geological, geotechnical, hydrological, GIS, and climate-resilient assessment as the basis for safer infrastructure planning

Intelligent Value Stream Mapping with Embedded Predictive Analytics

Onyedikachukwu Hannah Ifeabunike — 2026-06-01

This review examines Intelligent Value Stream Mapping (VSM) with embedded predictive analytics as an advanced lean manufacturing approach for overcoming the static, retrospective, and manually dependent limitations of conventional VSM. It focuses on how real-time data, machine learning, digital twins, and Industry 4.0 technologies can transform VSM into a dynamic decision-support system for proactive waste reduction and process optimization. The study synthesizes literature on traditional VSM, predictive analytics, data-driven manufacturing architectures, digital value stream twins, process mining, edge/cloud analytics, and lean tool integration. It evaluates data acquisition from sensors, IoT/RFID devices, ERP, MES, and shop-floor systems, together with preprocessing, feature engineering, predictive modelling, simulation, dashboard visualization, and decision-support mechanisms. The review shows that Intelligent VSM strengthens bottleneck prediction, inventory and lead-time management, continuous kaizen, demand-driven scheduling, and integration with Kanban, Andon, Poka-Yoke, Heijunka, and other lean tools. Evidence from the literature shows measurable gains, including 28% reduction in expected delivery time, 67.84% lead-time reduction, 20% productivity improvement, 25% OEE improvement, production balance improvement of 29.07%, and ANN prediction performance with MSE below 0.001 in selected applications. However, adoption remains constrained by poor data quality, heterogeneous system integration, black-box model behaviour, scalability limits, latency constraints, weak alignment with lean simplicity, and lack of standardized implementation frameworks. Future research should prioritize AI-driven optimization, digital twin ecosystems, edge-enabled real-time analytics, interpretable human-centered dashboards, and scalable standardized methodologies.