ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT

The Effect of Zinc Dialkyl Dithiophosphate (ZDDP) Additive on the Tribological Properties of Mahogany (Khaya senegalensis) Seed Oil

2025-09-01T20:27:43+00:00

Mineral oil based lubricants are non-renewable, harmful to health and prone to price fluctuations. Thus, vegetable oils are considered as suitable alternatives to mineral oils for lubricant production. As such, new research into the use of non-edible vegetable oils for lubricant development is advocated to address these challenges. In this study, tribological evaluation of lubricant developed from non-edible vegetable mahogany (Khaya senegalensis) oil for industrial applications was conducted. Interestingly, the oil was characterized, modified for suitability and used to develop lubricants for industrial applications. Additionally, commercially available mineral oil based lubricant SAE 20/W50 was used as a control. The effect of ZDDP additive on the tribological performance of mahogany seed oil based biolubricant developed was also studied. The results show that the developed mahogany seed oil based biolubricant had alkaline pH of 7.56, high viscosity index of 147.12, appreciable viscosity, excellent cold flow of -9.2 ºC. Similarly, the coefficient of friction of the biolubricant developed reduced from 0.095 to 0.090when 1% of ZDDP was added and form 0.095 to 0.087 when 3% of ZDDP was added but the coefficient of friction increased from 0.095 to 0.099 with the addition of 5% of ZDDP. Therefore, developed mahogany seed oil biolubricant was observed to be suitable and environmentally friendly substitutes to mineral oil base lubricant SAE 20/W50 for application in metal cutting and lubrication of gears in food processing industry.

Development of Smart Energy-Efficient Lighting System Using AC-Tied Solar PV with Light Sensitivity Control

2025-09-01T20:57:42+00:00

Unreliable power supply from utility companies in Nigeria undermines the ability of universities to provide quality services, particularly disrupting critical night-time security operations. A persistent issue at Lagos State University of Science and Technology (LASUSTECH), Ikorodu Lagos State is the pervasive darkness during utility load shedding, which directly hinders security personnel. Current solutions like using diesel generators (DGs), hybrid renewable energy supply systems (HRESS), and standalone renewable energy supply systems (SRESS) present significant drawbacks: in addition, DGs are expensive to run while HRESS require substantial upfront investment. Similarly, SRESS used for street lighting, leave many campus areas such as the walkways, offices, and classrooms in darkness at night. To address these shortcomings and enhance night-time security, this research proposes an improved alternating circuit (AC)-tied Solar-photovoltaic (PV)-based lighting system (IACSLS). This research involved systematic modeling, design, simulation, construction, and implementation of the proposed IACSLS at LASUSTECH. The IACSLS successfully generated an average of 580 Wh in four hours of peak sunlight per day from a 145 W solar-PV panel, which is enough to power a 30 W LED bulb throughout the night. The system is 100% renewable and shows a surplus of 6.71 kWh per year, indicating it produces more energy than it needs. The total initial capital cost is estimated to be $65.54, with a projected life span of 10 to 25 years. The deployed system consistently delivered over eight hours of lighting at night during load shedding offering a sustainable, cost-effective solution for improving night-time security activities on university campus.

Effect of Adsorbent Dosage on the Kinetics and Isotherms of Lead(II) Removal from Aqueous Solution Using Corn Husk

2025-09-01T21:09:51+00:00

Heavy metal contamination in water presents a critical environmental challenge, necessitating effective and sustainable solutions. This study explores the use of natural corn husk as an adsorbent for lead (II) removal from aqueous solutions. The corn husk was processed by drying, crushing, and sieving through a 75 μm mesh screen, followed by characterization using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, and Brunauer-Emmett-Teller (BET) analysis. Batch adsorption experiments were conducted to investigate the effect of adsorbent dosage on the isotherms and kinetics of lead (II) removal. The results revealed that the percentage removal of lead increased with both adsorbent dosage and contact time, achieving equilibrium at 105 minutes. The highest removal efficiencies were 73%, 93%, and 96% for dosages of 0.5 g/L, 1 g/L, and 1.5 g/L, respectively. However, the adsorption capacity decreased with increasing dosage, from 111.72 mg/g to 49.06 mg/g. FTIR analysis confirmed the presence of functional groups such as hydroxyl, carboxyl, and ether groups, which contributed to the adsorption mechanism through chemisorption. Similarly, BET results showed a high surface area of 114.55 m²/g with microporous characteristics, while SEM micrographs revealed a dense, rough, and porous morphology with well-distributed active sites. Furthermore, Isotherm analysis demonstrated that the Langmuir model provided the best fit across all dosages, indicating monolayer adsorption with maximum adsorption capacities (qm) ranging from 85 mg/g to 178.57 mg/g. Separation factor (RL) values of 0.2 and 0.19 at lower dosages indicated favorable adsorption conditions, while RL = 0 at 1.5 g/L suggested irreversible adsorption. Kinetic studies confirmed the pseudo-second-order model as the most accurate across all dosages, highlighting chemisorption as the rate-determining mechanism. These findings underscore the significant role of adsorbent dosage in adsorption efficiency and provide insights into optimizing isotherms and kinetics for lead removal using corn husk.

Transesterification of Waste Frying Oil Using Alkaline Activated Waste Marble Catalyst: Effects of Process Parameters, Modeling, and Optimization

2025-09-01T21:26:24+00:00

Through calcination at 600oC for four hours, a novel alkaline activated waste marble heterogeneous catalyst was produced and successfully used for biodiesel synthesis from waste frying oil. To determine the catalyst's appropriateness for the process, Scanning Electron Micrograph (SEM), X-ray Diffraction (XRD), Brunauer-Emmett-Teller (BET) and Fourier Transform Infrared Spectroscopy (FT-IR) were used. Similarly, to determine the waste frying oil's suitability for the transesterification process, FT-IR and GC-MS (Gas Chromatography Mass Spectrometer) were applied in its characterization. The variables (time, temperature, catalyst dosage, methanol/mol ratio and agitation speed) were considered during the transesterification process, and an increase in the process parameters significantly affected the yield. In addition, Response surface methodology (RSM) was applied to optimize the best conditions; the second-order polynomial model is displayed in the Analysis of Variance (ANOVA) with an R2 value of 0.9564, Adj R2 (0.9216), and Pred R2 (0.8257), indicating the acceptability of the model. The optimal yield of biodiesel (86.25%) was obtained at a catalyst concentration of 2.52wt. %, methanol/molar ratio of 6.14mol/mol, time of 1.10 hours, temperature of 59.8oC and agitation speed of 325.2rpm. Interestingly, the WFO biodiesel produced complied with ASTM D6751 requirements.

Using Cloud-Based Application for Assessment of Extreme Threat of Climate Impact on Water Resources: A Case of Vaal River Catchment, South Africa

2025-09-03T11:32:59+00:00

Climate change impact on water resources is becoming a global challenge with persistent escalation of threating weather and changes in precipitation patterns. The study evaluates the extreme impacts of climate change in Vaal River in South Africa. The channel of Vaal River is an area with a critical water resource which supports agricultural, industrial and domestic activities. A three-decade dataset (1993-2023) of precipitation, temperature, streamflow, combined with surface water changes was engaged to examine climate impact on Vaal River water resources. Modified Normalized Difference Water Index (MNDWI) derived from Landsat-5 (1994–2012) and Landsat-8 (2013-2023) imagery on the Google Earth Platform (GEE) was also used to examine the seasonal water stress and availability. The results revealed increasing temperatures and irregular precipitation which are indications of climate altering condition resulting to decreased surface water coverage especially during dry seasons. MNDWI result records a fluctuating value indicating variations in both seasons. Further result revealed weak correlation between precipitation and streamflow, driven by anthropogenic regulations and land use, The MNDWI wet season's linear trendline showed a positive slope of 0.0046 and R² value of 0.26 suggesting water availability over time. Similarly, polynomial trendline for the MNDWI dry season equally shows a moderate coefficient R² value 0.38 Adaptive strategies, including Integrated Water Resource Management (IWRM) and advanced monitoring systems that will include stakeholders, planners and policy makers at all levels are recommended to mitigate these impacts. More effort should be channeled towards sustainable solutions such as tree planting, tree box filter and afforestation practices to reduce the effect of climate change on water resources.

Modeling of Rainfall-Runoff of the Yobe River Basin Using HEC-HMS

2025-09-03T11:43:53+00:00

The Yobe river basin which drains into the Lake has experienced low flows which has been attributed to high evapotranspiration and infiltration rates and the growth of weeds such as Typha Australis in river channels. This research involved modeling rainfall-runoff of the Yobe River Basin in North-Eastern Nigeria using the HEC-HMS 4.8 application package for the purpose of forecasting and determination of low-flow events. Collection of Streamflow data was affected due to insurgency where water resources planning and management within the basin was impacted. Observed Climatic data (Precipitation and Evapotranspiration) were obtained from two gauging stations (Gashua and Gudumbali) for sixteen (16) years (1990-2005). Both climatic and streamflow data were subjected to Normality and Homogeneity tests and used for modeling, with the Climatic data as the Input data, while the Streamflow data were used for calibration and validation. In addition, Streamflow data for three gauging stations (Gashua, Geidam and Damasak) were also obtained from periods between 1990 to 2005 (16) years. The streamflow data were calibrated and validated with NSE (>0.63 to 0.68) in calibration for daily time steps. It was observed that the validation yielded NSE (>0.62 to 0.78). Therefore, HEC-HMS model can be used to project runoff from available climatic data in the Yobe River Basin. Furthermore, soil management of the Catchment area, channel improvement and river augmentation are some of the key strategies of buttressing the effects of high evapotranspiration and infiltration rates, as well as sustaining flow in the river up to the Lake Chad.

Development of a Simulated Annealing-Optimized Adaptive Neuro-Fuzzy Inference System (SA-ANFIS) for Sorghum Seed Planting Parameter Tuning

2025-09-03T12:00:25+00:00

Precision agriculture requires adaptive control systems to optimize planting operations, ensuring consistent planting depth under varying soil and operational conditions. This paper proposes a novel Simulated Annealing-optimized Adaptive Neuro-Fuzzy Inference System (SA-ANFIS) controller for real-time adjustment of planting parameters based on soil moisture and planting speed. The ANFIS framework combines fuzzy logic and neural networks to model nonlinear relationships, while Simulated Annealing (SA) optimizes membership functions and rule bases to enhance control accuracy. Experimental validation demonstrates that the SA-ANFIS controller significantly improves adaptive performance compared to standalone ANFIS controllers, reducing root mean square error (RMSE) by ~18% in dynamic field conditions. The proposed system offers a robust solution for precision planting machinery, enhancing crop yield and resource efficiency. It is, therefore, recommended for autonomous planters

Mapping and Stimulating Flash Flood in the Catchment of Jukskie River

2025-09-03T12:10:34+00:00

Flash floods are among the most severe hydrological hazards, posing significant threats to urban areas worldwide. This study focuses on flash flood mapping of Jukskei River, a catchment in Johannesburg, South Africa, prone to frequent flash flooding. The study employed combination of remote sensing and GIS techniques. The approach follows an analytical framework using satellite imagery and Digital Elevation Models (DEMs) Major extreme rainfall events were analyzed such as rainfall covering 241.6 mm (over 6 hours) and 122 mm (over 4 hours)—corresponding to return periods of 100 and 50 years, respectively. The results of the HEC-RAS simulation were integrated into QGIS to visualize the spatial extent of flooding for the rainfall intensities. The DEM data was processed to identify areas prone to overland flow accumulation, and flood extents were mapped based on the water surface elevations calculated for the 40.27 mm/h and 30.5 mm/h intensities. The mapping results highlighted critical zones where water levels exceeded the riverbanks, potentially endangering residential areas and infrastructures. The findings emphasized the importance of integrated flood risk management strategies to mitigate future hazards and enhance urban resilience.

Spatio-Temporal Dynamics and Forecasting of Urban Land Use and Land Cover Change in Maiduguri Using Google Earth Engine and Machine Learning

2025-09-03T12:29:45+00:00

Urbanization is one of the defining processes of the 21st century, particularly in the rapidly developing country like Nigeria. Google Earth Engine (GEE) and machine Learning techniques were utilized in this study to quantify spatio‐temporal Landuse and Landcover (LULC) dynamics for 2000, 2010, 2020, and 2025 and to forecast the future trends to 2035 in Maiduguri Metropolis. A decadal classification framework was adopted to facilitate long-term LULC analysis, while the inclusion of 2025 enables the capture of emergent post-conflict land transformations and the spatial impacts of the 2024 flood event, which significantly altered land cover configurations. The projection to 2035 maintains a 10-year forecasting interval, ensuring consistency with established temporal benchmarks. Landsat imagery of the selected years was acquired and processed in GEE using the Classification and Regression Tree (CART) algorithm to classify major LULC categories, including built-up areas, cropland, vegetation, wetlands, bare surface, and water body. The overall accuracy for all the assessed years was 80% which indicates high classification reliability. The generated LULC was used to carryout Regression models (linear for five classes and polynomial for cropland) were fitted in Python’s scikit‐learn to project 2035 distributions. The results revealed that built‐up area expanded from 324.8 km² (19.0%) in 2000 to 464.9 km² (27.2%) in 2025, cropland plummeted from 966.9 km² (56.7% ) to 103.6 km² (6.1% ) and bare land surged from 33.6 km² (1.9% ) to 834.1 km² (48.9%). The wetlands and water bodies exhibited fluctuating trends linked to insurgency‐driven land abandonment and the 2024 flood event. Forecasts for 2035 indicate bare land will dominate nearly 72% of the study area, built‐up will reach 517.4 km², and cropland will shrink to 75.3 km². Given these findings, the study strongly recommends integrated sustainable land-use planning and conflict-sensitive restoration strategies to mitigate escalating environmental and socio-economic risks in rapidly urbanizing conflict zones like Maiduguri.

Development and Characterization of Groundnut Shell Powder-Filled Polylactic Acid (PLA) Composites for Sustainable Engineering Applications

2025-09-03T12:41:37+00:00

This paper explores the economic potential of using groundnut shell powder as a filler in biodegradable polylactic acid (PLA) composites. Groundnut shells, an abundant agricultural waste, are proposed as a low-cost reinforcement material to develop sustainable bioplastics. The investigation characterized and determined the mechanical properties of groundnut shell powder (GSP) filled PLA thermoplastic composites. Two different fiber sizes; 150 μm and 212μm were pulverized. The fiber loadings were 10 %, 20 %, and 30 % by weight. The GSP-PLA composites were prepared by use of internal mixer, followed by compression molding process. Tensile and Flexural properties were tested using universal testing machine (UTM). The tensile strength decreased from control sample (0 %) to 10 % by weight fiber loadings for all mesh sizes. In addition, the strength increased from 14.2 MPa and 15 MPa for 150 μm and 212 μm respectively, at a loading of 20%. The modulus increased from 712.9GPa for a control sample (0%) loading to 2344.9GPa at 30% loading for 150 μm. Furthermore, it increased to 1576 GPa for 212 μm. The tensile percentage elongation reduced at 10% loading compared to 0% control sample. Flexural strength and modulus increased for both mesh sizes at all the fiber loadings. However, at 30% fiber loadings, the flexural strength and modulus decreased slightly for 212μm size composites. The hardness of the composites increased for both mesh sizes at all the fiber loadings. It is presumed that the tensile properties performed good at 20% loading as deduced from the findings. while flexural properties increased for all loadings. The study identifies key application areas including packaging, construction, agriculture, and consumer products, highlighting their relevance in supporting eco-friendly startups and rural industries. The innovation aligns with global sustainable development goals (SDGs) number 7 and 8 and offers promising opportunities for green entrepreneurship, especially in agrarian economies.