ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT

Effects of Drying Temperatures on Nutritional and Phytochemical Properties of Gongronema Latifolium Leaves

D. O. Amaefule, C. O. Nwajinka, N. N. Mbegbu, C. D. Okpala — Mon, 02 Jun 2025 19:29:19 +0000

This study investigated the effects of drying temperature on the nutritional and phytochemical components of Gongronema latifolium (Bush Buck) leaves; a widely spread edible indigenous leafy vegetable of West Africa. The leaves were dried at room temperature (RTD), in the open sun (OSD) and at 30, 40, 50, 60 and 70 °C in a convective oven (OD). Proximate and phytochemical analyses were done on the products of each OD temperatures, while for RTD and OS drying, the tests were done at their equilibrium moisture content (????????) of the ambient. Statistical analysis was done using MATLAB. The parameters of the fresh leaves with 82.95% moisture content served as the control. Drying decreased the product ???????? to 12.95%, 7.65%, 6.95%, 5.79% and 5.03% in the listed order of the drying temperatures. The proximate and phytochemical contents increased with drying temperature. 70 °C drying temperature gave the highest contents of ash (16.35%), fibre (27.85%), protein (22.70%), carbohydrate (32.84%) and fat (2.42%) in the dried leaves. Same went for the phytochemicals: alkaloid (4.50%), flavonoid (3.92%), saponin (1.46%), tannin (1.39%) and terpenoids (2.72%). Second order polynomial models fitted the data well and gave coefficient of determination (R2) of 0.789 for fibre, 0.869 for fat, 0.895 for protein, 0.921 for ash, 0.923 for terpenoids, 0.941 for moisture, 0.943 for flavonoids, 0.982 for saponnins, 0.987 for alkaloids and 0.992 for tannins content. These results show that there are strong relationships between the proximate and phytochemical composition and the drying temperatures. At their corresponding temperatures, OSD yielded the highest value of saponins (1.45%), tannins (0.64%) and terpenoids (2.45%), RTD gave highest value of alkaloids, flavonoids, carbohydrate (58.38%), protein (17.07%) and fat (1.10%), while OD yielded the highest fibre content (28.32%). The results show that drying methods and conditions should be chosen based on the intended use of the products.

Evaluation of Corrosion Inhibition, Thermo-Oxidative Stability, and Biodegradability of Castor Oil-Based Lubricant

Mon, 02 Jun 2025 00:00:00 +0000

Global lubricant demand is on the increase and the continual consumption of mineral oil-based lubricant has devastating environmental impact. Despite the identification of animal fat and vegetable oil as alternatives to mineral oil-based lubricants, there is concern about its sustainability due to the food-versus-lubricant debate. Thus, non-edible vegetable oil-based lubricant development has become a topical area of research. In this paper, the study of physicochemical, rheological, temperature, thermo-oxidative stability, corrosion inhibition and biodegradability properties of castor oil extracted from Nigerian grown castor bean seeds was conducted using standard test methods. The results show that castor oil has specific gravity of 0.955, free fatty acid value of 19.74 mg KOH/g, pH of 5.76, saponification value of 185.41 mg KOH/g and Iodine value of 92.1 gI2/100g oil. An assessment of the rheological and temperature properties of the castor oil gave kinematic viscosity at 400C and 1000C as 280.6 cSt and 77.5 cSt respectively, viscosity index of 33.4, pour point of -23.20C, cloud point of -12.40C and flash point of 2820C. The peroxide value of the castor oil was 8.92 meq/Kg and it was of corrosion grade 0. The castor oil has higher viscosity at 400C, lower viscosity index, and poor physicochemical properties compared to the SAE 20W50. The properties of the castor oil require improvement except its cold flow, flash point and corrosion inhibition properties. The castor oil is highly biodegradable while the SAE 20W50 has poor biodegradability. Therefore, castor oil conforms to ISO VG220 grade lubricant and qualifies to be called a biolubricant.

Modeling a Solar-Powered IoT Smart Home

A. Raji, F. O. Adunola, A. E. Airoboman

The concept of smart homes has gained more interest in recent years, as the unwavering advancement in IoT technologies enable devices to operate and communicate autonomously. As urbanization accelerate and energy demand increase, on-grid energy system faces significant challenges, including high carbon emissions, energy inefficiency, and reliance on nonrenewable energy sources. The incorporation of solar power into this system not only reduces dependence on traditional energy sources but also promotes sustainability by utilizing clean, renewable energy. The research Present a comprehensive model that addresses these challenges, focusing on energy efficiency, seamless IoT integration, user interaction, scalability. A potable model called SMHome system is powered by solar energy, developed with an algorithm for monitoring and controlling the home autonomously over the internet. The Arduino Nano collects data from various sensors (temperature, motion) and communicates with the ESP8266 for Wi-Fi connectivity. The ESP8266 sends this sensor data to a cloud platform via TCP, allowing remote monitoring and control through a mobile app. Integration with the IFTTT app enables automation, where specific triggers from the sensors can initiate actions, such as sending notifications or controlling devices based on user-defined conditions. The proposed SMHome system is structured in a form that a potable box powered by solar energy can only be easily and efficiently control appliances over the Internet and support home safety with autonomous operation. By integrating solar energy with IoT technologies, the research demonstrates the potential for improved energy management, reduced environmental impact and enhanced quality of life for residents.

Thin Layer Drying Kinetics and Influence of Drying Temperature on the Proximate Composition of Tiger-Nut Milk Powder

N. I. Nwagugu, W. I. Okonkwo — Tue, 03 Jun 2025 08:35:58 +0000

Milk derived from animal sources often contains anti-nutrient elements such as α-lactoglobulin, β-lactoglobulin, lactose and cholesterol, which can contribute to various health challenges. Plant-based milk alternatives, such as tiger-nut milk, offer a healthier option but often face limitations like short shelf life and high storage costs. This study aimed to investigate the drying kinetics and proximate composition of tiger-nut milk powder. Tiger-nut tubers were processed into milk from brown varieties and subsequently dried at 50, 60, and 70°C. Data obtained were fitted to five commonly applied drying mathematical models (Newton, Logarithmic, Page, Henderson and Pabis) to determine the one which predicted the drying kinetics of the milk more accurately. The dried powder was then analyzed for its proximate composition, including moisture content, crude fiber, carbohydrates, protein, lipids, ash, and pH using standardized procedures developed by Association of Official Analytical Chemists (AOAC). The best fit model for the drying kinetics analysis was determined using the coefficient of determination (R²), with the Page model having the highest R² values of 0.998-0.999. This analysis revealed that the Page model accurately describes the drying behavior, with both drying time and equilibrium moisture content decrease as the drying temperature increases. The results also indicated that tiger-nut milk powder contains a moderate protein content (7.12-7.26%), a high carbohydrate content (63.68-64.17%), and relatively low levels of lipids (6.12-6.24%) and ash (1.42%). These findings suggest that tiger-nut milk powder has the potential to be a valuable plant-based food alternative. Future research should focus on determining its specific mineral composition, bioactive compounds, and potential health benefits.

Response Surface Modeling of Soil Moisture Content Under Different Tillage Conditions

C. P. Nwachukwu, C. O. Umobi, C. D. Okpala, E. C. Nwanna — Tue, 03 Jun 2025 11:02:47 +0000

This study investigates the impact of three key factors - irrigation deficit percentage, NPK application rate, and tillage - on soil moisture content, a crucial parameter in agricultural productivity. To achieve this, a field experiment was conducted at Nnamdi Azikiwe University's Department of Agricultural and Bioresources Engineering Experimental Site/Farm Workshop, Awka. The experiment utilized a central composite design in response surface methodology, incorporating three factors: irrigation deficit percentage, NPK application rate, and tillage. This design enabled the researchers to examine the individual and interactive effects of these factors on soil moisture content. The results of the study revealed that the model was highly significant, with an R-squared value of 0.9084. This indicates that approximately 91% of the variation in soil moisture content could be explained by the model. Furthermore, the analysis showed that irrigation deficit percentage, NPK application rate, and tillage were all significant factors influencing soil moisture content. However, the interaction terms between these factors were found to be non-significant. A key outcome of this study was the development of predictive models for soil moisture content under different tillage conditions, namely No Tillage, Conservative Tillage, and Conventional Tillage. These models could be employed to forecast soil moisture content for specified levels of irrigation deficit percentage and NPK application rate under each tillage condition. The findings of this research provide valuable insights into the effects of irrigation deficit percentage, NPK application rate, and tillage on soil moisture content. These insights could be applied to inform agricultural practices in similar regions, ultimately contributing to improved crop yields and sustainable agricultural management.

Synthesis and Simulation of Tannery Wastewater Treatment Process with Biogas Generation Using WRC STOAT Software

A. A. Rasheed, F. G. Yusuf, S. Ogunleye, A. B. Ibrahim — Tue, 03 Jun 2025 11:18:55 +0000

Tannery wastewater is characterized by its high organic load and toxicity, primarily due to the presence of putrescible matter and heavy metals. This study aimed to simulate a comprehensive treatment process for tannery wastewater using Sewage Treatment Operation Analysis over Time (STOAT) software. The quality of treated wastewater from the simulation was evaluated against the National Environmental Standards and Regulations Enforcement Agency (NESREA) discharge standards to meet environmental safety requirements. The treatment system incorporated a multi-stage approach, including physical preliminary treatment units, chemical primary treatment, and biological secondary treatment. Additionally, the system featured a biogas production section and sludge dewatering units to maximize resource recovery. The simulation effectively modeled the treatment processes, capturing operational dynamics and interactions across the treatment units. Key pollutants, such as biochemical oxygen demand (BOD), total suspended solids (TSS), ammonia, and nitrate were monitored. Additionally, the study explored the impact of hydraulic retention time (HRT) on treatment efficiency. The results indicated that all parameters met NESREA's permissible discharge limits at various residence times. Similarly, the observed trends aligned with findings reported in the literature. Furthermore, the biogas yield from the simulation was 0.27 kg/m³ of tannery influent, with a maximum methane purity of 41%. Digested sludge was generated at a rate of 0.072 m³/m³ of tannery influent. However, extending digester residence time did not significantly enhance biogas yield or methane concentration, suggesting the need for process optimization or pre-treatment strategies. This research demonstrates the effectiveness of STOAT as a simulation tool for designing tannery wastewater treatment processes, ensuring compliance with stringent regulatory standards while optimizing resource recovery through biogas generation and sludge management. The findings emphasize the necessity of integrating physical, chemical, and biological treatment processes to enhance contaminant removal and improve the sustainability of tannery wastewater management.

Energy Storage: The Key to Reliable Renewable Energy Grids

S. N. Ukagu, M. O. Atu, I. A. Onyegbadue

Integrating intermittent renewable energy sources into the power grid poses significant challenges to grid stability and reliability. This study examines the integration of Energy Storage Systems (ESS) into power grids to enhance stability and performance. A simulation framework was developed to analyze the technical and economic viability of Battery Energy Storage Systems (BESS) and Pumped Hydro Storage (PHS) systems. The results demonstrate the effectiveness of ESS in alleviating voltage fluctuations, frequency deviations, and grid disturbances. A diversified energy storage portfolio with optimized siting and innovative market mechanisms maximizes the benefits of ESS integration. The study reveals that BESS excel in rapid response times and scalability, while PHS systems offer superior economic benefits. This study pioneers a comprehensive simulation framework integrating technical, economic, and regulatory aspects to optimize Energy Storage Systems (ESS) integration, providing novel insights into maximizing grid stability and performance amidst escalating renewable energy integration. The findings provide valuable insights for policymakers, industry stakeholders, and researchers seeking to optimize energy storage strategies for resilient, sustainable, and efficient power systems.

Improved Energy-Based Efficient Routing Protocol for Underwater Wireless Sensor Networks

Tue, 03 Jun 2025 12:10:22 +0000

Underwater Wireless Sensor Networks (UWSNs) rely on small, energy-constrained sensors deployed at varying sea depths for applications such as surveillance, environmental monitoring, and data collection. However, high energy consumption and end-to-end delay, exacerbated by dynamic depth and turbidity variations, significantly impact communication efficiency. Existing protocols, such as the Neighboring-Based Energy-Efficient Routing Protocol (NBEER), attempt to optimize energy usage but fail to adapt to these environmental changes, leading to reduced network performance. To address this, an Improved Energy-Based Efficient Routing Protocol (IEBERP) was developed, integrating a Distributed Underwater Clustering Scheme (DUCS) for efficient cluster formation and a Strata Adaptation Scheme (SAS) to dynamically reassign displaced nodes to the nearest cluster head (CH) after depth or turbidity changes. The protocol was simulated in MATLAB (R2024b), and results showed significant performance improvements over NBEER, achieving 9.68 TEC, 81.45 PDR, 46.42 E2ED, 234 NAN, and 35,191 NPR, compared to NBEER’s 12.60 TEC, 78.50 PDR, 53.00 E2ED, 198.30 NAN, and 22,500 NPR. These results highlight IEBERP’s enhanced energy management and adaptive clustering, leading to improved routing efficiency, reduced latency, and higher packet delivery rates. This makes IEBERP a promising solution for reliable and energy-efficient communication in dynamic underwater environments.

Optimization of Machining Parameters and Nano-Lubricant Effects on Drilling, Grinding, and Turning Machining Process; Challenges and Future Trends - A Review

P. O. Omoniyi — Tue, 03 Jun 2025 12:21:24 +0000

Any machining operation that generates heat must adequately dissipate it to minimize thermal stresses on the tool-workpiece contact. This inescapable heat production phenomenon, caused by using suboptimal machining parameters and a lack of long-lasting machining lubricant, adversely impacts the finished surface quality, tool destruction rate, and workpiece structure. Therefore, this review focuses on optimizing machining parameters and nano-lubricant effects on the various response parameters such as surface finishing, materials removal rate, tool wear rate, and cutting forces under drilling, grinding, and turning machining. The study reviewed reputable articles from Elsevier, Springer, and other quality outlets. This review cut across the impact of machining parameters and nano-lubricants on the drilling, grinding and turning process. The study also discussed the challenges of optimizing machining parameters during the drilling, grinding, and turning.

Assessment of Vegetation Loss and Land Surface Temperature Changes in Bwari Area Council, Abuja Nigeria

K. J. Ekanem, A. Bukuromo — Tue, 03 Jun 2025 12:40:06 +0000

Urban vegetation is one of the key essential components that contributes to ecological balance and environmental sustainability. However, due to rapid urbanization in the last few decades, there has been cases of vegetation loss which has increased the land surface temperature (LST) and affected the environmental sustainability. Thus, the aim of this study is assessing the impact of vegetation loss on Land Surface Temperature of Bwari Area Council during the periods of 1990-2021, using geospatial techniques. The support vector machine (SVM) supervised classification algorithm was used to classify Satellite imageries into land use land cover (LULC) maps according into buildup area, water body, vegetation and rock/bare ground using ArcGIS Pro. All the classified LULC maps had an overall accuracy of more than 90% with the overall Kappa coefficient also more than 0.9. The analysis of LULC estimation suggests a significant increase in Built-up areas (+ 255.45%) and a reduction in Vegetated areas (-75.17 %) from 1990 to 2021. Brightness temperature, Land Surface Emissivity (LSE) and Normalized Difference Vegetation Index (NDVI) were computed to estimate land surface temperature. The results show that the LST was higher in the regions of built-up areas and rock/bare ground but lower in vegetated areas with the maximum temperature of the study area increasing from 37.11 °C in 1990 to 58.87 °C in 2021. Correlation between land surface temperature and NDVI/NDBI for the study periods was carried out. The results show that correlations between NDVI and LST are rather weak negative, but there is a strong positive correlation between NDBI and LST. These results call for implementation of policies to control rapid urban growth in Bwari Area Council and preserve vegetal covers and as well an extension of the implementation of the Abuja Master Plan to Satellite Towns around Abuja.

Performance Evaluation of Yolo Object Detection Models for Automated License Plate Recognition

O. F. Odeyinka, W. A. Raheem, C. O. Folorunso, A. Fajinmi

This paper outlines an evaluation and comparison of three different You Only Look Once (YOLO) object detection models - YOLOv3, YOLOv8, and YOLOv10 for use in automated license plate recognition (ALPR) systems. To analyze these models, a total of 261 license plate images were collected from the car park of an auditorium inside the University of Lagos, Nigeria. Metrics of each model (accuracy, precision, recall, F1 score, and training efficiency) were used to measure the performance of the models. Results indicates that YOLOv8 (accuracy of 86.9%, precision score 100%, recall of 87%, and an F1 score of 0.93) significantly outperformed the other models, demonstrating its potential as a robust solution for object detection. In contrast, YOLOv3 had an accuracy of 62.1%, precision of 75%, a recall of 78.3%, and an F1 score of 0.766, reflecting balanced performance but slower training times. YOLOv10, despite being the latest version, showed mixed results, achieving an accuracy of 43.2%, a precision of 47.5%, a recall of 82.6%, and an F1 score of 0.603. This study highlights the critical importance of model selection based on specific application needs and suggests that further optimization may enhance the capabilities of YOLOv10 for future developments in ALPR systems.

Seismic Behaviour of High-Rise Buildings Equipped with Fluid Viscous Dampers: A Review

I. A. David, K. Mohammed, N. Muazu, M. S. Nadro — Tue, 03 Jun 2025 13:26:35 +0000

Earthquakes pose a significant threat to structural integrity and human safety, necessitating effective seismic mitigation strategies. This review paper explores the effectiveness of Fluid Viscous Dampers (FVDs) in mitigating seismic responses in high-rise structures. FVDs, as passive energy dissipation devices, enhance structural resilience by reducing inter-story drift, base shear, and overall structural accelerations without significantly altering stiffness. The study compares FVDs with alternative damping systems, including Tuned Mass Dampers (TMDs), Hysteretic Dampers (HDs), Friction Dampers (FDs), and Lead Rubber Bearings (LRBs), highlighting the superior adaptability and efficiency of FVDs in high-rise applications. Placement strategies for FVDs, their integration with other damping systems, and their effectiveness under varying seismic intensities are examined. Findings indicate that uniform distribution across all stories ensures balanced energy dissipation, while targeted placement at lower levels enhances efficiency in specific structural configurations. Also, hybrid approaches, such as combining FVDs with BRBs or base isolation, show promising outcomes in optimizing seismic resilience. The review suggests the importance of further research into computational optimization and practical implementation, torsional irregularities, uniform load distribution in high-rise structures on FVD performance particularly in developing regions like Nigeria, where seismic risks are evolving.

A Review of Blended Non-Edible Green Oil-Based Lubricants for Enhanced Performance in Two-stroke Engines

A. A. Oyedeji, M. S. Abolarin, S. A. Lawal, A. A. Abdullahi, I. O. Sadiq — Tue, 03 Jun 2025 13:40:54 +0000

The increasing environmental concerns associated with petroleum-based lubricants and growing demand for environmentally friendly solutions have spurred research into non-edible green oil-based lubricants, particularly for their application in two-stroke engines. Two-stroke engines, while offering advantages in power-to-weight ratio, pose environmental challenges due to their inherent design of the oil-fuel mixture. By utilizing non-edible sources, these lubricants help avoid competition with food crops, ensuring that edible oils remain available for human consumption, while non-edible oils present a promising solution. Study have shown green oil enhances mechanical and thermal braking efficiency by 13% and 27%, while it reduces emission of CO, CO2 and HC approximately by 62%, 54% and 44% respectively compared to conventional lubricants. This review explores the properties of various non-edible green oils, including their physicochemical characteristics and potential as base stocks for lubricants production. Furthermore, it explores the challenges and opportunities associated with these non-edible green oil-based lubricants. Furthermore, it analyses previous research on the application of green oil-based lubricants in two-stroke engines, focusing on their impact on engine performance, emissions, and wear. The review also discusses the role of additives and blending in improving the overall performance of these lubricants and highlights the need for further research to optimise their application in two-stroke engines. Finally, the review identifies key research gaps and future directions, emphasizing the need for systematic investigation of blend ratios, exploration of environmentally friendly nano-additives, and rigorous engine testing to unlock the full potential of blended non-edible green oil-based lubricants for sustainable two-stroke engine applications. This review underscores the potential of blended non-edible green oil-based lubricants as viable solutions for enhancing performance in two-stroke engines and encourages further research into their long-term effects and commercial viability.

Rainfall and Streamflow Dynamics in the Borno Region of the Lake Chad Basin: A Hydrological Assessment

B. O. Sanyaolu, U. A. Ibrahim, A. N. Alkali — Tue, 03 Jun 2025 13:59:11 +0000

This study assesses the water resources potential within the Maiduguri catchment of the Lake Chad Basin by analyzing a 20-year using rainfall data between 1999 to 2018 and streamflow data between 1981 to 2000 (each over a 20 year period). The results indicates that monthly rainfall was generally higher between 1999 and 2011, followed by a significant decline from 2012 to 2018. This shift suggests a progressive extension of aridity from the Lake Chad region into the Sudan Savannah, likely influenced by the effects of climate change. Flooding events were recorded in 16 of the 20 years analyzed. Streamflow analysis of River Ngadda, which traverses Maiduguri, revealed an average discharge of 6,201.2 m³/s over the study period, with a 7-year span of flooding. Additionally, storage apportionment data were evaluated to inform water resource management in the region.

Modelling of a Real-Time Aerial Surveillance System for Quadcopter Application Using Machine Vision

K. Okhiria, J. D. Jiya, I. S. Sintali — Tue, 03 Jun 2025 14:15:41 +0000

Unmanned Aerial Vehicles (UAVs) have gained significant traction for real-time surveillance applications such as object tracking and search-and-rescue missions. A key enabler of these capabilities is the integration of a real-time, highly accurate object detection system. While available two-stage detectors like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN achieve high detection accuracy and precise localization by dividing the image into regions and classifying each region, they are often slow, complex, and resource-intensive, making them unsuitable for real-time applications. To address this limitation, this research utilizes a Darknet-based YOLOv3 (You Only Look Once, Version 3), a state-of-the-art algorithm for real-time object detection in videos, live feeds, and images. Leveraging a deep Convolutional Neural Network (CNN), YOLOv3 efficiently learns features and predicts object locations and class probabilities in a single pass, ensuring high-speed and reliable detection. A real-time aerial surveillance system for quadcopter application using machine vision is proposed. The training dataset, obtained from the internet and self-taken images from a camera, was manually annotated into three critical categories: suspect, bandit, and weapon. The dataset was subsequently divided into training and testing subsets. Experimental results demonstrate that the proposed system achieves outstanding detection accuracy, with an overall mean average precision (mAP@0.5) of 93.82%, precision of 94%, and recall of 83%. Compared to a ResNet-50-based Faster R-CNN model, the YOLOv3-based approach outperformed, achieving success rates of 0.81 for the Bandit class and 1.00 for both Suspect and Weapon classes. This research improves drone-based AI for real-time object detection in security systems, enhancing efficiency and adaptability. It also creates specialized datasets, including a tailored bandit dataset, to support future UAV security research.

Modeling Thermal Death Time (D – Value) Of Bacillus Cereus in Acha (Digitaria Exilis) Starch Flour

W. A. Adebayo, K. A. Taiwo, O. Alakija, O. C. Olatunde — Tue, 03 Jun 2025 14:29:37 +0000

This study investigated thermal death time (D – value) of Bacillus cereus in acha starch flour, with the aim of providing thermo-bacteriological data that would enhance the safety of acha starch flour. The data would therefore serve as a guide to potential food processors, engineers and scientists thereby promoting the starch usage in food development and formulation beyond its present status. Acha starch was prepared, stored under hygienic conditions and sterilized in an autoclave prior to its thermal treatments. The sample water activity (aw) was then adjusted, and the value confirmed via the aw meter. Design Expert 13 for window was used for the experimental lay-out, comprising three inactivation temperatures and aw values with all experiments conducted in triplicate. The Bacillus cereus thermal destruction characteristics were obtained by plotting number of survivor (CFU/g) against time and corresponding D-value was determined. The D-values obtained were analyzed descriptively and inferentially using Turkey’s posthoc test (Design-Expert 7.00) for Window and fitted into a linear equation representing the dependent and independent variables. The D-value ranged from 20.4 to 12 min. as water activity and destruction temperature changed from 0.55 and 92.1 °C to 0.65 and 80 °C, respectively. The interactive effects of water activity and destruction temperature on natural logarithm of D-value of Bacillus cereus in acha starch flour was linear. The maximum D-value was observed at 80 °C temperature when the water activity was 0.55. This study, therefore, provides valuable thermo-bacteriological data that could be employed as a guide to potential food processors, scientists and engineers in order to improve consumption safety of the product.

Effect of Rainfall and Fertilizer Application Timing on Nutrient Exports from a Maize Field

M. Ogunremi, T. P. Abegunrin, T. P. Abegunrin, A. Akintola, I. A. Abdulsalam — Tue, 03 Jun 2025 14:47:46 +0000

Nutrient losses from agricultural fields are significantly influenced by fertilizer management practices and rainfall patterns. Gaining insight into these effects supports the development of effective mitigation strategies. This study investigated the effect of rainfall and fertilizer application timing on nutrient export from a maize field. Daily rainfall data were obtained from Ogun-Osun River Basin Development Authority (OORBDA) meteorological station at Igboora. The field experiment included early, split, and late single fertilizer application treatments, specifically assessing their impact on nitrate loss. Runoff from each treatment plot was collected using plastic gutters leading to wooden tanks. Samples were manually drawn at the onset, midpoint, and conclusion of each runoff event, provided rainfall exceeded 10 mm. Runoff samples were analyzed for sediment concentration using gravimetric techniques, while nitrate, ammonium, total dissolved nitrogen, and total dissolved phosphorus were measured using standard colorimetric methods. Rainfall events ranged from 29.7 mm to 393.8 mm. The analysis revealed that nitrate concentrations in runoff averaged 35.3±20.55 mg/L, ranging from 5.67 to 78.41 mg/L. Early fertilizer application resulted in an increased average nitrate concentration of 41.4 mg/L. In terms of sediment-bound nutrients, particulate phosphorus concentrations averaged 10.09±7.17 μg/g, with values between 0.37 and 24.09 μg/g. Particulate nitrogen concentrations averaged 13.15 ± 7.87 μg/g, ranging from 3.07 to 31.04 μg/g. Also, it was observed that fertilizer treatments led to an increase in particulate phosphorus concentrations, averaging 10.83 μg/g after the initial application. The observed influence of rainfall and fertilizer timing on multiple nutrient pathways highlights the need for integrated nutrient management strategies, especially in tropical agro-ecosystems.

Response Surface Modeling of Soursop Rich Seed Pyrolysis for Optimum Bio-oil Production

Wed, 04 Jun 2025 09:21:08 +0000

This research investigates bio-oil production from soursop seed through pyrolysis, with process modelling and optimization conducted using RSM via Box-Benkhen design. Soursop seeds were oven-dried, ground, and sieved into various particle sizes within 1.0 to 6 mm range before undergoing pyrolysis at temperatures between 400 to 600 °C under inert nitrogen gas flow rates between 1.0 to 1.5 L/min. A Box-Behnken design under Response Surface Methodology (RSM) was used to model and optimize the effects of temperature, particle size, and inert gas flow on bio-oil yield. Proximate and ultimate analyses characterized the feedstock, while SEM revealed a porous structure favorable for pyrolysis. Bio-oil was characterized using FTIR and GC-MS to identify key functional groups and fatty acid composition. Proximate analysis showed the seeds had high volatile matter and fixed carbon, indicating good potential for pyrolysis. Ultimate analysis revealed carbon, hydrogen, nitrogen, oxygen, and sulphur contents of 51.29%, 5.90%, 0.50%, 42.30%, and 0.01%, respectively. Scanning Electron Microscopy (SEM) showed a rough, porous structure with oil-like droplets on the surface, which enhanced pyrolysis efficiency by providing a larger reactive surface area and improving devolatilization rates. The experimental design considered temperature, particle size, and gas flow rate combinations, with the bio-oil yield as the response. Results showed that increases in these parameters significantly affected bio-oil production. The maximum observed yield of 33.1% was achieved at 500°C, 6 mm particle size, and 1.0 L/min gas flow. The RSM model showed a high degree of fit with an R² value of 0.9875, adjusted R² of 0.9715, and predicted R² of 0.8007. Optimization predicted a maximum yield of 31.43% under conditions of 461.84°C, 3.84 mm particle size, and 1.02 L/min flow rate, with a desirability of 1.0. Experimental results closely matched these predictions, validating the model. Similarly, FTIR analysis indicates that the predominant monounsaturated fatty acid made up 45.55% of the total fatty acid content, which depicts that the oil belongs to the linoleic acid group. Furthermore, the FTIR analysis reveals that the alkene group contributes to increased reactivity and combustion efficiency, boosts the octane number of the bio-oil, and decreases the boiling point of the oil. Therefore, the FTIR and GC-MS analysis findings confirm that the bio-oil was within ASTM specifications.

Tilapia Fish Scale-Derived Hydroxyapatite Inhibitor for Copper Corrosion: Electrochemical, Adsorption, and Mechanistic Investigations

O. S. I. Fayomi — Wed, 04 Jun 2025 09:59:14 +0000

This investigation analyzes hydroxyapatite (HAp) derived from tilapia fish scales as a green and sustainable corrosion inhibitor for copper in 1M HCl. While fish scales are an abundant biowaste source, the use of the scales' in assessing its corrosion inhibition capability will provide an eco-compatible option for the protection of industrial metals. The hydroxyapatite was purified using a multi-step process consisting of deproteinization, alkaline treatment, and high-temperature calcination under 1000°C to yield a purified nano-HAp powder. Open Circuit Potential (OCP), Linear Sweep Voltammetry (LSV), and Tafel polarization were used to determine the corrosion inhibition efficiency under various temperatures (30°C, 40°C, and 50°C) and different concentrations (0.2 g, 0.4 g, and 0.6 g). The results showed an extensive reduction in the current density of the corrosion and the rate of the corrosion, with a maximum inhibition efficiency of (~95%) occurring under the 0.6 g concentration. The analysis of the adsorption showed that the inhibitor conformed to the Freundlich and Temkin isotherms, inferring multilayer adsorption and strong surface interactions. Optical micrographs corroborated the protective capability of the inhibitor through reduced roughness of the surface and reduced pitting. This study established that hydroxyapatite obtained from fish scales is an effective alternative, and eco-compatible corrosion inhibitor, contributing to waste reduction and green chemistry.

Synchronized Analysis of Biodiesel from Khaya Senegalensis Oils Using FTIR, GC-MS and NMR at Top Parametric Conditions

Z. D. Ishaya, J. Samuel, G. E. Ede, G. E. Ede, D. S. Yawas, J. Y. Bwede — Wed, 04 Jun 2025 10:22:13 +0000

Alternatives to costly fossil fuel have witnessed rekindled interest because of the depletion of fossil reserves and environmental concerns. In this study, non-edible vegetable oil from Khaya Senegalensis (KS), which grows well in semi-arid zones was converted to biodiesel via transesterification under the reaction condition of 1.25 % catalyst and 1:6 Oil-methanol molar ratio. FTIR, GC-MS and NMR techniques were concurrently used to monitor common or similar peaks in the conversion process in an extensive cross comparison to validate other techniques' results. Furthermore, the composition of conventional diesel with the vegetable biodiesel produced were simultaneously analyzed. The results showed that conversion peaks were aligned, and successful delivered 82.02 % yield. The results from FTIR, GC-MS and NMR investigations all agreed, on the conversion of triglyceride molecules in the crude KS oil to fatty methyl esters in the biodiesel. Both produced biodiesel and conventional diesel were observed to contain alkanes and alkenes functional groups, that impart good fuel properties.

Transitioning to Sustainable Energy: The Promise of Solar Power – A Review

J. N. Onah, G. O. Jemiriayigbe — Wed, 04 Jun 2025 10:43:31 +0000

Over time, the global energy landscape has shifted from localized, experimental systems to complex, interconnected power grids. Understanding the historical trajectory of these developments provides crucial insights into shaping a sustainable energy future. In light of escalating energy demand, environmental degradation and fossil fuel depletion, there is an urgent need to transit toward cleaner, more sustainable sources of electricity. While renewable energy technologies offer promising alternatives, countries like Nigeria face unique challenges in identifying the most viable options for large-scale deployment. This study employs a structured literature review to examine both the historical evolution and current landscape of power generation technologies. The review encompassed conventional systems (e.g., fossil fuels, nuclear) and renewable sources (e.g., hydro, wind, geothermal, solar). Findings show that past power transitions were driven by more than just technological superiority—they were shaped by economic, institutional, and societal factors. Drawing from this, the study identifies solar power as the most promising renewable option for Nigeria, due to its abundant sunlight, declining costs, and ease of deployment across diverse locations. Compared to wind, tidal, and geothermal energy, solar presents a more practical and scalable solution for off-grid and rural electrification. This insight is reinforced by historical parallels, such as the role of complementary innovations and institutional support in the dominance of alternating current power systems. Solar power stands out as Nigeria’s most feasible pathway toward sustainable and resilient energy development. Lessons from historical power transitions highlight the importance of aligning technological innovation with supportive policy, infrastructure, and public engagement. As the country navigates its energy future, prioritizing solar investment and integration will be vital for achieving environmental sustainability, economic growth, and long-term energy security.

Effect of Lantana Camara, Cassia Occidentalis and Rucinus Communis Seeds and Leaves Extract as Corrosion Inhibitors of Mild Steel in 2M HCL Solution

E. T. Andy, A. A. Hammajam, I. S. Aji — Wed, 04 Jun 2025 10:58:13 +0000

Corrosion phenomena, control, and prevention are significant scientific issues that require ongoing attention due to the increasing demand for metallic materials in various technological developments. The use of natural inhibitors is an attractive option for preventing corrosion due to their environmental friendliness, cost-effectiveness, and ease of sourcing and renewability. This research investigates the potential of locally sourced, non-edible plants as corrosion inhibitors. Specifically, extracts from Lantana camara, Cassia occidentalis, and Ricinus communis seeds and leaves were tested on mild steel in a 2M HCl solution to determine their corrosion prevention potency and compare the inhibitive properties between the leaves and seeds of each plant. The maceration method was employed for extraction using ethanol as the solvent. Corrosion tests were conducted using the weight loss technique to determine the corrosion rate of mild steel coupons over a 14-day period, with measurements taken at 24-hour intervals. The results indicate that Cassia occidentalis leaves and Lantana camara leaves exhibit lower corrosion rates compared to their respective seeds. Conversely, Ricinus communis seeds showed a lower corrosion rate than its leaf extract. All inhibitors demonstrated notable inhibitive properties, with Cassia occidentalis leaves displaying the highest inhibitive efficiency, likely due to its high phytochemical constituents.

Investigation of Plantain Extract for Enhancing Copper Durability in Acid-Based Corrosive Environments

O. S. I. Fayomi — Wed, 04 Jun 2025 11:07:27 +0000

This study investigates the application of plantain peduncle extract (PPE) as a green corrosion inhibitor of copper in 1 M hydrochloric acid (HCl) using electrochemical, adsorption, and surface analysis techniques. Ethanol-extracted PPE was tested at concentrations of 0.1–0.3 mL and temperatures of 30–50°C. Electrochemical tests, such as potentiodynamic polarization and open circuit potential (OCP) measurement, proved PPE to inhibit corrosion current density (Jcorr) by a maximum of 88% and anodically shift corrosion potential (Ecorr), with the maximum inhibition efficiency of 89.5% occurring at 0.3 mL PPE and 40°C. similarly, adsorption experiments confirmed that Freundlich isotherm (R² = 0.906 at 40°C) best described the process, indicating heterogeneous multilayer physisorption, also confirmed by Gibbs free energy values (ΔGads = -16.58 to -19.78 kJ/mol). In addition, optical micrographs confirmed these findings, with minimal pitting and smooth surfaces in ideal conditions (0.3 mL PPE, 40°C), compared to extensive corrosion in uninhibited samples. Statistical comparison using Analysis of Variance, (ANOVA) revealed the significant influence of concentration (p < 0.0001) and temperature (p = 0.0048), with their interaction (p = 0.0213) showing the necessity of balanced operating parameters. While PPE functioned well under middle temperatures, efficiency reduced at 50°C (IE% = 80.2%), reflecting thermal limitations.

Tribological Evaluation of Canarium Shweinfurthi (African Elimi) Oil as Lubricant in Metal Forming

Wed, 04 Jun 2025 12:16:56 +0000

Reducing cost of production arising from high energy due to friction and tool failure using cheaper readily available and environment friendly lubricant is evaluated in this study. An experimental evaluation of tribological properties of Canarium Shweinfurthi (African Elimi) was carried out using Ring Compression Test. This was carried out using Boron Nitride as additive to determine the best quantity that can be added in vegetable oil lubricants. The formulated oils were applied to the die/ring surfaces as they were compressed in the Califonia Bearing Ratio (CBR) using digital display Testometric Universal Testing Machine. The results show that friction factors for the developed lubricant range between m = 0.3 and 0.4 while the frictional coefficient results show μ = 0.07 to 0.09 which compared favorably to referenced (mineral) oil which has m = 0.4 and μ = 0.12. Additionally, frictional coefficient obtained from the developed lubricant are within the range for vegetable lubricants such as groundnut oil, palm oil, palm kernel and shear oil which range from 0.072 to 0.3. Furthermore, it was observed that Canarium Shweinfurthi oil could be used as substitute to mineral based oils that are currently in use as lubricants in manufacturing process. Therefore, industrial organizations can take advantage of the benefits this natural, environmentally friendly, biodegradable and low toxicity oil lubricant presents.

Predictive Modeling and Simulation of Battery Degradation in Power Systems

M. A. Gana, A. A. Warude, A. Bukar — Wed, 04 Jun 2025 12:35:42 +0000

In modern power systems particularly those incorporating renewable energy sources like solar and wind, batteries are essential for balancing supply and demand, stabilizing the grid, and enabling energy storage during low-demand periods. This research introduces a comprehensive modeling and simulation framework aimed at predicting how long batteries will last by examining various degradation mechanisms, including capacity fade, temperature effects, internal resistance growth, and state of charge cycling. A hybrid approach that combines electrochemical, thermal, and mechanical degradation models was used to simulate how batteries age under different operating conditions. The simulation results showed that lithium-ion batteries experienced a 12.5% capacity fade after 500 charge-discharge cycles under normal operating conditions, and the degradation rate increased to 20% in high-temperature environments (45°C). Additionally, it was observed that an 8.7% increase in internal resistance significantly affected efficiency. Furthermore, the result revealed that the adopted predictive models achieved an impressive accuracy of 94.2%, allowing for a reliable estimation of the remaining useful life (RUL) of the batteries. These findings highlight how advanced modeling techniques can really enhance battery management strategies with reduced maintenance costs and boost the reliability of power systems. Therefore, the hybrid degradation models showed an impressive predictive accuracy, surpassing recent benchmarks in the field, where older methods showed accuracies of between 91% and 93% using machine learning and physics-informed neural networks.

Biogas Production Using a Mixture of Avocado Seeds and Cassava Peels Blended with Cow Dung

A. E. Adeleke, E. E. Anjuwon, S. O. Giwa — Wed, 04 Jun 2025 12:45:13 +0000

Increase in the cost of fossil fuels as a source of energy, coupled with environmental pollution associated with their consumption, calls for the search for alternative energy sources. Therefore, this study focused on the construction and performance evaluation of a biodigester to produce biogas as a renewable energy source for domestic use. A 24-litre biodigester was constructed and used in this study. The biogas was produced using a mixture of cassava peels and avocado seeds with cow dung as inoculum under mesophilic conditions. The cassava peels and avocado seeds were mixed in the ratio 5:1. The experiment was carried out under mesophilic conditions, while the mass and composition of the produced biogas were measured. The findings showed the production of 256 g of biogas corresponding to 0.438 m3. The biogas produced was found to contain 64.4% of methane, 5% of water vapour, 0.9% of O2 content, 560 ppm of CO, and 478 ppm of H2S. interestingly, a relatively higher methane content was observed in this study than in previous studies. Thus, implying the influence of the choice of the agricultural wastes (cassava peels and avocado seeds in this case) and inoculum used as substrate which enhanced anaerobic digestion process in connection with the improved biodigester. These results revealed that biogas generation could be achieved using locally sourced agricultural wastes and thus promoting green and renewable energy in agreement with the Sustainable Development Goals. Therefore, Biogas production would not only provide an alternative source of energy but would also aid in farmland and marketplaces' waste management.

Barriers to Adoption of Construction 4.0 for Circular Economy: A Qualitative Approach

A. A. Soyingbe, A. C. Jago — Wed, 04 Jun 2025 19:11:02 +0000

Despite global advances in Construction 4.0 technologies for supporting circular economy (CE) goals, adoption remains limited in developing countries particularly Nigeria where contextual barriers remain poorly understood. Digital tools such as Building Information Modelling (BIM) and Internet of Things (IoT), drive sustainability through resource efficiency, waste minimization, and a closed-loop material flow. However, the extent to which these technologies are adopted in developing countries like Nigeria remains under-researched. This study investigates the specific challenges impeding the adoption of Construction 4.0 technologies in advancing CE practices within the Nigerian construction industry. While global literature highlights barriers such as cost, regulatory gaps, and resistance to change, this research uncovers localized obstacles including infrastructural deficiencies, poor internet reliability, dominance of the informal construction sector, and limited technical training opportunities. Using a qualitative research design, semi-structured interviews were conducted with industry professionals in Lagos State, Nigeria who were selected through purposive and snowball sampling to ensure relevance insights. Thematic analysis revealed that while most of the barriers identified mirror those already established in global literature such as high implementation costs, general lack of awareness, regulatory inadequacies, lack of standardization, skill shortages and implementation capacity, the study also uncovers additional, context-specific challenges that deepen understanding of these barriers in Nigeria’s setting. These include chronic infrastructural deficiencies such as unreliable internet and power supply, the dominance of an informal construction sector that resists standardization and technological uptake, and deep-seated cultural resistance to digital transformation. Furthermore, the study finds that even where awareness of technologies like BIM exists, practical adoption remains minimal due to the absence of policy support, technical training frameworks, and adequate incentives. For instance, several participants noted that Construction 4.0 tools are seen as “premium” and often incompatible with the cost and workflow structures of small- and medium-sized firms, especially those operating informally. This paper contributes to the literature by situating global adoption challenges within a localized Nigerian context, highlighting how familiar barriers are amplified by the institutional dynamics specific to developing countries, particularly Nigeria. The findings offer insights for policymakers, construction firms, and educators seeking to promote CE through construction 4.0. The findings also have practical implications for improving policy, workforce training, and infrastructure investment strategies tailored to Nigeria’s construction sector. By revealing how global challenges manifest in context-specific ways, the study supports the development of targeted interventions that bridge the gap between innovation and local feasibility.

Drone Equipment and Configuration for Detecting Theft in Telecommunication Infrastructure

C. O. Njoku, A. V. Gambo, Y. Bello — Wed, 04 Jun 2025 19:20:18 +0000

Drone aircrafts, also known as an Unmanned Aerial Vehicle (UAV) or Unmanned Aircraft Systems (UAS) has gained more importance in environmental monitoring as well as in telecommunication infrastructure theft response around the world. The study aims at identifying the type of sensors that can be mounted on drones for effective telecommunication infrastructure theft detection and prevention. The various parts of drone and configuration were tested. Sensors capable of detecting telecommunication infrastructures in the visible, infra-red, near infra-red, laser fluoro-sensor were all evaluated. The ability of the UAV to continuously and successfully patrol along or beside telecommunication infrastructure reveals that it takes about 5 minutes for the UAV to complete a programmed 150m autonomous patrol. During power test, result showed that for every 150m programmed patrol, 15 minutes of solar charging is required to restore the battery back to its initial voltage. It was concluded that a drone can properly be used for telecommunication infrastructure theft response operations. Appropriate sensors mounted on the drone can detect telecommunication infrastructure theft and can also access locations which are not readily accessible and other aerial patrol platform. The limitation of drones is the payload capability and its inability to operate well in windy weather since data collection with drone are faster, cheaper and easier during telecommunication infrastructure theft response operations. It is highly recommended to mount the suitable sensor capable of detecting telecommunication theft and vandalization even during nighttime operations.

Effect of Oil Palm Biomass Cellulosic Content on Molecular Structure of Biochar

A. A. Lawal, M. A. Hassan, B. Mohammed, Y. Shirai — Wed, 04 Jun 2025 19:33:35 +0000

Biochar is attractive mainly due to its diverse surface functionality bringing the possibility of multipurpose utilization. The purpose of this study was to evaluate the effect of biomass cellulosic content on evolution of molecular structures of biochar. Commercial cellulose, oil palm front, and palm kernel shell were pyrolyzed at 630 °C, and their biochar structures were analyzed using ultimate and proximate compositions, pH point of zero charge, FTIR and XRD. Evaluation of biochar nanotexture based on cellulosic content (100% for commercial cellulose, 39.5% for oil palm front and 20.5% for palm kernel shell) revealed that commercial cellulose decomposed rapidly into non-graphitizing large size crystallites (65 nm) with substantial defects within their graphene sheets. The thermal decomposition mechanism for cellulose and lignin differs: cellulose decomposed rapidly to form O heterocyclic rings before final phase transition to graphene sheets at a much lower temperature, whereas lignin decomposed slowly and begins forming graphene sheets after series of condensation reactions at higher temperatures. Amorphous chars derived from lignin were thermally stable, slowing down the rapid formation of crystallites in biochar from palm kernel shell. Conclusively, highly cellulosic biomass is highly thermally unstable and tends to decomposed into biochar with lower surface functionality compared to biochar derived from highly lignified biomass.

Response Surface Methodology and Artificial Neural Network Modeling and Optimization of Luffa Cylindrica Fibre Pyrolysis in a Fixed-Bed Pyrolizer

Wed, 04 Jun 2025 20:08:05 +0000

Bio-oil production from Luffa fibre, a plentiful agricultural byproduct, has attracted considerable interest as a sustainable and renewable energy source. In this study, response surface methodology (RSM) and artificial neural network (ANN) modelling were used to optimize operating conditions for bio-oil produced by pyrolysis from luffa cylindrica fibre. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) are used to model and improve operational parameters like temperature, particle size diameter, and inert gas flow rate. This is done to boost bio-oil production and quality. We develop a predictive model for bio-oil characteristics using ANN modelling, which effectively optimizes pyrolysis conditions. This study offers significant knowledge on the production and characteristics of bio-oil derived from luffa cylindrica fibres. By employing both models, we leveraged Response Surface Methodology (RSM) flexibility to provide statistical measures of individual models and their interaction impact on the process output while benefiting from Artificial Neural Networks (ANN) efficiency in processing data and acquiring complex patterns. It offers a method to improve the production process methodically. Comparing the prediction findings of the ANN with those of the RSM, it was shown that the former were superior. Different models have been trained using various transfer functions and varying numbers of neurons with 0.99797, 1.0, and 0.9989 R² values for the training, validation, and test stages, respectively. The proposed network had an overall R² factor of 0.99869. The results were deemed satisfactory based on the overall R² value being near 1.0. The optimization of operational parameters enhances the effective transformation of luffa cylindrica fibre into bio-oil, therefore encouraging the utilization of this sustainable resource for the generation of renewable energy. This strategy aligns with the increasing focus on decreasing the environmental consequences of conventional fossil fuels and promoting alternative and eco-friendly energy supplies.

Integrating Bands Algorithm Estimation on Water Turbidity Variation in Hartbeespoort Dam, South Africa

G. U. Fayomi, E. K. Onyari, G. O. Ode, O. I. Nkwonta — Wed, 04 Jun 2025 20:22:59 +0000

Water resources remain the most essential need for both human and ecosystems sustenance. Continuous check of both surface and ground water quality remains a welcome approach globally to secure water safety and fitness for different purposes. This study aims to examine the effect of turbidity concentration on water quality of Hartbeespoort dam using remote sensing and Arc GIS. Study objectives examined the present situation of the dam water, access complete one year (2023) data of the water quality variations as weather changes. Remote sensing and Arc GIS application was utilized for this study with the use of Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIR). NSMI mapping of turbidity showed high values range of 2.054 high concentration in the month of October, in February, 6.171 in June and 8.273 in October, 2023. NDTI (Normalized Difference Turbidity Index) mapping equally recorded high value of 2.194 in the month of October. The regression analysis utilizes Linear, Exponential and polynomial equation to examine seasonal variation of turbidity in Dam water. The result revealed a strong correlation such as (NDTI in Linear regression analysis records (????2 )= 0.54 while NSMI in linear regression records ( ????2 )= 0.35. The value derived from NDTI and NSMI algorithm confirmed the capacity of band combination indices in retrieving suspended sediment in form of turbidity using Landsat 8-9. These study findings also punctuate the effectiveness of Landsat 8-9 satellite imagery in evaluating turbidity concentration especially in situations where in-situ data is not available.