Arid Zone Journal of Engineering, Technology and Environment (AZOJETE)
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Assessment of Agro-Waste Potentials for the Generation of Electrical Energy in Northeast Nigeria
Get PDFIn this paper eight major Agricultural-wastes (agro-waste) (maize cob, groundnut shell, bean pod, wheat husk, rice husk, millet bran, sorghum bran and sugar cane bagasse) derived from widely grown crops in the northeast sub-region have been investigated for their energy resource potentials. The core research task is to estimate the aggregate crop residues from raw data collected from river basin authorities and ministries of agriculture and water resources as well as from structured surveys of major markets, large commercial farms and Agro-Allied Industries. This work relies on directed data analytics approach and has arrived at gross estimates of 6.89x104 metric tons per annum as agro-wastes available for electricity production; corresponding to approximately 13.5% share of the total agro-wastes of the northeast sub-region. Further, samples of all agro-wastes were characterized via proximate analysis which yielded their respective lower heating values to be within the range of 12.52MJ/kg to 16.66MJ/kg. From end-use viewpoint, the estimated agro-wastes of the study area can generate electrical energy of 55.8GWh/annum for geographically scattered rural communities
Modeling a Solar-Powered IoT Smart Home
No full textThe 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
Performance Evaluation of Yolo Object Detection Models for Automated License Plate Recognition
No full textThis 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
Drone Equipment and Configuration for Detecting Theft in Telecommunication Infrastructure
No full textDrone 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
Modeling of Rainfall-Runoff of the Yobe River Basin Using HEC-HMS
Get PDFThe 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
Crack and Spall Detection in Buildings using Yolov8 And Detectron2βBased Web Application
Get PDFCracks and spalls in building structures pose serious risks to safety and durability. Conventional methods of detecting these defects are manual, time-consuming, and error-prone. Hence, this study develops a web-based system for automated defect detection using deep learning models. Two object detection models (YOLOv8 and Detectron2), and a CNN model (Resnet18), were trained on 1798 annotated images which consisted of benchmark datasets (METU, VCC) and with locally acquired images. Classification and object detection were done on both datasets acquired. YOLOv8 achieved a weighted average of 99.0% precision, 99.0% recall, and 99.0% accuracy, while Resnet18 reached 98.8.0% precision, 98.8% recall, and 98.8% accuracy weighted average for mild crack, severe crack and spall. For the object detection, YOLOv8 (mask mAP50 of 93.0%) achieved superior segmentation accuracy than Detectron2 (mask mAP50 of 87.5%). Both models demonstrated strong performance in detecting spalls, mild and severe cracks (mAP > 0.73). The Detectron2 model was deployed in a web-based application to enable real-time crack and spall identification. These results confirm the feasibility of AI-assisted structural health monitoring and highlight pathways for improving crack detection through balanced datasets, synthetic augmentation, and higher-resolution training in both Nigerian and global contexts
Spatial Assessment of Aquifer Vulnerability Using Dar Zarrouck Parameters in Idunmwowina, Edo State, Nigeria
No full textRapid urbanization and unregulated sand mining in many semi-urban areas of Edo State have heightened concerns about aquifer vulnerability and groundwater contamination. This study evaluated the spatial variability of aquifer vulnerability in Idunmwowina, Ovia North-East Local Government Area, Edo State, using Dar Zarrouk parameters derived from vertical electrical sounding (VES). Five VES were conducted with the Schlumberger configuration at a maximum current electrode spacing of AB/2 = 200 m. Data interpretation was carried out using the partial curve matching technique in Surfer software, while kriging interpolation was applied for spatial mapping. The geoelectric curves identified include KHK (40%), HAA (20%), HAK (20%), and AAK (20 %), indicating predominantly semi-confined aquifers. The results of Dar Zarrouk parameters revealed hydraulic conductivity values ranging from 0.73β1.46 m/day, transmissivity between 2.72β7.67 mΒ²/day, transverse resistance from 26,621β221,682 β¦mΒ², and longitudinal conductance (S) values between 0.004β0.335 β¦β»ΒΉ (mean = 0.0752 β¦β»ΒΉ). Additionally, the Spatial distribution maps showed higher transmissivity and hydraulic conductivity in the southwestern part of the study area, while longitudinal conductance values were generally low, suggesting weak aquifer protective capacity. Therefore, the results indicate that the aquifer system is vulnerable to contamination from leachates generated by nearby dumpsites. It It highlights the need for effective land use management and sustainable waste disposal practices to safeguard groundwater resources in the area
Energy Storage: The Key to Reliable Renewable Energy Grids
No full textIntegrating 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
Rainfall and Streamflow Dynamics in the Borno Region of the Lake Chad Basin: A Hydrological Assessment
No full textThis 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 mΒ³/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
Investigation of Plantain Extract for Enhancing Copper Durability in Acid-Based Corrosive Environments
No full textThis 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 0.1β0.3 mL and temperatures of 30β50Β°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 40Β°C. similarly, adsorption experiments confirmed that Freundlich isotherm (RΒ² = 0.906 at 40Β°C) best described the process, indicating heterogeneous multilayer physisorption, also confirmed by Gibbs free energy values (ΞGads = -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, 40Β°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 50Β°C (IE% = 80.2%), reflecting thermal limitations