BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Bayero Journal of Engineering and Technology (BJET) is a publication of the Faculty of Engineering, Bayero University, Kano - Nigeria. BJET publishes original articles in English Language from all fields of Engineering and Technology. The journal receives articles for publication at all times, however publishes three issues per year (January, May and September). The journal uses a double-blind review system for the received manuscripts. Desk evaluation and plagiarism check are applied in BJET before continuing to the next review process to make sure all manuscripts submitted to BJET are original and not duplication from others. References writing tools were also applied in this BJET to make sure a correct citation index such as Mendeley. The article writing template is attached and can be downloaded on the BJET website.
Proceedings of the 5th Faculty of Engineering Conference 2026
The 5th Faculty of Engineering Conference of Bayero University, Kano provided a dynamic platform for researchers, policymakers, industry practitioners, and innovators to exchange ideas and present research addressing contemporary engineering challenges. Held from 9th to 10th February 2026, the conference was organized under the theme “Engineering Innovation and Economic Policies: Driving Sustainable Industrial Growth in Nigeria.” The conference brought together participants from academia, industry, and government institutions, fostering interdisciplinary dialogue on the role of engineering in supporting national economic transformation, technological advancement, and sustainable development. The technical sessions featured peer-reviewed papers covering diverse areas of engineering, including industrial development, energy systems, infrastructure innovation, agricultural mechanization, digital technologies, artificial intelligence, engineering entrepreneurship, and sustainable development solutions. This volume of proceedings contains selected peer-reviewed papers presented during the conference. The contributions highlight emerging research trends, practical engineering solutions, and policy-relevant insights aimed at strengthening Nigeria’s industrial capacity and technological competitiveness. The proceedings serve as an important knowledge resource for academics, practitioners, policymakers, and students interested in advancing engineering-driven development. The Faculty of Engineering, Bayero University, Kano, remains committed to promoting research dissemination, innovation, and collaboration among stakeholders. It is hoped that the ideas presented in this volume will stimulate further research, strengthen academia–industry–government partnerships, and contribute to sustainable industrial growth in Nigeria and beyond.
Copyright (c) 2026 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Evaluating the Effects of Road Bumps on Passenger Comfort in Ilorin Metropolis: A Case Study of Odota-Airport Road and Adewole-Oloje Road, Ilorin West
This study investigates the effects of road bumps on passenger comfort along two selected routes in Ilorin West, Nigeria: Odota-Airport Road and Adewole-Oloje Road. Field surveys, physical measurements of bump geometry, and in-vehicle acceleration data were used to determine vertical acceleration and root mean square (RMS) values for different vehicle seats and road segments. Passenger comfort was also assessed through structured questionnaires, and results were compared with ISO 2631 comfort thresholds. The findings reveal that larger and more frequent bumps, as well as higher vehicle speeds, are associated with increased vertical acceleration and discomfort, particularly for passengers in smaller vehicles. Survey responses indicated that road bumps contribute to body pain, fatigue, and reduced travel satisfaction, with the Adewole-Oloje Road showing a higher incidence of discomfort. The study recommends standardizing bump dimensions, implementing appropriate signage, and conducting regular maintenance to improve both safety and comfort. These results offer practical guidance for urban planners and transport authorities seeking to balance traffic calming and passenger well-being in Nigerian urban settings.
Ilorin West, Passenger comfort, Road bumps, Traffic calming, Vibration dose value.
Engr. Dr. Ibiwoye Emmanuel Olatunde IBIWOYE
A Methodological Comparison of Linear and Non-Linear Regression for Kinetic Analysis of Hydrogen Peroxide Decomposition by Catalase
TThis study presents a comprehensive kinetic analysis of hydrogen peroxide decomposition by bovine liver catalase, with particular emphasis on methodological evaluation of parameter estimation techniques. Kinetic analysis revealed that under the experimental conditions employed, the decomposition exhibited apparent second-order behavior (R²>0.993), with rate constants of 0.13 L·mmol⁻¹·min⁻¹ at 24°C and 0.064 L·mmol⁻¹·min⁻¹ at 6°C. The apparent activation energy was estimated as 28.5 kJ/mol from Arrhenius behavior. A critical comparison of regression methods revealed striking discrepancies: linear transformation approaches (Lineweaver-Burk, Eadie-Hofstee, and Hanes-Woolf methods) produced statistically problematic results, including physiologically implausible negative values for Michaelis-Menten kinetic parameters, Vm and Km. In contrast, non-linear regression, which minimizes the residual sum of squares (RSS), yielded robust, meaningful parameters (Vm = 659 and 25.6 mmol/L.min; Km = 962 and 69.9 mmol/L at 24°C and 6°C, respectively) and a significantly better fit to the experimental data. However, the analysis also highlights the necessity for cautious interpretation of regression-derived parameters due to inherent correlation between Vm and Km, experimental design limitations, and model simplification constraints. This work demonstrates that while non-linear regression is indispensable for accurate kinetic analysis, its outputs must be interpreted within appropriate statistical and experimental contexts to ensure biologically relevant conclusions.
A LoRa Based Distributed Environmental Sensing Network for a Digital One Health Surveillance System
This paper presents the design, development, and validation of a low-cost, energy-efficient Internet of Things (IoT) temperature monitoring node developed as a proof-of-concept for Digital One Health Surveillance Systems (DOHS). The system is designed to address microclimatic variability in resource-constrained environments, where real-time environmental data are critical for understanding and mitigating health risks at the human–animal–environment interface. The proposed architecture integrates low-power sensing, data logging, and wireless communication to enable continuous temperature monitoring while maintaining minimal energy consumption. Field evaluations conducted using a standalone IoT node demonstrate reliable data acquisition, stable operation, and cost-effective performance under real-world conditions. Although the current implementation focuses on a single-node deployment to validate system functionality and energy efficiency, the design is inherently scalable and intended to support future multi-node, networked deployments for distributed environmental surveillance. The results confirm the feasibility of the proposed node as a foundational component for scalable DOHS frameworks, particularly in low-resource settings where affordability, energy efficiency, and deployment flexibility are essential. Field testing over 14 days demonstrated ±0.6°C measurement accuracy, robust communication up to 820 m with <5% packet loss, and calculated battery autonomy of 32.9 days via ESP32 deep-sleep power management. Total component cost (₦65,000 ≈ $40–$50 USD) represents <13% of commercial alternatives. The system provides a scalable, replicable framework for distributed environmental sensing in academic campuses, agricultural zones, and wildlife–human interface areas, enabling high-resolution microclimate mapping to support One Health oriented research, smart campus development, and evidence-based public health decision-making. It demonstrates that low-cost, locally maintainable, solar-powered LoRa IoT nodes can deliver robust, long-range environmental monitoring in tropical, resource-limited contexts
Assessment of Physico-Chemical Water Quality Standard of Bayero University
Safe drinking water is essential for public health, yet water quality deterioration remains a concern in many developing regions. This study assessed the physico-chemical and selected heavy metal quality of drinking water within Bayero University Kano (BUK), Nigeria, and compared it with the Nigerian Standard for Drinking Water Quality (NSDWQ) and World Health Organisation (WHO) guidelines. Water samples were collected from boreholes, underground reservoirs, and overhead storage tanks across both the Old and New Campuses of the university. Standard analytical methods recommended by the American Public Health Association (APHA) were used to analyse the parameters. The results revealed variations in water quality along the distribution system. Most parameters, including turbidity, electrical conductivity, sulphate, chloride, calcium, magnesium, manganese, and dissolved oxygen, were within the permissible limits of WHO and NSDWQ. However, all samples showed acidic pH values ranging between 5.32 – 5.38 which is below the recommended range. The concentrations of nitrate (ranging from 56.04 to 434.31 mg/L) and ammonia (ranging from 1.40 to 2.94 mg/L) in all samples surpass the recommended guideline limits, indicating potential contamination from agricultural practices and other human-related sources. High nitrate content in drinking water can cause methemoglobinemia in infants. Additionally, isolated instances of elevated iron and potassium levels were detected. The study underscores the need for continuous monitoring, improved water treatment, and effective management strategies to ensure safe and sustainable drinking water supply within the BUK community.
Physico-chemical parameters; Groundwater; Nitrate contamination; Drinking water standard
Development of FLC-Based Antilock Braking System Wheel Slip Control for Half And Quarter Vehicle Models
An antilock braking system (ABS) is an essential mechanism that improves the braking system of a vehicle. Different methods were used to design a better controller in order to improve the system. Literature studies had shown that the major technique used in modelling the system is the quarter-vehicle model for uncomplicatedness in the controller design. A half-vehicle model was adapted and will have more dynamic parameters of the overall system as compared to the quarter-vehicle model. In this work a fuzzy logic controller (FLC) for each wheel is designed separately; this is because the frictional force for the front wheel is higher than that of the rear wheel. Using the same vehicle parameters for the half model, another FLC is designed for the quarter model. The performance of the two models was evaluated by comparing the stopping distance of the two models. Simulation results show that the FLC-controlled half-vehicle model performed better than the quarter-vehicle model in terms of stopping distance, which is the critical factor in ABS. Using the same vehicle parameters for both models, simulation results have shown that the percentage reduction in terms of stopping distance has improved by 16.3% for the FLC-controlled half-vehicle model as against that of the FLC-controlled quarter-vehicle model, as well as a 13.8% improvement with the proposed FLC as against the sliding mode controller with disturbance observer. Evaluation results show that the stopping distance of a vehicle with ABS is better with the half-vehicle model and FLC than with the commonly used quarter-vehicle model; this improves safety and can save more lives, as the vehicle has more tendency to stop as compared with the quarter-model design
Dynamics; Frictional; Antilock; Spontaneously; Quarter; Half
Assessing Hospital Air-Conditioning System Management at the National Hospital, Abuja in Relation to Total Quality Management Principles
Effective management of hospital air-conditioning systems is critical to patient comfort, staff productivity, as well as infection control in healthcare facilities. This study assessed air-conditioning management practices at h Total Quality Management (TQM) principles. A quantitative survey design was adopted, and data were collected from clinical staff, patients on admission, and maintenance personnel. A total of 284 valid responses were obtained, representing a response rate of 90.7%. Descriptive statistics and chi-square tests were employed for data analysis using IBM SPSS. The findings reveal substantial deficiencies in system reliability, maintenance responsiveness, and preventive servicing. Only 12.5% of respondents strongly agreed that air-conditioning units are consistently functional, while over 50% expressed dissatisfaction with temperature regulation and indoor air quality. Maintenance inefficiencies were evident, as 43.7% of respondents disagreed that faults were addressed promptly. Chi-square analysis indicated a statistically significant disparity between existing management practices and TQM-aligned benchmarks (p < 0.05), suggesting weak adherence to key TQM principles such as preventive maintenance, continuous improvement, and data-driven decision-making. Furthermore, 89.9% of respondents supported the adoption of IoT-based real-time monitoring systems as a proposed improvement strategy. The study concludes that current air-conditioning management practices exhibit limited conformity with TQM principles and highlights the need for structured, quality-driven, and technology-supported maintenance frameworks to improve system reliability and service delivery in public healthcare facilities.
Development of Eco-Friendly Polyester Matrix Composite Reinforced with Doum Palm Kernel Powder for Advanced Motorcycle Leg Shield Design
This study investigates the mechanical properties of polyester matrix composites reinforced with varying weight percentages of doum palm kernel powder (DPKP) aimed at motorcycle leg shield design. Four composite formulations with varying polyester-to-doum palm kernel powder ratios (90/10, 80/20, 70/30, and 60/40) were fabricated and subjected to mechanical and thermal testing to evaluate their suitability for structural applications. The mechanical properties were evaluated through tensile, compression, flexural, and impact tests, while the thermal performance was analyzed using dynamic mechanical analysis (DMA). The results indicated that the 90/10 composition exhibited the highest stiffness, strength, and energy dissipation capacity, making it the most suitable for applications requiring rigidity and impact resistance. In contrast, the 60/40 composition demonstrated increased flexibility and water absorption at the rate of 8.30% after four days, along with reduced mechanical strength (12.119 MPa) and thermal stability. DMA results further revealed that the 90/10 formulation retained its stiffness up to higher temperatures of 55.5°C indicating the onset of the material's transition from a glassy to a rubbery state. Overall, the work successfully demonstrated the potential of doum palm kernel powder as reinforcing filler in polyester composites for high-performance motorcycle leg shields due to its superior mechanical and thermal properties.
Doum palm kernel, composites, polyester matrix and Dynamic Mechanical Analysis
Assessment of an Improved Wiener II Path Loss Model for Signal Propagation on Concrete Walls
Path loss analysis of Wi-Fi (IEEE 802.11x) signal propagation plays significant role in the plan and function of wireless local area networks (WLAN) applications. Path loss models are crucial in the planning of wireless network as they facilitate survey and mapping of a site for RF installation via simulation; thus, avoiding tedious physical measurements and serve as a guide to a network designer in determining the best location for network devices. Among others, path loss depends upon signal frequency, distance, antenna height and other environmental (medium) characteristics. Frequency and distance are the most important parameters for path loss calculation. This paper develops a modified Wiener II path loss model suitable for assessing Wi-Fi signal propagation through concrete wall. It analyses the effect of distance and frequency on the received signal quality. Empirically, a computer system running the inSSIDer software (a Wi-Fi network scanner application) was used to measure the received signal strength from a Wi-Fi signal source (access point); the measured results were recorded at every 10 m interval between the source (Wi-Fi Access point) and the destination (a personal computer). For the theoretical results, Wiener II model was simulated in Matlab and the result was also recorded. A comparison of both the empirical and theoretical results revealed that the original Wiener II model consistently deviates from the measured results by an average of 1.11%. The Wiener II model was modified by tuning its parameters based on the medium characteristics. The improved Wiener II model estimates the measured path loss with an average deviation of only 0.38% from the measured results.
Copyright (c) 2025 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Artificial Neural Network-Based Model of Ajaokuta Steel Thermal Power Plant
This paper develops the identification of thermal power plant using artificial neural network (ANN) technique taking Ajaokuta steel power plant as a case study. The thermal power plant, built in 1987, has a full capacity of 60MW at a frequency of 50Hz. Data was collected from the Ajaokuta steel power plant for the input/output parameters of the main components for the identification process. This include governor valve position, superheated steam flow rate to the turbine, turbine blades torque as well as the frequency of the generated electrical power. Firstly, identification models for the governor, turbine and generator were separately developed. To simplify the process, each model is treated as a single input single output (SISO) system. Then, the thermal power plant was modelled as a whole taking frequency as the input and generated electrical power as the output. The obtained data is used to train a feedforward neural network with one hidden layer in Matlab environment. An exhaustive search routine was developed to control the choice of the architecture (training function and number of layers of neurons) for the feedforward ANN models during the identification process. This optimizes the developed ANN model for every component of the power plant. A correlation analysis on the inputs/output dependencies reveals 95% confidence. To validate the developed ANN models, dynamic linear models were estimated using ARMAX method. Similarly, the order of each ARMAX model was chosen to minimize the mean square error. Compared to the ARMAX models, the accuracy of the proposed ANN models outweigh that of their numerical counterparts by 6.32, 0, 99.82, and 3.09 percent for the governor, turbine, generator and the complete plant respectively. Thus, the identified ANN models provide better representation of the thermal power plant.
Abdulrahman Rufai YusufAbdulrahman Rufai
A Gantry Crane Control Scheme Using Hybrid Input Shaper and PID Controller
Gantry crane is a critical system used for point-to-point transportation of a payload in industries, ports, nuclear plants etc. Speed, accuracy and safety are of paramount importance in gantry cranes (GCs) operation. However, operating GCs as fast as possible generally results in payload sway coupled with inaccurate positioning of the load which degrades the speed, accuracy and safety. This work proposed a hybrid control system for sway suppression and precise trolley positioning in gantry crane systems. The hybrid control system combines Zero-vibration (ZV) and Zero-Vibration Derivative (ZVD) input shapers for sway suppression. Then a Proportional- Integral Derivative (PID) controller is designed for trolley position control. The effectiveness of the hybrid controller was studied and investi-gated by simulating the proposed hybrid ZV-ZVD shaper for sway suppression and the PID controller for precise position control using MATLAB/Simulink. Experimental results show that hybrid ZV-ZVD was able to suppress the pay load sway to 99.69% and the combination of hybrid ZV-ZVD and PID controllers offer precise trolley position control.
Copyright (c) 2021 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Assessment of the Feasibility and Potential Impact of IoT-Based Predictive Maintenance for HVAC Systems in Public Hospitals in Nigeria
Effective heating, ventilation, and air-conditioning (HVAC) performance is critical to thermal comfort, operational efficiency and better performance of installed equipment in healthcare facilities. However, air-conditioning systems in many Nigerian public hospitals are managed using reactive maintenance practices, resulting in frequent breakdowns, energy waste, and service disruption. This study presents an engineering feasibility assessment of an Internet of Things (IoT)-based predictive maintenance framework for hospital HVAC systems using the National Hospital, Abuja, as a case study. A low-cost distributed monitoring architecture incorporating temperature, current, voltage, and humidity sensors interfaced with an ESP32 edge gateway was designed to enable real-time equipment condition monitoring and automated fault detection. Analytical models were developed to estimate system power consumption, sensor performance, and preventive maintenance thresholds for early fault identification. A cost–benefit analysis indicates that predictive monitoring could reduce HVAC downtime by approximately 25–35% and decrease energy consumption by 20–30%, with a projected payback period of less than two years. Stakeholder surveys (n = 284) further validated operational deficiencies and confirmed institutional readiness for technology-assisted maintenance. The results demonstrate that IoT-enabled predictive maintenance is technically feasible and economically viable for improving HVAC reliability and service delivery in resource-constrained public hospitals. The proposed framework provides a scalable pathway for modernizing healthcare facility management in Nigeria and similar contexts.
Hydraulic Performance and Optimal Siting of Service Reservoirs in the Ilorin Water Distribution System
This paper presents the evaluation of the hydraulic performance of Ilorin metropolitan water distribution system with the aim to determine the optimal locations of service reservoirs and nodal pressures for improve water supply. In an attempt to improve the water services in Ilorin, the Kwara State Government proposed the construction of additional service reservoirs at different locations within the metropolis. The GIS was used to provide georeferenced locations of pipes, nodes, reservoirs, while the Hydraulic conditions were performed with EPANET 2.2 software in order to determine the output pressure at demand nodes for various scenarios under consideration. Ten cases were considered using the location of the service reservoirs and the quantity of water distribution to the study area. The pressure distribution at the nodes were determined for the ten cases. The minimum and the maximum nodal pressures obtained were 10 m and 84 m respectively based on case IV. These are favorably comparable with the recommended values for satisfactory performance. Case IV has the highest percentage number of nodes pressure that fall between 25 m and 84 m. About 20% of the nodes have pressure below 25 m and none above 84 m. This will rule out the possibility of pipe bursting as a result of excessive pressure in the pipeline. Hence Case IV has the optimal nodal pressure distribution and can be concluded that the study provide a suitable model for appropriate location of the service reservoirs based on pressure requirement at demand nodes.
An In-Depth Comparison of Plain CNN, Fine-Tune VGG16, and Vision Transformer Models in Object Detection
Object detection is one of the most essential yet challenging tasks in computer vision, playing a crucial role in applications such as tumour detection, quality control and inventory management, security and surveillance, autonomous systems and robotics, crop monitoring and disease detection in agriculture, defect detection in the construction industry and items detection for home robots. With the rise of deep learning techniques, significant progress has been made through models like convolutional neural networks (CNNs) and, more recently, vision transformers (ViTs). In this research, we investigate how three object detection architectures- plain CNN, transfer learning, and ViTs- perform in detecting multiple household objects under controlled conditions. The findings show that ViTs consistently outperformed plain CNN and transfer learning in key performance areas. While the plain CNN achieved a peak IoU of 97.10%, VGG16 reached 98.01%, and the ViTs model attained the highest IoU of 98. 42%, with a smoother and more stable learning curve. Additionally, the mean square error (MSE) was lower for ViTs at 5. 5.197%, compared to the plain CNN and the fine-tune VGG 16, which settled at 11. 40% and 10. 21%, indicating better prediction precision. Loss metrics for ViTs were also consistently lower, decreasing to 5. 88% compared to plain CNN and VGG16, which settled at 11.23% and 10. 01%, demonstrating more efficient learning with less fluctuation during training. However, this came at the expense of increased training time. The VGG16 required approximately 1, 326 seconds per epoch, compared to about 147 and 150 seconds per epoch for plain CNN and the ViTs. By comparing the three models in terms of IoU, MSE, loss behaviour, and execution time, this research highlights the growing strength of transformer-based models in object detection tasks. These results not only reinforce the potential of ViTs but also offer valuable insights for researchers and practitioners aiming to balance performance with computational costs in real-world detection tasks.
Key words: Computer Vision (CV); Deep Learning (DL), CNNs (Convolutional Neural Networks); You Only Look Once (YOLO); Vision Transformers (ViTs); VGG (Visual Geometry Group); MSE (Mean Squared Error)
Quantification and Characterization of Medical Solid Waste at Abubakar Tafawa Balewa University Teaching Hospital, Bauchi, Nigeria
This study aims to quantify and characterize the medical solid waste generated at Abubakar Tafawa Balewa University Teaching Hospital (ATBUTH), Bauchi, and to recommend possible integrated solid waste management strategies for sustainable disposal. The study lasted one month, during which questionnaires, interviews, and surveys were conducted to gather the necessary data. The ATBUTH Bauchi produced an average of 578.1 kg of solid waste weekly during the study period. The Maternity Ward generated the highest weekly volume at 137 kg. Conversely, the Administrative Block recorded the lowest waste output at 40.5 kg. Based on the mean waste composition, food waste, and medical/infectious waste being the largest portions at 26.4% and 21.7%, respectively, while dirt, ashes, and bricks made up the lowest at 4.3%. The mean per capita waste generation was approximately 0.42 kg/bed/day. It was observed that 35.6% of the total waste was recyclable, and 48.4% was biomass. When properly harnessed, this waste has the potential to reduce environmental pollution, create employment, and generate wealth for ATBUTH and its surrounding communities.
Solid Waste Management, Hospital, Quantification, Characterization.
Assessing the Enhancement of the Mechanical Properties of Aluminium 7075 using Gula Clay at Constant 2wt% Soot for Military Application
This study investigates the enhancement of mechanical properties of Aluminium 7075 alloy by incorporating Gula clay and soot as reinforcements for ballistic application. Aluminium 7075, widely used in aerospace, defense, and automotive industries, is known for its excellent strength-to-weight ratio but requires improved impact resistance and tensile properties for advanced ballistic applications. The research involves systematically varying the composition of Gula clay (1 - 5wt.%) at constant amount of 2wt.% soot to produce six distinct samples through liquid metallurgy and heat treatment processes. Microstructural analysis, Mechanical tests, including Vickers micro-hardness, tensile strength, impact resistance, total elongation, and wear resistance, were conducted to evaluate the composite properties. Based on the obtained optical micrographs, dendritic structure, and Zn – Al rich matrix of unreinforced Al7075 was vividly seen, the reinforced Al7075 consist of both primary matrix of Zn – rich phase, Al – rich secondary phase with uniform distribution reinforcements in the Al7076 alloy. It was observed that as the volume of Gula clay was increased, the dendritic structure of Al7075 was covered with the reinforcement. The mechanical tests results revealed that the addition of Gula clay in the range of 1 – 5 wt.% and at 2wt.% soot enhances tensile strength from 186 MPa (aluminium 7075) to 269 MPa (44.6% increment), while impact resistance improves from 17.6J to 34J (48% improvement), the wear resistance of Al7075 was enhanced by 81%. But there was a gradual marginal decrease in the ductility of Al7075 from 3.4 – 10.9%. The improved properties demonstrate the potential of this composite material to meet international standards for ballistic applications. These findings fill a significant gap in lightweight, cost-effective ballistic materials, providing an alternative to conventional alloys. This research contributes to the development of advanced materials for military applications while promoting the use of sustainable reinforcements
Al 7075, Gula Clay, Soot, Ballistic, enhancement
REMOVAL OF CADMIUM FROM SURFACE WATER OBTAINED AT THE OIL EXPLORATION SITE OF NIGER-DELTA REGION, NIGERIA: OPTIMIZATION OF THE BATCH-SORPTION PROCESS
The aim of the study was to determine the efficiency of cadmium contaminant removal from surface water situated near oil exploration sites in Niger-Delta Region. And to optimize the conditions, that is parametric factors for batch-sorption process of cadmium removal by studying as a function of the contact time, temperature, dosage (sorbent loading) and agitation speed using already synthesized bimetallic supported multi-walled carbon nanotube sorbents. The batch-sorption process was fully optimized in a system using 24 factorial model. The optimum values of the parametric factors were found to be contact time 59.729 min, temperature 30.989 oC, dosage 0.112 g and agitation 594.988 rpm, respectively. The removal efficiency, in this case, that is the maximum value of 99.071 % was obtained by the empirical model having desirability of 1.000 (and 98.96 % was experimentally verified). Therefore, this study showed the validity of the empirical model and sorption-capability of carbon nanotubes to effectively remove cadmium ions from the polluted surface water obtained at the oil exploration sites in the Niger-Delta region of Nigeria.
Batch-sorption Optimzation; Surface water; Cadmium Removal; Niger-Delta, Oil exploration, MWCNT
ESTIMATION OF DC SERVOMOTOR PARAMETERS USINGGREY WOLF, WHALE AND GENETIC ALGORITHM OPTIMISATION TECHNIQUES
This paper investigates the use of time-domain response with recent meta-heuristic optimisation techniques such as; Grey wolf optimisation, Whale optimisation algorithm, and Genetic algorithm for the estimation of the parameters of a servomechanism with insufficient datasheet information. First, to estimate the servomechanism’s closed-loop position second-order transfer function, the known servo gear mechanism and unknown speed first-order transfer functions were used. In the experimental investigation, the average time constant of the Servomechanism was found to be 162mS for both forward and reverse rotations. Thereafter, the meta-heuristic algorithms were used in MATLAB to simulate the identified position closed-loop velocity feedback transfer function to obtain the Servomechanism’s electromechanical parameters. The simulation and experimental response of the servomechanism were in excellent agreement, with the Genetic and Whale optimisation algorithms having the best and worst root mean squared error fitness scores of 0.00706 and 1.90374 respectively.
Systems Identification, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization, Servomechanism
Epemu Ayebatonye Epemu Ayebatonye Marttyns
CHARACTERIZATION OF RISE HUSK AS REINFORCEMENT IN COMPOSITE MATERIALS
Rice husk (RH) as a silica source was studied, resulting from the husk removed in the lab (RHA) and that collected from commercial rice milling area (RHB). Rice husk consists of inorganic, combustible matter in the rice that has been fused into an amorphous structure. A composting treatment was employed in an effort to improve the processability of rice husks. Changes in the chemical composition and physical structurewere analysed. Microscopic techniques, such as X-ray fluorescence (XRF), X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to observe the surface and internal structure of the RHA and RHB. The results among other things revealed that RHA and RHB both did not contain SiO2, as they are not in ash form. Microscopic examination showed that it amorphous and has a porous cellular structure and consists of irregular-shaped particles. However, scanning electron microscopy images showed that the epidermis became rugged and lumpy because the composition of rice husks (cellulose, hemicellulose, lignin, and pectin) was partially decomposed due to controlled burning conditions, an effect confirmed by the chemical composition. All results indicated that the rice husk prepared and treated in the lab has better physical and chemical properties that randomly selected at the rice processing area, which are beneficial for its utilization in engineering application.
Muslims are obliged to perform ablution (Wudhu)before prayer or other deeds. The ablution stations usually employ the use of manually operated water taps, which are not capable of controlling the water flow and leads to too much of water wastage in the process. This research developed a system called Automatic Multiple Users’ Ablution System to tackle the problem through automating the process. The system uses ultrasonic sensors to detect human presence and an Arduino mega-2560 microcontroller as the CPU. It sends the “on” and “off” instructions to the relay to activate the DC mini pump to allow water flow. This allows the system to work only when needed and users are not required to touch the valve in order to activate the water flow. The system is capable of detecting the presence of human in the range of 6cm maximum below the tap. The use of an automatic ablution system will significantly reduce unnecessary wastage of good water compared to manual tap, due to the fact about 2400 cm3 of good water can be saved by each tap (i.e. 11.76% of the water wasted using the manual tap), which can be generalized to all the eight taps, that is approximately 19200 cm3 amount of water is saved using the automatic ablution system compared to the manually operated tap.
Prayer; Control water usage; Multiple user; Automatic taps; Manually operation; Arduino mega-2560 Microcontroller; Ultrasonic sensor; DC mini pump.
IMPACT OF LOAD TORQUE VARIATIONS ON THREE PHASE INDUCTION MOTOR TO ENHANCE ITS PERFORMANCE
This paper presents the impact of load torque variations on the three phase induction motor for improved output performance. The objective was realized with the aid of Matlab/Simulink software. The induction motor used in this work are of specifications 3-phase, 4.5 KW, 50 Hz, 2 poles, 210 V, load torque (20 – 60) Nm. The transient and steady state responses of the motors were obtained, which revealed that that increase in load torque leads to decrease in motor speed, increase in load torque resulted to corresponding rise in stator resistanc. Similarly, increase in load torque leads to an increase in rotor resistance and corresponding increase in current.
FABRICATION OF CARBON NANOTUBE-REINFORCED ALUMINIUM MATRIXCOMPOSITE USING POWDER METALLURGY
Carbon nanotube reinforced Aluminium (CNT-Al) nanocomposites with four different concentrations of 1, 1.5, 2 and 2.5 wt% CNT were successfully fabricated using powder metallurgy route. Ball milling parameters such as preliminary mixing of CNT and Al in a tube via manual hand shaking and process control agent (PCA) were considered for the homogeneous and uniform dispersion of CNT in aluminium matrix with lower milling time. The characterization of CNT-Al mixed powder and CNT-Al nanocomposite were performed using SEM, FESEM, EDX and XRD analyzer. Density was measured using a digital densitometer and compared with the theoretical relative density. It was found that, 1 wt% CNT has the highest percentage of the relative density of 8.40%, Whilst 1.5 and 2 wt% showed almost same value for the relative density (≈77%), however, 2.5 wt% CNT in nanocomposite showed a slightly lower relative density of 74% indicating more lighter than 1.5 and 2 wt% CNT composition.The morphology of the developed CNT-Al nanocomposite showed uniform and homogeneous dispersion of CNT in the Al matrix. And 1.5 wt% CNT was assumed to have better and uniform CNT dispersion and it was achieved within short time of 250 and 300 rpm milling speed. Uniform and homogeneously dispersed CNT has a significant effect in reducing the weight of Al which lead to the achievement of the light weight CNT-Al nanocomposite.
BIOGAS PRODUCTION FROM CHICKEN MANURE: CHARACTERIZATION AND KINETIC MODELS
Characterizing chicken manure (CM) before anaerobic digestion (AD) and the knowledge of the kinetics of the process resulting in minimum and maximum biogas product is important. CM is a hazardous animal residue and kinetics is rarely studied without co-digesting with other feedstocks. Therefore, the objectives is to use POLYMATH 6.10 Educational Release and the Origin 2018 softwares to determine kinetic parameters in Modified Gompertz, Transfert, Cone and Logistic models used in biogas kinetic study and to propose the best model based on the characteristic of the CM used and the regression fitting. This was achieved by using regression parameters estimated by the two softwares using the Levenberg-Marquadt nonlinear technique. Findings shows that Logistic gave the best prediction, followed by Cone, Modified Gompertz, and Transfert model, in that order; though the above models puts biogas potential of CM at approximately 1m3 from 1:1 water-to-CM digestion with 63% methane, 29% carbondioxide, 5.8% hydrogen, 1.4% hydrogen sulphide, 0.6% ammonia, 0.1% nitrogen and 0.1% moisture.
Chicken manure; Biogas potential; Biogas Kinetic Model; Characterization; Bioreactor
Copyright (c) 2022 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
MODELING AND PARAMETER IDENTIFICATION OF A TWO-WHEELED INVERTED PENDULUM ROBOT
This paper presents a dynamic model and parameter identification for a two-wheeled inverted pendulum (TWIP) robot. The work is aimed at obtaining an accurate mathematical model of the TWIP and obtaining its parameters. The mathematical model is developed using the Euler-Lagrange method. Then, the parameters of the system are identified using measurements and computations. The nonlinearity and instability of the TWIP is evaluated through simulations of the model in SIMULINK. The simulation results show that the developed model represents the actual system in terms of nonlinearity and instability. Thus, the model can be used to develop a controller for the nonlinear plant.
Two-wheeled Inverted Pendulum (TWIP) robot; Lagrange modeling technique; parameter identification; gener-alized coordinates, tilt angle
DESIGN AND IMPLEMENTATION OF LONG-RANGE ENVIRONMENT MONITORING SYSTEM
Long-range communication and low power consumption are essential characteristics for IoT-enabled devices. The field of machine-to-machine (M2M) communication within the Internet of Things (IoT) is examined in the Design and Implementation of Low Power Wide Area Network for Internet of Things. Determining which LPWAN technology is best for a given use case has become cumbersome, and the majority of monitoring systems use short-range and higher power consuming networks. Through this work, the research aims to leverage LPWAN technology, specifically LoRa network, to achieve continuous real-time data transmission to a monitoring station. To achieve this Arduino microcontroller and SX1278 which serves as the LoRa node was integrated to sensors for real-time monitoring. Data acquired from the sensors was delivered to the LoRa gateway. The LoRa gateway was registered on The Things Network (TTN). An application was developed and integrated to the TTN to visualize the data. The received signal strength Indicator (RSSI), Signal to Noise Ratio (SNR) and Packet Delivery Ratio(PDR) were used to analyze the performance of various spread factors of the LoRa transmission. The finding from this research shows that LoRa network is suitable for deployment in monitoring temperature, humidity, Nitric Oxide(NO) gas level with range of 0.5to 25ppm and flame in Nigeria amongst other LPWAN technologies. This system successfully detects four environmental parameters: temperature, humidity, gas level and flame level, transmits the data to a base station over distances ranging from 10 to 500 meters, and securely stores the data in the ThinkSpeak IoT cloud platform. The data are displayed on LCD and web via ThingSpeak.
Sensors, Gateway, Spreading Factor, LoRa, IoT, ThingSpeak, Battery lifetime
REVIEW ON THE PERFORMANCE ENHANCEMENT METHODS IN THERMO-ELECTRIC GENERATOR (TEG) BASED ENERGY HARVESTERS FROM ASPHALT PAVEMENTS
Asphalt pavements receive a lot of solar radiation from the sun, which is converted into thermal energy in the pavement, leading to the heating and softening of the pavement material, thus affecting its longevity. Also, in urban areas where these pavements form up to 40% of the land area, it leads to a phenomenon called the urban heat island (UHI). Thermoelectric generators (TEGs) can convert the thermal energy in the pavement into useful electrical energy for other applications, thus preventing these effects and also being a source of energy. The use of TEGs has the potential of solving these problems, and researchers have been working on developing TEGs for energy harvesting from asphalt pavements. The reported efficiencies of these systems are very low, and there is a need for improving the efficiency and power density of these devices. Thus, this paper firstly provides the details of the technology for energy harvesting from pavements and also provides an up-to-date review of the various strategies that are employed in improving the performance of the TEGs in asphalt pavements. These strategies include improving the thermoelectric properties of the TEG module or material; enhancing the thermal conductivity within the asphalt pavement; TEG system design and optimization; and increasing the temperature difference between the hot and cold sides by employing a well-designed heat sink. All these performance enhancement methods were found to improve the performance of the TEG system. The reported efficiencies of TEG systems embedded in asphalt pavements are still below 10%, and thus there is a need to improve the efficiencies. Additionally, most of these studies are either numerical or tested in laboratory prototypes.
Asphalt Pavement: Heat Sink: Urban Heat Island: Energy Harvesting: Seebeck Effect
Mubarak Danladi Muhammad Mubarak Danladi Muhammad
PERFORMANCE EVALUATION OF FPGA ACCELERATOR FOR VISION BASED FIRE DETECTION SYSTEM
Fire disasters are endangering human lives and pose a great threat to economic infrastructure, and the environment around the world. As a result, there is need for robust early detection of fire outbreaks to minimize loss of human lives, protect properties and the ecosystem from great damages. Conventional fire detection systems are based on electronic sensors. As a result, they suffer from the problems of transport delays, conduction delays, limited detection range, high false alarm, and not suitable for outdoor applications. Image processing and computer vision techniques for fire detection have been proposed in recent years to address the limitations of such sensor-based systems due to technological advances in electronics and computer science. However, most of these computer vision-based fire detection solutions are implemented in software platforms and as result, they have shortcomings of inefficiency, high hardware requirements and high cost. In this research study, a vision fire detection accelerator based on Field Programmable Gate Array (FPGA) was developed. MATLAB R2021a software was used for decoding the image dataset into pixel stream data. The design was captured in very high-speed integrated circuit HDL (VHDL). The design was synthesized with Xilinx Vivado 2021 design suite and simulated with Xilinx ISIMI. It has been shown that the design has achieved better resource utilization and power consumption compared to a similar work (15% Look-up Table (LUT), 1% Digital Signal Processor (DSP), 24% Input-Output (IO), 92.062W Dynamic and 1.029W Device Static). The hardware accelerator which was developed as an Intellectual Property (IP) core can also be employed to speed up image processing and computer vision algorithms in embedded vision, smart camera and video analytics applications.
Fire detection; FPGA; Performance Analysis; Image Processing; Hardware Accelerator.
Copyright (c) 2024 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Performance Evaluation of a Python-Based Model for Computing Normal and Critical Depths in Open Channel Flows
This paper presents the performance evaluation of a python-based model, a computer program developed for efficiently computing normal and critical depths in trapezoidal, circular, rectangular, and triangular open channel cross-sections. The model utilizes improved explicit equations to provide fast and highly accurate solutions, addressing the limitations of existing methods that are restricted to certain cross-sections or limited applicability conditions. The performance of the model was evaluated in terms of usability, stability, and accuracy. Accuracy was assessed by comparing the model's results with solutions from trial-and-error for a range of channel geometries in terms of maximum relative error and coefficient of determination. Usability was evaluated based on the model's user-friendly interface and ease of use. Stability was assessed by examining the model's ability to provide consistent results across different input conditions. The results demonstrate the model's high precision, with the maximum relative error of 1-2% and coefficient of determination (R2) exceeding 0.99 for both normal and critical depth computations across the cross-sections. The model proved to be stable, giving solutions even for extreme channel geometries. The use of Python programming language ensures a user-friendly interface, allowing hydraulic designers and engineers to efficiently determine critical and normal depths without the need for manual calculations or complex software. Overall, the model provides a practical and versatile tool for open channel flow analysis, contributing to improved hydraulic design and management practices. The model's accuracy, efficiency, and broad applicability make it a valuable resource for engineers and researchers working in the field of water resources and hydraulic engineering.
AN IMPROVED CHANNEL RANKING ALGORITHM FOR SPECTRUM HANDOFF IN COGNITIVE RADIO AD-HOC NETWORK.
The rapid increase in wireless communication technology in the past decade has led to scarcity of spectrum. Thus the world is moving away from fixed Spectrum Access (FSA) techniques to Dynamic Spectrum Access (DSA) techniques. Cognitive Radio (CR) is one of the spectrum access technique that makes use of DSA by allowing Secondary Users (SUs) to make use of the Primary Users (PUs) channels opportunistically. The SUs have to vacate the PUs channels when the PUs arrive on their channels in order to avoid interference with the PUs transmission. The process whereby the SUs switch their communication to another vacant channels when the PUs arrives on their channels is known as spectrum handoff. Finding other vacant channels for the interrupted SUs to continue their transmission is a challenging task. Several researchers have used different technique to select the best channel by considering channel occupancy as a criterion, but these technique still suffer some challenges such as high number of handoff, delay and low throughput. Therefore this paper presents an Improved Channel Ranking Algorithm (ICRA) that rank channel based on channel occupancy and channel signal quality. The channel with the highest rank is selected as the next target channel. Simulation was carried out using Network Simulator (NS) and the results obtained were plotted using MATLAB. The results showed that the ICRA reduced the number of handoff by 14% and 25% compared to Novel Proactive Handoff Scheme (NPHS) and IEEE 802.11 scheme, it also reduced the packet delay by 12% and 34% when compared to the NPHS and IEEE 802.11 scheme. Furthermore, the ICRA increased the throughput by 8% and 97% when compared to the NPHS and the IEEE 802.11 scheme, it also improved the packet delivery ratio by 15% and 96% when compared to the NPHS and IEEE 802.11 scheme
Cognitive Radio, Channel ranking, Channel occupancy, Channel signal quality, Primary users.
PROGRAMME DEVELOPMENT AND SIMULATION STUDIES OF A SINGLE SLOPE SOLAR STILL
This study develops a computer programme using MATLAB® software for simulating a single slope solar still, based on published models for the solar still and its major inputs variable, the ambient temperature and the solar irradiance. Then the solar still is digitally studied for the effects of some variables on its performance. The effects of variables such as depth of water in the basin slope of glass cover, insulation thickness, solar radiation and wind velocity on factors such as the distillate yield, water temperature and efficiency of the still are investigated using derived models. The simulated trends show that the distillate yield, water temperature and efficiency increase with increase in insulation thickness, wind velocity and solar radiation. But decreasing water depth increases the above-mentioned parameters. Changes in glass cover slope have minimal effect on the still efficiency.
Solar still; Solar still model; solar irradiance model; ambient temperature model; heat transfer coefficients