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.
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
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.
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.
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
AUTOMATIC GENERATION CONTROL USING A FRACTIONAL ORDER CASCADE CONTROLLER
The frequency stability of power systems depends on the active power balance between the generation and demand. The varying nature of the demand upsets this balance which consequently results in the deviation of the frequency of the system from its nominal value. In order to stabilize the frequency, the generation is continuously adjusted with the help of a controller to match the load at all times. This control process is called automatic generation control. This study proposes a new structure of fractional order cascade controller, namely fractional order proportional integral – fractional order tilt integral derivative (FOPI-FOTID) controller for automatic generation control (AGC) of a four-area deregulated power system. The parameters of the controller were optimized using the Grasshopper Optimization Algorithm (GOA). The superiority of the proposed controller was evaluated by comparing its performance against a non-cascade FOTID controller and a cascade tilt integral – tilt integral derivative (TI-TID) controller, under a step load perturbation in the power system. The recorded improvements over the cascade TI-TID controller are; 47%, 18.7%, 30.7% and 23.04% for the settling time, and 56.9%, 70.9%, 62.6% and 56.86% for the undershoot of the frequency deviations of area-1, area-2, area-3 and area-4 respectively; 32.86%, 3.13% and 23.0% for the overshoot of the frequency deviations of area-2, area-3 and area-4 respectively.
Automatic generation control; Fractional order calculus; Cascade control; Power system
Copyright (c) 2023 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
USING SMART ANTENNA FOR INTERFERENCE RESISTANT INDUSTRIAL WIRELESS SENSOR NETWORKS: A REVIEW
Wireless sensor networks deployed in harsh and time-varying radio environments, such as those seen in industrial settings, are susceptible to radio frequency interferences that are not present in conventional wireless sensor networks used for office or home automation. Noise and other wireless interference affect network connectivity and reduce communication channel quality. Furthermore, reliability and latency, which are critical components of industrial communication, suffer. It is difficult to build wireless networks in industrial environments that can coexist with competing wireless devices in an increasingly congested frequency spectrum and are immune to signal interference. To address these difficulties, we must consider the benefits that approaches that have been effective in other application areas can bring to industrial communication. Smart antenna approaches present a viable solution for increasing the interference resistance of industrial wireless sensor networks. Integration of smart antennas into the physical layers of Industrial Wireless Sensor Network (IWSNs) can enable effective signal transmission and reception by devices, which is superior to the omnidirectional antennas now in use. This would reduce interference and boost IWSNs wireless reliability.
Industrial wireless sensor networks, Interference, Interference mitigation techniques, Smart antenna, Smart antenna prototype
A Review of Islanding Detection in Microgrids
The increasing integration of Distributed Energy Resources (DER) such as solar photovoltaics, wind turbines, and energy storage systems into modern power systems has led to the rapid evolution of microgrids. Microgrids offer numerous benefits, including improved energy resilience, reduced transmission losses, and the facilitation of renewable energy adoption. One of the critical challenges associated with microgrid operation is the phenomenon of islanding—a condition in which a microgrid or a portion of the power system continues to operate in isolation from the main utility grid after a disturbance, such as a fault or maintenance-induced disconnection. This review paper provides a comprehensive examination of existing islanding detection methods, broadly categorizing them into passive, active, hybrid, and communication-based approaches. We present these methods in terms of detection speed, accuracy, Non-Detection Zones (NDZ), impact on power quality, and compatibility with various DER technologies. By consolidating and evaluating the current landscape of islanding detection strategies, this review aims to guide researchers and system designers in selecting or developing appropriate detection mechanisms for reliable microgrid operation in both grid-connected and islanded modes.
The Role of Explainable AI in Flood Modelling and Risk Assessment in the Era of Climate Change: A Systematic Review
Flood risk is intensifying under climate change, making accurate and transparent prediction tools a global priority. While artificial intelligence (AI), machine learning (ML) and deep learning (DL) have shown strong performance in flood forecasting and susceptibility mapping. Hence, their black-box nature limits stakeholder trust and operational use. This review systematically examines the emerging role of explainable AI (XAI) in flood analysis under changing climate conditions. A structured search was conducted across Scopus, Web of Science, IEEE, Springer, and ScienceDirect, followed by PRISMA screening. Sixty-four eligible studies were analysed and grouped into four thematic domains: (i) flood forecasting, (ii) hazard and susceptibility mapping, (iii) integration with climate projections, and (iv) decision support systems. Across these domains, SHAP, LIME and attention mechanisms emerged as the most widely applied XAI techniques, improving model interpretability and stakeholder engagement. However, some key gaps persist, which includes weak operational adoption, limited integration with climate change scenarios, data scarcity in developing regions and a lack of user-oriented visualisation tools. This review concludes that XAI provides a promising pathway toward more trustworthy and actionable flood risk assessment but calls for standardised frameworks, stronger linkage with climate projections, and policy-friendly outputs to bridge research and practice.
Feasibility and Performance Characterisation of a Dual-Fuel Engine Automobile Using Premium Motor Spirit (PMS) and Compressed Natural Gas (CNG)
Abstract The increasing environmental and energy challenges associated with Premium Motor Spirit (PMS) in spark-ignition engines necessitate the exploration of alternative and cleaner fuels. This study investigates the feasibility and performance of PMS–Compressed Natural Gas (CNG) dual-fuel operation using a validated Computational Fluid Dynamics (CFD) framework in ANSYS Fluent. A high-fidelity 30° sector model of a single-cylinder engine was developed, incorporating PMS direct injection and CNG port injection, with simulations conducted across substitution ratios of 0–40%. Mesh independence and time-step sensitivity analyses were performed to ensure numerical reliability, and validation against experimental benchmarks confirmed deviations within ±5%. Results show that moderate substitution (20–30% CNG) achieves the most effective balance, maintaining brake thermal efficiency at ~38.8% and limiting power losses to less than 6%, while reducing CO₂ and NOₓ emissions by 22% and 26%, respectively. These findings demonstrate the potential of PMS–CNG dual-fuel technology as a cost-effective transition pathway toward sustainable transport, particularly in regions such as Nigeria where natural gas infrastructure is expanding. The study also provides systematic CFD-based optimization and detailed insights into in-cylinder flow dynamics, offering a valuable complement to experimental research.
Dual-fuel engine automobile, Premium Motor Spirit (PMS), Compressed Natural Gas (CNG), ANSYS Fluent, CFD, combustion efficiency, emissions, k-ε turbulence model, Eddy dissipation concept
A Review on Telecommunications: Evolution, Current Trends, and Future Prospects - Opportunities and Challenges
The field of telecommunications has undergone a transformative journey from primitive methods of communication to modern, high-speed, and intelligent networks. This paper reviews the historical evolution of telecommunications, highlighting the advancements across various cellular generations, from 1G to 5G, and exploring the prospects of 6G. The study delves into current trends such as the integration of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and cloud computing, alongside the pivotal role of fiber optics in meeting the growing demand for data. It examines the opportunities these technologies present, such as enhanced connectivity, operational efficiency, and innovative applications, while addressing inherent challenges such as regulatory constraints, cybersecurity threats, and infrastructural costs. Also, the potential of emerging technologies like quantum computing and satellite internet are discussed as transformative forces shaping the future of telecommunications. The paper emphasizes the need for sustainable practices, including energy-efficient networks and green telecommunication initiatives, to mitigate environmental impacts. This comprehensive review provides valuable insights into the dynamic landscape of telecommunications, offering guidance for stakeholders navigating its complex opportunities and challenges.
Telecommunications Evolution, 5G Deployment, IoT, AI, Sustainable Telecommunications
AQUIFER VULNERABILITY ASSESSMENT USING DRASTIC AND GIS IN DAMATURUMETROPOLIS, YOBE STATE, NIGERIA
The study employed DRASTIC model and Geographic Information System (GIS) technology to assess the intrinsic groundwater aquifer vulnerability to contamination in Damaturu Metropolis, Nigeria. The DRASTIC index model uses seven (7) environmental parameters; depth to water level, net recharge, aquifer media, soil media, topography, impact on vadose zone and hydraulic conductivity for the assessment of intrinsic aquifer vulnerability. The DRASTIC scores obtained vary from 81 to 153 which was classified into four vulnerability zones i.e. low, moderate, high and very high. The GIS analysis shows that of the total area of Damaturu Metropolis, about 8.5% was classified as “Low Vulnerability”, 66% as “moderate vulnerability”, 18.8% as “high vulnerability” and 6.7% as “very high vulnerability”. The validation of the DRASTIC model was achieved through comparison between the DRASTIC vulnerability index map and nitrate concentrations of 52 borehole water samples in the area and using Pearson’s (r) correlation coefficient equation. It was found that r = 0.7 which indicates a significantly high correlation between the vulnerability index and nitrate concentration values. Overall, this study produced a very valuable tool for the state agencies responsible for groundwater resource management, environmental protection and planning because it can be very useful in planning and siting activities that generate pollutants (e.g., sanitary landfill, soak away, automobile workshops and petrol station) which pollute the groundwater, in determining where groundwater protection or remediation efforts be directed to, thereby reducing the overall cost of groundwater monitoring in Damaturu metropolis.
CHARACTERIZATION AND GAUSSIAN DISTRIBUTION OF TENSILE STRENGTH OF SOME REBARS PRODUCED FROM SECONDARY SOURCE
In this work, the chemical, physical and mechanical characterization of two rebars produced locally in some areas in Nigeria were investigated. The chemical composition, phases and microstructure of the rebars were determined using optical emission spectrometry (OES), X-ray diffraction (XRD), optical microscopy/scanning electron microscopy (SEM). The mechanical characterization was determined using hardness, and tensile strength. The tensile strength data of the rebars were subjected to Gaussian distribution. From the results, it shows that the steels have carbon content in the range 0.444-0.864 wt% C. The XRD result shows the presence of ferrite and sharp peaks of cementite. The optical micro-scope shows the presence of large amount of cementite in the ferrite matrix. The SEM analysis shows the presence of defects in the microstructure. The Gaussian distribution shows wide variability in the tensile strength data of the rebars. From the chemical, physical and mechanical characterization results, it shows that the rebars are below the standard quality.
Rebars: microstructure; gaussian distribution; ferrite; cementite
Development of Anionic Surfactant from Neem Seed Oil for Enhanced Oil Recovery (ROE) Applications
The demand for sustainable and eco-friendly alternatives in the oil and gas industry has intensified interest in bio-based surfactants for enhanced oil recovery (EOR). This study investigates the synthesis and characterization of an anionic surfactant derived from Azadirachta indica (neem) seed oil, offering a biodegradable and cost-effective substitute for conventional synthetic surfactants used in sandstone reservoirs. Neem seeds were cleaned, air-dried, milled, and subjected to solvent extraction using Soxhlet apparatus with n-hexane. The extracted oil underwent proximate and physicochemical analyses to assess its surfactant-producing potential. Proximate analysis revealed a moisture content of 28.76%, oil content of 14.44%, protein content of 16.67%, and ash content of 5.10%. Physicochemical parameters such as acid value (1.88 mg KOH/g), saponification value (184.80 mg KOH/g), oil yield (23.00%), viscosity (32.45 mPa·s), and surface tension (31.55 mN/m) supported its suitability for surfactant production. An anionic surfactant was synthesized via saponification followed by sulfonation with concentrated sulfuric acid under controlled temperature and agitation. The pale-yellow final product exhibited foam formation, indicating surface activity. The results of FTIR analysis confirmed the chemical transformation, with characteristic peaks at 3290 cm⁻¹ (O–H), 1640 cm⁻¹ (C=C), and 1104 cm⁻¹ (S=O), validating the presence of alcohol, alkene, and sulfonate functional groups essential for amphiphilic behavior. SEM analysis of the synthesized surfactant at varying magnifications (500x, 1000x, and 1500x) revealed granular and vesicle-like structures. These micelle-forming features are indicative of self-assembly, crucial for interfacial tension reduction, emulsion stability, and wettability alteration in EOR applications. The results demonstrate that neem seed oil is a promising renewable feedstock for the production of effective anionic surfactants. Its favorable chemical profile, successful structural modification, and supportive morphology confirm its potential in green chemical applications such as EOR, detergents, and emulsifiers. Future work should include performance testing under simulated reservoir conditions to validate field applicability and scale-up potential, especially in regions rich in neem resources.
An Improved Canny Edge Detection for SVM-Based Brain Tumor Image Classification
This paper proposes an efficient method for automatic detection and classification of brain tumor. The proposed system incorporates a multi-stage processing that includes preprocessing, segmentation, feature extraction, feature selection, and classification. Preprocessing techniques including median filtering and high pass filtering are employed to enhance image quality; the segmentation process involves Canny edge detection and moving average filtering. Feature ex-traction is performed using Convolutional Neural Network (CNN), and feature selection is conducted using Minimum Redundancy Maximum Relevance (mRMR) to ensure optimal feature representation. Support Vector Machine (SVM) trained on datasets of varying sizes is deployed as a classifier. Evaluation results revealed that the proposed method achieved detection and classification accuracy of 95.83%. The proposed method is expected to facilitate early-detection, improve the accuracy in diagnosis of brain tumors, and ultimately make efficient the work of medical practitioners.
Monitoring Desertification and its Environmental Effects in Sokoto State using Geospatial Approach
Report by the International Food Policy Research Institute highlights that Nigeria loses approximately 0.35% of its total land area estimated at 923,768 km² to desertification annually. As of the year 2020, about 35% of the country's landmass is under threat, putting the livelihoods of over 40 million land dependent individuals at risk. This study employs Remote Sensing and Geographic Information System (GIS) techniques to monitor the rate of desertification in Sokoto State, Nigeria. Multi-temporal satellite imagery, including Landsat TM (2000), Landsat 4 (2005), Landsat 7 (2010), and Landsat 8 OLI (2015, 2018), was obtained from the United States Geological Survey (USGS). Geometric and atmospheric corrections were applied to enhance image accuracy. Supervised classification using the Maximum Likelihood algorithm was adopted, and desertification indicators precipitation, temperature, and population were integrated with land use/land cover (LULC) outputs for comprehensive analysis. Findings reveal a substantial decline in vegetated areas from 33.4% in the year 2000 to 12.3% in the year 2020, largely driven by urbanization, agricultural encroachment, and possible climate factors. Built-up areas significantly increased from 15.1% to 32.3% over the same period, indicating rapid urban development. Bare soil coverage exhibited fluctuations, with a notable rise from 41.6% in the year 2015 to 52.8% in 2020. Water bodies declined from 2.6% in 2000 to 1.6% in 2020, raising concerns over water availability. Projections using Markov cellular automata algorithm for the year 2025 and 2030 indicated continued degradation, with vegetated areas recording a decrease trend pattern by 8.2% to 10% and built-up areas to record an increasing trend of 40% to 53% by 2030. These trends underscore the urgent need for strategic interventions and policies aimed at mitigating desertification and restoring degraded lands to sustainable productivity.