ABUAD Journal of Engineering Research and Development

Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture

Samuel Omaji — 2024-07-01

Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.

Development of an Automatic Phase Selector for Nigerian Power Utility Customers

Lambe Mutalub Adesina — 2024-07-01

Power utility customers in a developing country like Nigeria have constituted a habit of changing the electricity supply line from an unavailable or unstable phase to the most available or stable phase. The category of customers involved in this character are those on single phase power supply. However, this act is being carried out manually at the meter point using the cut-out fuses. This attitude results in phase unbalances, overheating electrical equipment including feeder pillars, transformer coils, network faults, and overall system instability. Thus, this paper presents the development of an Automatic Phase Selector for Nigerian Power Utility Customers. The device automatically selects an available phase from the three-phase power supply lines. The research comprises designing an automatic phase selector circuit, simulation of the designed circuit, programming code development in C- Language for the microcontroller, construction of the designed circuit, and carrying out tests on completed work done to ascertain the effectiveness of the developed system. The system operation involved a three-phase supply from the closest distribution network of the power utility company which is connected to a three-in-one gang switch while the switching ON and OFF of their static switches represent phase-off in an ideal situation. The operational results of this system are presented in the form of the truth table which indicates that the affected customer would not have a power supply only when the 3-phases are under voltage or overvoltage or unavailable. This implies that one of the three phases that meet the three criteria would be switched ON. A pure sine wave was used as input into the Optocoupler and the output waveform of the rectified pulsating signal is separately displayed. This output waveform is very clean and noiseless. Finally, the system when practically tested with an unbalanced three-phase supply, worked perfectly enhancing the flexibility of operating an Automatic Phase selector and hence avoiding manual switching of the phase selector which has been attributed to changing of cut-out fuses and associated stress as well as having a user-friendly phase selector.

Unlocking the Potential of Palm Kernel Shell and Quarry Dust: A Cost-Driven Approach to Replacing Sand and Gravel in Concrete

Hassan Abdullahi Maikano — 2024-07-10

This research investigates the potential of palm kernel shells (PKS) and quarry dust (QD) as sustainable and cost-effective replacements for sand and gravel in concrete production. The study explores the impact of varying PKS and QD content on workability, density, water absorption, and mechanical properties. While increasing these alternative aggregates decreases workability and density, it improves water absorption and, in some cases, mechanical strength. Response Surface Methodology (RSM) identified a combination of 5% PKS and 20% QD (-1, -1) as the optimal replacement level for achieving a balance between cost and performance. This mix offers a significant cost reduction of 18.2% relative to concrete made with conventional aggregates. The study highlights the potential of PKS and QD as sustainable alternatives for conventional aggregates. Utilizing these readily available waste materials can reduce reliance on natural resources, promote waste management practices, and contribute to a more environmentally friendly construction industry. Additionally, the research suggests that quarry dust alone might be a more suitable replacement material than PKS due to its superior influence on concrete strength. This research provides valuable insights for optimizing concrete mix design with PKS and QD, promoting cost-effective and sustainable construction practices in regions with abundant palm oil production and quarrying activities.

Evaluation of Effective Interfacial Area in a Rotating Packed Bed Equipped with Dual Gas Inlets

Usman Garba — 2024-07-10

This study investigates the effective interfacial area in a novel rotating packed bed (RPB) equipped with dual gas inlets instead of the conventional single-gas-inlet RPB. The aim is to enhance the mass transfer efficiency of gas-liquid contacting processes in RPBs by increasing the number of gas inlets to improve the spread of gas supply into the packing. The RPB is a promising gas-liquid contactor configuration known for its intensified mass transfer characteristics. However, the impact of additional gas inlets on the effective interfacial area of the packing remains unexplored. An experimental method assessed the interfacial area under varying operational conditions which include a liquid flow rate of 0.30-0.60 m³/h, a gas flow rate of 100-300 Nm³/h, and a rotation speed of 600-1000 rpm. At operating conditions covering the maximum rotation speed of 1400 rpm, gas flow and liquid flow rates of 300 Nm³/h and 0.60 m³/h respectively, the results showed that on average, 55 to 97% of the 2400m²/m³ specific packing area can be effectively utilized for gas-liquid mass transfer during separation operations using the RPB. Compared to results reported for single-gas-inlet RPBs using similar packings, the RPB with double gas inlet proved to provide higher utilization of the packing. By simply doubling the number of gas inlets, the findings provide valuable insights into optimizing RPB designs and operations which could enhance mass transfer efficiency for various chemical and environmental applications.

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

Folashade Olamide Ariba — 2024-07-10

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

Response Surface Methodology Optimization of Wear Rate Parameters in Metallic Alloys

Blessing Ngozi Goodluck Aliemeke — 2024-07-10

The optimization of wear rate parameters in metallic alloys using Response Surface Methodology (RSM) has been experimentally performed. The wear rate, a critical factor affecting the durability and performance of metallic components, served as the response parameter, while track diameter, sliding speed, and mass difference were considered as independent variables. The Central Composite Design (CCD) experimental method systematically explored the response surface and optimizes the wear rate. A mathematical model was developed, revealing a significant p-value of 0.043 in the ANOVA table, indicating the collective influence of the independent variables on wear rate at a significance level of 0.05. Furthermore, the model demonstrates a substantial explanatory power, with R-squared of 69.45% and adjusted R-squared of 51.95%. The p-value calculated to be 0.60 for the statistical Lack of fit indicated a satisfactory model. These findings highlight the effectiveness of RSM in optimizing the experimental input values and offer valuable insights for enhancing the durability and performance of metallic alloys in various industrial applications. The obtained result addresses the problem of uncertainty inherent in optimal levels of input parameters wear experimentation.

Models Development for Prediction of Blast Efficiency and Total Charge in a Typical Quarry

Kayode Augustine Idowu — 2024-07-16

The prediction of blast efficiency is usually achieved by using models; this in turn, gives better and more efficient rock fragmentation. However, the accuracy of the prediction often times relies on the model development validation. In this study, models were developed and compared upon validation for predicting the blast efficiency and total charge required for efficient fragmentation using artificial neural network (ANN). Rock samples were gathered from the study are, and the uniaxial compressive strength (UCS) test was carried out on all the samples based on international standard. The average UCS obtained from the rock samples at the Eminent quarry (EQ) is 153.61 MPa. The dimension of in-situ rock mass considered in the study area is 60 m x 40 m, and the in-situ block sizes obtained vary from 2.02 m² to 3.20 m². The average percentage value of F₅₀ obtained from the Split-Desktop image analyses is approximately 72.44 cm. The various results obtained from the UCS, in-situ block size distribution, image analysis of the blasted rocks and the total charge were used to develop the models for the prediction of blast efficiency. The key issue of concern about these models is that they are mostly site specific and the fact that if they perform well in a location does not guarantee the other. Hence, the validation and suitability of these models on the mine site. The blast efficiency prediction using ANN is compared with measured efficiency and the value of coefficient of determination, R² obtained is 0.9733. The value of the coefficient of determination, R² obtained from ANN by comparing the prediction of the total charge and the measured total charge is 0.9773. The findings showed that, the proposed ANN based mathematical models are suitable and thus, give better prediction to blasting efficiency and the possible total charge.

Enhancing Social Engagement among Online Learners' Using AI-Driven Tools: National Open University of Nigeria Leaners' Perspective

Christiana Uchenna Ezeanya — 2024-07-16

The need for online education has increased significantly. People now prefer to work to fulfill the necessities of life and pursue education to advance their skills because of the rising difficulty. This quest increases the demand for distance education thereby raising questions about how distance learning institutions can effectively assist their learners. Employment of Artificial Intelligence (AI) tools will not only provide solutions but also improve and render effective service and support to learners. AI-driven tools such as personalized or adaptive learning and chatbots for learner support have significantly helped to improve efficiency in virtual environments. This research aims to investigate how National Open University of Nigeria (NOUN) students view the contribution of AI tools in enhancing social interaction in their virtual learning environment. The study seeks to determine the requirements, inclinations, and challenges related to social interaction in the online learning space and explore how AI-powered solutions might effectively address these challenges to create a more dynamic and engaging learning environment. A survey was conducted to ascertain the level of awareness among the learners on the use of these tools, the challenges related to social interaction in online space and explore the ways AI-powered tools can effectively address issues in the learning environment to create a more dynamic and engaging learning environment. This study has identified that a greater number of learners in NOUN have little or no knowledge of the availability of these tools as well as how they can effectively use it. The level of awareness of the learners on the use of these tools is low. The study found 27.5% awareness and usage of AI tools provided by the institution. Several platforms were identified by respondents; however, ChatGPT was the most widely used AI platform. The study also discusses the importance of AI tools in enhancing collaboration and social engagement among learners. It identifies the challenges in integrating AI in Education and provides possible solutions to the challenges.

Investigating Internal Heat Exchanger Performance in a VCR System with a CO2 and LPG Refrigerant Mixture

Taiwo Elizabeth Oshodin — 2024-07-16

In this study, an attempt was made to develop a cooling system with an internal heat exchanger using a mixture of carbon dioxide (CO₂) and liquefied petroleum gas (LPG) as refrigerants to help eliminate the global warming potential and other harmful environmental effects caused by conventional refrigerants'. The CO₂ and LPG refrigeration experimental setup was constructed with varying sizes of capillary tubes, a pressure controller, an evaporator, and a gas hob. The working ranges were initially confirmed through exploratory experiments with low-pressure and high-pressure flow circuits, using and without an internal heat exchanger (IHE). The evaporator temperature helped to determine the proportional changes in the coefficient of performance (COP). The REFPROP software design was used to conduct experiments and determine the important process parameters. A confirmation test was performed to validate the expected results of the REFPROP software technique. The results showed that the experiments conducted using IHE had a COP with greater performance levels as follows: mean of 1.398 and SD of 0.367 which is greater than the value of the experiments undertaken without IHE which had a COP performance levels as follows: mean of 0.67 and SD of 0.19. The Paired Samples T-test found these differences to be significant, at p-value < 0.033. The null hypothesis was rejected, hence there is evidence to suggest that the COP of the experiment with IHE is statistically greater than the COP of the experiment without IHE, with a 95% confidence interval of -1.357 and -0.099

Mitigating the Impact of Climate Change on Vegetable Farming: An Evaluation of Artificial Planting Technique

Samson Ayorinde Akangbe — 2024-07-16

A worldwide issue, global warming results from human activity changing the climate and having a negative impact on people, animals, and plants. However, in terms of plants, the sun provides the primary elements required for healthy growth of photosynthetic plants, which use the energy from the sun to create food for themselves. Light with varying wavelengths that serve distinct functions during the photosynthetic process are the essential elements that are captured from the sun. The wavelength of the ultraviolet (UV) component of sunlight varies, characterized as UV A (315–400 nm) and UV B (280–315 nm) are the primary components that must be precisely proportioned for a profitable farming. In order to lessen the impact of climate change on vegetable farming, this research suggests integrating light emitting diodes (LEDs) in artificial growing machines as well as planned irrigation systems as an alternate source of ultraviolet sunshine. To provide the necessary UV light combination, blue, red and white colours of light-emitting diodes (LEDs) were combined using diffusers. The red, blue, and white LEDs were used for two weeks, each 12 hours a day, to influence the plants growth, with red promoting photosynthesis, white improving it, and blue encouraging stem and leaf growth. An Arduino Uno was used to program both the hardware and software components of the automated growth machine. The outcome of planting varied vegetable plant under LED lights was contrasted with the outcome of planting the identical set of plants under direct sunlight. After the first and second weeks of planting, the plants' performances under both circumstances are comparable.

Elevating Tanzania's Tourism: Integrating GIS, AR and AI for Immersive Exploration and Promotion

lazaro Inon Kumbo — 2024-07-16

This study presents a comprehensive examination of the integration of Geographic Information Systems (GIS), Augmented Reality (AR) and Artificial Intelligence (AI) in tourism promotion in Tanzania. The literature review underscores the significance of these technologies in enhancing visitor experiences, destination management, and marketing strategies. The proposed integrated system design combines GIS's spatial mapping capabilities, AI's personalised recommendations, and AR's immersive content delivery to optimise tourist satisfaction and engagement. Key components include the GIS module for spatial data management, the AI recommendation engine for personalised suggestions, and the AR interface for immersive content overlay. Discussions highlight how the proposed system, by addressing critical challenges in the tourism sector, aligns with existing research findings and reassures its effectiveness. Ultimately, the study emphasises the potential of GIS, AR and AI technologies to revolutionise tourism promotion in Tanzania, fostering sustainable growth and cultural appreciation while enhancing visitor experiences.

Sparse FIR Filter Design using Double Generalized Orthogonal Matching Pursuit (DGOMP)

Samuel Farayola Kolawole — 2024-08-12

In this paper, sparse FIR filter was designed using Double Generalized Orthogonal Matching Pursuit (DGOMP) to reduce memory usage and increasing the speed thereby decreasing computational complexity of the algorithm. Mathematical models were formulated and simulations were conducted to validate the performance of the proposed method. The performance was compared with BOMP and Conventional FIR filter. The results showed that the DGOMP method achieved higher sparsity and a better approximation of an ideal filter. Additionally, the designed sparse FIR filters using DGOMP showed better performance in terms of time of execution when the signal lengths keep increasing, giving a 10% faster execution time when compared to BOMP. The passband and stopband attenuation, as well as ripple values were better, offering the flexibility of parameter adjustment. The results showed that DGOMP is a promising approach for designing sparse FIR filters.

Cybersecurity Assessment and Vulnerability Modelling of Networks and Web Services in Nigerian Colleges of Education

Nnachi Lofty Amah — 2024-08-12

Cybersecurity threats are among the most significant risks facing organizations and government today, and administrative boards have now been held accountable. This is an experimental research activity conducted to perform a holistic cybersecurity assessment and vulnerability modelling on the Information and Communication Technology (ICT) infrastructure and services of Colleges of Education in the six geopolitical zones. The study adopts an integrated bi-modal threat modelling and assessment (IBTMA) method by combining assessment and modelling approaches, which involves mixed-methods, along with computer-based experimentation to comprehensively evaluate and model cybersecurity threats, identify vulnerabilities, and propose effective mitigation strategies. Logistic regression data analysis was used to model the relationship between dependent variables (e.g., presence or absence of vulnerabilities or threats) and independent variables (e.g., cybersecurity practices, system configurations, policies, and staff training programs). This cybersecurity assessment provides the initial understanding of the security landscape and practices. The next step involves using the Microsoft Threat Modeling tool on the assets to identify specific threats. These threats are then prioritized based on their potential impact and likelihood. Assessment result of the vulnerability exposure is supported by the threat modelling report, which shows several threats: tampering, elevation of privilege, denial of service, privilege escalation, information disclosure, and spoofing. Findings from the study indicate that colleges face critical network and web vulnerabilities that need holistic solution.

Drying Process of Senna alata Medicinal Leave: Comparative Empirical and Artificial Neural Networks Modelling of Mass Transfer Kinetics with Energy Analysis

Abiola John Adeyi — 2024-08-12

This study investigated the microwave drying of Senna alata leaves (SAL) for sustainable utilization. The effect of SAL form (un-chopped and chopped) and microwave power (200, 400 and 600 W) on the drying characteristics and energy utilization with comparative semi-empirical and artificial neural network (ANN) modelling was investigated. SAL was dried at the selected drying factors (leaf form and microwave power); and moisture transport characteristics including moisture content, moisture ratio, effective moisture diffusivity, activation energy, energy consumption, specific energy consumption and energy efficiency were determined gravimetrically and empirically. In addition, models were utilized to represent the experimental observations and compared statistically. Results showed that un-chopped SAL had a drying time of 10, 8.87, 7.34 s while chopped SAL had a drying time of 8.34, 5.45, 3.5 s at 200, 400 and 600 W, respectively. The effective moisture diffusivity of un-chopped and chopped SAL ranged between 1.40e-6 - 1.94e-6 m²/s and 1.99e-6 – 3.79e-6 m²/s at 200, 400 and 600 W, respectively; while activation energy was 1.79 and 3.64 W/g, respectively. The un-chopped SAL has energy efficiency of 47.38, 26.71 and 21.52% while chopped SAL has energy efficiency of 56.47, 43.49 and 45.14 KJ/kWs at 200, 400 and 600 W. The range of coefficient of determination (R²) of empirical models was 0.9963 – 0.9994 while R² value of ANN model was 0.9996. It was generally observed that the form of SAL and selected microwave power affected the drying and energy indicators, where size alteration (chopping) and increment in microwave power reduced the drying time and improved the energy indicators. The semi-empirical and ANN models performed well in representing the drying process with ANN having a marginal edge. These results are useful in conservation of SAL, control and commercialization of the microwave drying process.

Comparative Modelling, Sensitivity Analysis and Thermodynamics Study of the Adsorption Characteristics of Dried Nauclea latifolia Medicinal Leaves

Abiola John Adeyi — 2024-08-12

The adsorption characteristics of Nauclea latifolia medicinal leaves were examined across selected temperatures (30–50°C) and water activity levels (0.044–0.900) to assess its storage stability. Both univariate semi-empirical and multivariate statistical models were comparatively employed to represent and predict the observed adsorption characteristics. Additionally, sensitivity analysis was conducted to evaluate the dependence of the adsorption characteristics (that is, equilibrium moisture content (EMC (g/g d.b.)) on temperature and water activity storage factors. The net isosteric heat and entropy of adsorption were also determined alongside compensation theory values. The results indicated that EMC decreased with increasing temperature and increased with rising water activity. The minimum and maximum EMC values of 0.015 and 0.221 g/g d.b. were observed at 50°C. The safe moisture content for storing dried Nauclea latifolia medicinal leaves was 12.6 (g/g d.b.) at 30 to 40 °C and 9 (g/g d.b.) at 50°C. Amongst the models tested, the Peleg model demonstrated best performance, with its R² values ranging from 0.9897 to 0.994 and RMSE values between 0.0039 and 0.0129. Sensitivity analysis revealed that EMC is more sensitive to water activity than to temperature. The net isosteric heat and entropy of adsorption decreased with increasing EMC, indicating that the process was enthalpy-driven. In conclusion, the findings underscore the importance of environmental management in maintaining the storage properties of Nauclea latifolia medicinal leaves. The results of the models are useful in guiding the optimal storage conditions and the design of tailored storage facilities for Nauclea latifolia medicinal leaves.

Reliability-based Assessment of the Structural Integrity of some Existing Reinforced Concrete Columns

Samuel Lambe Akingbonmire — 2024-08-14

Abnormal loading can initiate the progressive collapse of a reinforced concrete building. A progressive collapse may start as a local failure, followed by a sequence of reactions leading to a massive portion failure of an entire structure. Reinforced concrete columns are significant structural elements in ascertaining the integrity of framed buildings. This paper presents the report on the structural integrity of reinforced concrete columns of two selected university buildings labelled A and B investigated by using non-destructive testing techniques. First-Order Reliability Method (FORM) was deployed to process the data from the field in order to generate the implied safety indices for all accessed columns in the two buildings. The computed safety indices decrease as the simulated designed practical axial loads/moments increase for all the assumed steel ratios (0.4%, 1.59% and 6%) based on BS 8110:1997:1. When compared with the target safety level of 3.8 according to BS EN 1990:2002+A1:2005 for 50 years reference period of Class RC2 structural members in the ultimate limit state, almost all the columns passed the reliability test except the columns labelled 71 in building A; and C81A and C85 for building B. The highlighted critical columns show the direction for immediate repairs to forestall the initiation of eventual progressive failure of the buildings.

Comparative Study of the Viscosities and Thermal Conductivities of Groundnut and Coconut Oils Dispersed with Graphene Particles Reinforced with Oleic Acid

Yusuf Aliyu — 2024-08-24

This study addresses some challenges accrued using mineral oil as cutting fluid and suggest alternatives to suitable, eco-friendly, non-toxic and biodegradable solution using vegetable oil. Oils extracted from vegetables are environmentally friendly, biodegradable, and non-toxic compared with mineral oils. To investigate their optimal use for industrial applications, this study tested base oil's thermal-physical properties (kinematic viscosity and thermal conductivity). Temperatures of 40⁰C and 100⁰C were considered for kinematic viscosity, and it was improved with the infusion of graphene nanoparticles and oleic acid. The thermal conductivities of the base oils at temperatures of 50⁰C, 60⁰C, and 70⁰C were tested against the addition of graphene nanoparticles at the same temperatures with compositions of 0.001%, 0.003%, and 0.005%. Thermal conductivity of the groundnut oil at 50, 60 and 70⁰C were 0.495, 0.320 and 0.225 Wm-¹K-¹. The average of the compositions at 50, 60 and 70⁰C were 0.527, 0.33 and 0.25 Wm-¹K-¹. Compare to coconut oil at 50, 60 and 70⁰C were 0.534, 0.318 and 0.214 Wm-¹K-¹, and the average of the compositions at 50, 60 and 70⁰C were 0.622, 0.36 and 0.24 Wm-¹K-¹. Kinematic viscosity increments of coconut oil performed better than groundnut oil at 0.001wt% with 40⁰C is 7.15% and 3.68% for groundnut oil. Groundnut edged coconut oil at 0.003wt% at 40⁰C 17.98% and 11.83%. Similarly, with 0.005wt% at 100⁰C coconut oil improve with 63.70% compare 59.73% of groundnut oil. Groundnut oil has a higher viscosity index than coconut oil without the addition of nano-lubricant 436.3 and 209. With the infusion of nano-lubricant the average viscosity index for groundnut oil is 535.17 compare to 406.25 of the coconut oils. It can be verified that the infusion of graphene nanoparticles in both oils can be deployed in machining applications to reduce the friction between contacting surfaces and dissipate heat from the cutting zone.

Development of a Particulate Matter and Carbon Monoxide Detector

Adetoye Ayokunle Aribisala — 2024-08-24

Air pollution is inarguably a common tragedy in the today’s world: a resultant effect of industrialization and civilization. This work considered two of the most common domestic air pollutants – particulate matter (PM) and carbon monoxide (CO). This developed device is able to detect the presence of these two pollutants in the atmosphere, and trigger an alarm when the levels of these pollutants is above the safe level with respect to the World Health Organization (WHO) standards. NOVA SDS011 and MQ135 were used as the particulate matter and carbon monoxide sensors respectively, 20 x 4 Liquid Crystal Display (LCD) was used as the display unit, and a buzzer as the alarm device which is triggered when the pollutant level is high. The device utilizes Arduino Uno R3 as its microcontroller for controlling the operation of the device. The key contribution to knowledge of this work is the design of a low-cost, portable and modern pollutant detector that can be traditionally deployed in either closed or open environments. On testing the device under different conditions for 500 seconds per condition, the indoor PM2.5, P.M10 and CO levels ranged between 16-19 µg/m³, 43-80 µg/m³and 0.6-1.3 parts per million (PPM) respectively. The outdoor PM2.5, PM10 and CO levels were between 17-23 µg/m³, 19-62 µg/m³ and 0.3-0.6 PPM respectively. These levels are considered reasonable enough compared to World Health Organization safe limits of below 25 µg/m³, below 54 µg/m³ and 9 PPM for the PM2.5, PM10 and CO respectively. The device was further exposed to the combustion of fuels and to a dusty environment to read very unsafe limits. This work helped to develop a cost-efficient pollution detector; even as optimal operating efficiency was retained.

Development of an Automatic Scissors Screw Car Jack

Arinola Bola Ajayi — 2024-08-24

In this paper, an existing manual scissors screw car jack was converted to an automatic scissors screw car jack by introducing a sleeve coupling between the manual screw jack and a 12V DC electric motor, which is powered through a direct current energy flow directly from the car battery. The DC electric motor provides a turning effect to automatically power the screw jack for the purpose of lifting the desired load. A screw jack (manual or automatic) is a simple machine used in lifting heavy loads. It becomes very useful in vehicles when changing a punctured tire. It is mandatory for each vehicle plying an approved road to carry a jack for fixing a punctured tire. However, the operation of most car jacks is done manually and also requires prolonged bending or squatting positions when using the jack, making it difficult for most women, the disabled, the elderly, etc., and above all, not ergonomically suitable for the human body due to crouching and squatting positions when in use, which may result in health complications. The automatic scissors car jack developed was successfully tested on five different types of vehicles, with different weights. Five different tests measurements were carried out on each vehicle. The time taken to lift the vehicles 0.03 m off the ground was recorded. They ranged from 74 s to 90 s depending on the weight of the vehicle involved. The heavier the vehicle, the longer the time taken to lift the vehicle off the ground to the predetermined height of 0.03 m.

Whale Optimization Technique Based Economic Load Dispatch

Modu Abba-Gana — 2024-08-24

This study concentrates on optimizing the Economic Load Dispatch (ELD) for three major Nigerian power systems: Sapele, Jebba, and Egbin. These systems, each comprising varying numbers of generating units and facing fluctuating load demands ranging from 300 MW to 1000 MW, necessitate efficient resource allocation to minimize operational expenses. Employing the innovative Whale Optimization Algorithm (WOA), inspired by the cooperative behaviour of humpback whales, this research tackles the intricate non-linear characteristics of the ELD problem. The primary goal is to determine the ideal power generation timetable that reduces total generation costs while fulfilling power demand constraints. Through mathematical modelling, the power systems and their economic aspects are represented. The proposed WOA-based approach is implemented and juxtaposed against optimization methods to gauge its efficacy in achieving cost-effective load dispatch. In addition to the fast convergence characteristics of the optimization technique, the study reveals minimum optimal generation costs of 150,567 Naira/Hr, 189,352 Naira/Hr, and 244,075 Naira/Hr for the Sapele, Jebba, and Egbin power systems, respectively, under various load conditions. Conversely, maximum optimal generation costs reach 480,431 Naira/Hr, 590,871 Naira/Hr, and 750,453 Naira/Hr for the same systems, demonstrating the algorithm's adaptability to diverse load scenarios.

Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach

Sunday Oluyele — 2024-08-24

Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria.

Assessment of Air Pollution Levels from a Building Construction Site on Lagos Island

Temiloluwa Oluwapelumi Susan Owolabi — 2024-08-24

The introduction highlights the challenges of air pollution from construction activities on a site in Lagos Island, Nigeria, emphasizing the need for comprehensive studies to assess air pollution levels and evaluate its implications for public health and environmental quality. The methodology outlines the monthly data collection process, using the Earth Sense Zephyr (equipped with electrochemical detectors for gases) to measure CO, NO, NO₂, O₃, and Optical light scattering for particles) to measure PM₂.₅, and PM₁₀, and the ARA n-FRM Sampler for additional data collection on PM₂.₅, and PM₁₀. The study found that CO, NO, and NO₂ levels were influenced by construction activities, vehicle emissions and industrial sources, with notable peaks in CO and NO concentrations during specific months. Ozone levels remained consistently low, likely due to the "titration effect," while particulate matter (PM₂.₅ and PM₁₀) showed significant seasonal variation, peaking during the dry season due to construction dust and dry weather conditions. The findings underscore the need for stringent regulatory measures and effective dust control practices, particularly during periods of increased construction activity and dry weather, to mitigate air pollution and protect public health. In conclusion, the study provides valuable insights into the dynamics of air pollution from a typical construction site in Lagos Island, emphasizing the urgency of sustainable interventions to safeguard public health and environmental integrity. The study proposes enhanced monitoring and surveillance, stringent regulatory measures, promotion of sustainable construction practices, and public awareness and education, to address the challenges associated with construction-related air pollution on Lagos Island.

Recent Advances on the Adsorption of Pollutants from Aqueous Media Using Clay-Based Adsorbents

Toyin Adedayo Oreofe — 2024-09-01

The sequestration of pollutants from wastewater remains an active research topic recently owing to persistent disposal of industrial wastewater to waterbodies without adequate management strategies available especially in the developing countries. Different technologies have been employed in which adsorption has found a wide range of application. Today, various low cost adsorbents have been developed and evaluated for the adsorption processes. Clay mineral is one of the low cost natural adsorbents requiring minimum modification to enhance its adsorptive capacities. To maintain a clean and safe environment the water bodies must be free of contaminants of emerging concern. The availability of potable water is a global effort, as two of the UN's seventeen Sustainable Development Goals (SDG) are centered on water which is not surprising. Goal 6 focuses on clean water and sanitation whereas Goal 14 focuses on life below the water. With this in view, the availability of potable water highlights the significance of this study, which analyzes the potential of clay minerals as a good precursor for water treatment. Therefore, this review focuses on the clay minerals, its availability in Nigeria, classification and modification of the clay adsorbent.

Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models

Kingsley Ifeanyi Chibueze — 2024-09-01

The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions are rapidly increasing. It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. This research utilizes a dataset sourced from MainOne Limited, which covered August 18th, 20th, 22nd, 23rd, and 24th, 2023, and included banking operation hours from 7 AM to 4 PM each day. Preprocessing of data is conducted to optimize model training. Following training, various performance metrics including accuracy, precision, recall, F1 score, response time, and confusion matrix are assessed. Results demonstrate that Random Forest outperforms other models in accuracy, precision, recall, F1 score, and response time, with an accuracy of 98.90%. This research discusses the importance of continuous innovation in banking network analytics to tackle evolving congestion challenges. Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further.

Circular Microstrip Antennas in 5G: Evaluating Metamaterial Integration

Israel Adeolu Oluwafemi — 2024-09-14

The rapid emergence of Fifth-Generation (5G) technologies necessitate the development of highly efficient antenna systems with compact design that can support Ultra-Wideband (UWB) frequencies. This work presents the design and enhancement of a Circular Microstrip Antenna (CMSA) for 5G UWB applications using metamaterials. The study focuses on the design of CMSA and the integration of a Complementary Split-Ring Resonator (CSRR) into the circular patch of the CMSA. The design is simulated using Computer Simulation Technology (CST) Studio 2023. The system design without metamaterials achieved a gain of 5.28 dBi and a bandwidth of 353.0 MHz. The integration of the CSRR led to an improvement in gain, 5.39 dBi at 3.8 GHz, which is above most of the literature reviewed, although there was a slight reduction in bandwidth to 135.2 MHz. The objectives of achieving a CMSA design with a gain between 5 to 10 dBi while maintaining a compact size were accomplished. Despite the slight reduction in bandwidth observed when integrating the CSRR into the CMSA, the overall results highlight the significant role metamaterials played in enhancing the performance of microstrip antennas for 5G technology applications.

Appraisal of the Flooding Behaviour of Rotating Packed Beds

Usman Garba — 2024-09-14

Rotating packed beds (RPBs) enhances mass transfer processes because a centrifugal force which is several -times greater than gravity is used as the driving force. The complexity of fluid flow across RPBs has made predicting and accurately determining their hydrodynamic behaviours difficult. The flooding point as a hydrodynamic characteristic is essential for the accurate design and scale-up of RPBs. However, variations in flooding point definitions and methodologies across the literature highlight the need for standardized approaches in studying RPB flooding phenomena. This study compared four approaches based on pressure drop fluctuations and the volume of liquid ejected from the RPB to determine the onset of flooding in RPBs using experimental results from a pilot-scale counter-current RPB. For rotational speeds of 300 -1500 rpm, gas flow rate of 100-300 Nm³/h, and liquid flow rates of 0.39-0.75 m³/h, the pressure drop varied from 314 to 2,100 Pa. Quantitative comparisons of the results based on different flooding point definitions showed wide variations with the values of the pressure drop at the onset of flooding differing by as much as 325 %. A quantitative approach based on virtual observations and the ejection of 8 % of the total liquid flow rate from the rotor’s eye is proposed as the standard method for identifying the onset of flooding in RPBs.

Formulation Ratio Effectiveness of Green Metal Working Fluid (GMWF) as a Bio Alternative for Green Manufacturing

Vincent Aizebeoje Balogun — 2024-09-15

Metalworking fluid (MWF) is essential for ensuring quality products and extended tool life during machining operations. While there are various sources of MWF, the need to minimize health hazards associated with mineral-based metal working fluid now calls for more environmentally friendly green metal working fluid (GMWF) from bio-degradable sources. Also, the effectiveness of vegetable-based GMWF significantly depends on the degree of functionalization. Though some studies considered the issue, the comparative analysis of the effect formulations (variation in concentration) of the constituting elements of the GMWF, especially for the base vegetable oil under consideration; has been grossly underreported. In this study, a GMWF emulsion has been developed from soybeans, palm fruits, and coconut with varying formulation ratios. Physicochemical characterization such as flash point, fire point, pour point, pH, density, and viscosity of the developed GMWF were analyzed. Also, a performance evaluation of the said GMWF was carried out and the investigation has shown that the physicochemical properties of the developed GMWF matched, as a potential substitute for conventional mineral-based MWF. Additionally, a performance evaluation conducted during a mechanical machining operation revealed that the GMWF showed an improved surface roughness of about 10.77% compared to conventional mineral MWF. Observations during the machining operation further revealed that the formulated GMWF demonstrated some level of environmental tolerance as it was not associated with misting or the discharge of fumes. The research outcome will impact green machining science and MWF technology for sustainable mechanical machining and cutting fluid development.

Microstructure Characteristics and Mechanical Properties of Grey Cast Iron at Varied Ferrosilicon Addition

Bolarinwa Johnson Kutelu — 2024-09-15

Inoculation is an essential metallurgical route for controlling solidification conditions of cast iron, consequently, in this study, the influence of varied percent ferrosilicon (FeSi) addition on microstructure and mechanical properties of grey cast iron (GCI) was investigated. A 50Kg capacity rotary furnace was used to melt the charge (Auto engine block scrap, graphite, ferrosilicon (FeSi) and limestone). The casting was produced in a greensand mold with wooden rectangular pattern of length 50 mm and breadth 30 mm. Chemical compositions and carbon equivalent values (CEVs) of the samples were determined, using Optical emission spectrometry (AR 4 metal analyzer) and the expression respectively. Microstructures of the samples were obtained, using metallurgical microscope (model number NJF-120A). The tensile and hardness properties were measured, using Universal tensile tester and Rockwell hardness tester respectively. From the results, C and Si were the major elements. Other trace elements were Mn, P, S and Al. CEVs of both the control and inoculated samples were less than 4.3%. Microstructure of the control sample was comprised of primary dendrites and graphite flakes, while those of the inoculated samples were characterized by varied amount of more developed primary dendrites, longer graphite flakes and austenite dendrites. Also, a number of small MnS particles were observed in relative amount within the microstructures. Tensile and hardness properties of the FeSi inoculated samples were superior to the control sample. Highest tensile strength and hardness values of 76.62 MPa and 99.89 HRB respectively were obtained at the optimum inoculation of 1.5 wt. % FeSi.

Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique

Anthony Kwubeghari — 2024-09-15

The rapid increase in the adoption of 5G networks has revolutionized communication technologies, enabling high-speed data transmission and connectivity across various domains. However, the advent of 5G technology comes with an increased risk of cyber-attacks and security breaches, necessitating the development of robust defence mechanisms to safeguard network infrastructure and mitigate potential threats. The work presents a novel approach for modelling a cyber-attack response system tailored specifically for 5G networks, leveraging machine learning techniques to enhance threat detection and response capabilities. The study introduced innovative methodologies, including the integration of standard backpropagation and dropout regularization technique. Furthermore, an intelligent cyber threat classification model that proactively detects and mitigates malware threats in 5G networks was developed. Additionally, a comprehensive cyber-attack response model designed to isolate threats from the network infrastructure and mitigate potential security risks was formulated. The result of testing the response algorithm with simulation, and considering quality of service such as throughput, latency and packet loss, showed 80.05%, 24.9ms and 4.09% respectively. During system integration of the model on 5G network with stimulated malware, the throughput reported 71.81%. Also, packet loss reported loss rate of 23.18%, while latency reported 178.98ms. Our findings contribute to the advancement of cybersecurity in 5G environments and lay the foundation for the development of robust cyber defence systems to safeguard critical network infrastructure against emerging threats.

Low and High Gas Tungsten Arc Welding Heat Inputs Influence on Corrosion of Stainless Steel Weldments

Bolarinwa Johnson Kutelu — 2024-09-15

In this study, influence of low and high heat inputs on corrosion susceptibility of 304L austenitic stainless steel (ASS) in simulated 0.5 molar solution of NaCl was investigated. Gas tungsten arc welding (GTAW) was used to generate low and high levels welding heat input. Microstructures of the weldments were examined, using metallurgical optical microscope (OMM) (Olympus GX51), while the corrosion behaviours were evaluated by potentiodynamic polarization tests, and corrosion data were recorded, using a computer-based data logging system – Autolab PGSTAT 204N. From the results, the evolving microstructures of the weldments before corrosion were characteristically heterogeneous; austenite (γ) was the leading phase, while ferrite (α) grains were dispersed within the γ matrix. Fusion zone (FZ) and heat affected zone (HAZ) microstructures after corrosion were characterised by pits of varying sizes with different alignments. And at GTAW speed, current and voltage of 7.2 mm/s, 200A and 40V, corresponding to low heat inputs, there were few number and size of pits relative to 1.7 mm/s, 200A and 40V, corresponding to high heat input. Shift in corrosion potentials (E_corr) toward less negative direction, that is more nobility was observed at the low heat inputs induced GTAW parameters as compared to the corresponding high heat inputs induced GTAW parameters. In general, corrosion susceptibility of 304L ASS in the simulated 0.5 molar solution of NaCl was heightened at high heat inputs induced GTAW parameters as compared to the corresponding low heat inputs parameters.

Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection

Nwamaka Georgenia Ezeji — 2024-09-15

Electricity theft is a significant challenge for utility companies worldwide, leading to substantial economic losses and inefficiencies in power distribution. Traditional methods of detecting electricity theft, such as manual inspections and routine audits, are often inefficient and ineffective. To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection. To achieve this, a Particle Swarm Optimization Algorithm (PSO) was applied to improve training performance of the SVM, using data of meter recharge information collected from Enugu Electricity Distribution Company (EEDC). The system effectiveness is validated through extensive testing using real-world data from various regions and scenarios, demonstrating its robustness and adaptability. The system result considering FDR reported that 0.11 was achieved for the particle swarm based SVM model. When TPR was considered for analysis, it was observed that particle swarm based SVM attained a score of 0.89. In addition, Particle swarm based SVM attained PPV of 0.895. In terms of accuracy, the particle swarm based SVM reported an accuracy of 0.857. The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation. The implementation of this AI-driven solution offers numerous benefits, including enhanced detection accuracy, reduced operational costs, and improved overall efficiency of power distribution networks. Moreover, it enables utility companies to take proactive measures to prevent theft, ensuring a more reliable and secure electricity supply for consumers.

Design and Finite Element Analysis of a Thresher for Palm Oil (Elaies guineensis) Extraction Plant

Olukunle Elijah Itabiyi — 2024-09-15

This study presents the design and Finite Element Analysis (FEA) of a thresher used in palm oil (Elaeis guineensis) extraction plants. The FEA was performed to ensure safe and cost effective of the thresher before fabrication. The analytical design of the threshing shaft and drum of the thresher was validated using SolidWorks (2021) CAD software for static simulation, employing plain carbon steel as the material. For the threshing shaft, forces of and were applied at strategic points, resulting in a maximum bending stress of , significantly below the yield strength of . The shaft's diameter of 50 mm was confirmed as adequate with a factor of safety (FOS) ranging from 3.17 to 142.42, validating the shaft design's safety for fabrication. Similarly, the drum unit, supported by a spider arm and cylindrical bars, was subjected to an equivalent twisting moment of 861.25 Nm and a batch weight of 1226.25 N. The maximum von Mises stress of was well within safe limits, indicating robustness under operational loads. The maximum resultant displacement and equivalent strain were respectively which can be said to be minimal, reinforcing the drum's structural integrity. A minimum FOS of 20.45 further highlighted the drum's durability and resistance to fatigue. These results confirm the reliability and safety of the designed thresher components, ensuring efficient and sustainable palm oil extraction.