LAUTECH Journal of Engineering and Technology (LAUJET)
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Assessment of groundwater quality and suitability for domestic and irrigation use in selected parts of Oyo Town, southwestern Nigeria
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This study attempts to evaluate if the groundwater quality in some parts of Oyo Town, Nigeria is suitable for domestic and irrigation purposes through an integrated analyses of physical parameters, cations, and anions. Groundwater samples from twenty locations were collected and analyzed for indicators such as pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), and major ion concentrations. Laboratory analyses followed standard procedures by the American Public Health Association (APHA), using Atomic Absorption Spectrophotometry for cations and conventional titration for anions. The Water Quality Index (WQI) was calculated to provide a comprehensive assessment. The results show that the groundwater largely meets the World Health Organization (WHO) standards for drinking and irrigation. pH values ranged from slightly acidic to nearly alkaline, while EC values indicated low to moderate salinity levels. Most samples exhibited low Sodium Adsorption Ratio (SAR) and Soluble Sodium Percentage (SSP), suggesting minimal sodium hazards for soil and crops.The groundwater in Oyo Town is suitable for drinking and irrigation, although variations in water quality highlight the influence of both geogenic and anthropogenic factors. This study highlights the general suitability of groundwater in Oyo Town for drinking and irrigation, with variations in quality underscoring the need for ongoing monitoring and sustainable management.
 
Development and performance of a hot-press machine for particle-board production
Get PDFWaste management in wood and agricultural processing industries conserves resources, energy, and money. Hot-press machines can help small-scale producers afford essential materials like lamination, composites, and woodworking, but current models struggle with tracking process parameters, frequent maintenance, and productivity loss. This research aim at development of a hot-press machine for composite production. Design for the machine include the frame upper and lower platens, hydraulic-jack-base, and mixing-unit, control-box, mould and mould-plate. The components bought off-shelve were hydraulic-jack, heating-element, thermostat, and pressure-gauge. Design done accordance to standard methods. Fabrication process was done at Works and Maintenance Metal Workshop, University of Ibadan. Agricultural-waste Z.mays-cob were sourced, milled, air dried and sieved and retention on sieve number 2.00mm was used for the composite production. Performance evaluation of the machine was done using 60:40; 70:30 and 80:20, of Z.mays-cob particle and Urea-Formaldehyde as the composite-mix ratio. Density, water-absorption, and thickness-swelling were determined for 2 and 24hours respectively. Optimal temperature was 1200C and regulated with thermostat connected to 1500W heating-element, while 3bar pressure-gauge was incorporated onto a 5ton hydraulic-jack. The board densities were significant. Water-absorption and thickness-swelling were found favourably. The hot-press machine was successfully developed with a cost of $95 and was able to produce composite board suitable for interior usage from agricultural-waste.
Keywords: Z.mays-waste, Pressure, Temperature, Composite material, particle boar
Energy Theft and Fault Detection in Smart Energy Meter Using Fuzzy Logic
Get PDFEnergy theft and fault is a significant global issue in the energy sector, which accounts for non-technical loss in power utilities. Specifically, in Nigerian context, energy theft arises from meter tampering to reduce energy consumption readings, unauthorized or illegal connections, and meter swapping. The nexus of Internet of Things and Fuzzy logic learning techniques applied in smart metering system could mitigate this endemic problem. While there are efforts in literature that address this problem. However, there is still need for an optimal and smart metering system with an increased intelligence and accuracy to address energy theft and fault detection problem in distribution network. Hence, this work presents a solution to energy theft and fault detection in the distribution network by designing a smart energy metering system that applies simple fuzzy logic rules to analyze data thereby detecting theft and fault in the system. The system compares the measurements from a pole-based smart meter and consumer unit meter for detection of fault and theft. The smart energy meter is designed with a microcontroller and uses Wi-Fi communication for data transmission from pole-based metering point and consumer unit metering point to the central server for analysis and processing. The result shows that Fuzzy logic is useful in solving energy theft and fault detection problems. The accuracy and precision are 95% and 95% respectively for theft detection as well as 91% and 95% for fault detection.
 
Characterization and energy analyses of municipal solid waste in Obafemi Awolowo University, Southwestern, Nigeria
Get PDFThis study investigated the potential of generating electricity from municipal solid waste (MSW) generated in OAU. It also determined the quantity of municipal solid waste generated within OAU community and characterized the energy content of the combustible portion of the collected MSW. This was with a view to predicting theoretical quantity of electricity that can be generated from the MSW collected within OAU community. Load-count-analysis was used for the MSW quantification while sampling method was used for the characterization of the collected samples. A total of ten samples of 10 kg each were collected from OAU dumpsite (Asunle) in September 2015 (wet season). The collected samples were sorted, weighed and separated into combustible and non-combustible fraction. The combustible portion was thoroughly mixed and shredded with milling machine to size of less than 3 mm for laboratory analysis. The calorific value of the samples was determined using bomb calorimeter following the standard method. The energy content of the MSW was analysed based on result of the quantification and the composition. Results of MSW quantification indicated that the total amount of waste generated on a daily basis was approximately 4.4 ton. Characterization showed that the waste was made up of approximately 34.8% paper, 18.1% textile, 9.4% electronics, 4.4% metal, 6.2% bio, 6.3% wood and 20.8% miscellaneous. Combustible fraction was 65.4% while the average moisture content was 19.04% on wet basis (w.b). The average calorific value obtained was 10.77 MJ/kg. The energy analysis indicated that, with minimum conversion efficiency (25%), 0.4MW of electricity could be generated. The study concluded that the MSW in OAU has capacity to generate electricity to the tune of 0.4MW on the basis of 4.5 tonne of waste collected per day with minimum conversion efficiency of 25%
Development of a polymer matrix composite reinforced with luffa fibre and white clay: Development of a polymer matrix composite reinforced with luffa fibre and white clay
No full textLuffa is a natural fiber and has found utilization for centuries in various industries, including textiles, craft, and agriculture. The poly matrix materials generally have poor tensile and flexural strength making them to be less preferred in various applications. Therefore, this research aimed to develop a polymer matrix composite reinforced with luffa fibber and clay for use in the automotive and construction sectors. The luffer fruits and white clay were collected from Ilorin and epoxy resin, hardener, and sodium hydroxide (NaOH) were purchased from Jopart Chemical Co-limited, Ilorin, Kwara State, Nigeria. The luffer fruits were stripped of the husks and cut into long fibers. Both the luffer and the white clay were then rinsed using distilled water and sun-dried for 48 hours. The dried luffa and white clay were ground into smaller particles. Eight (8) composite samples were produced from a mixture of resin (65 – 80%), luffa (10 – 70%), and clay (8 – 15%) following the design of experimental techniques. The samples were molded in molding boxes and allowed to solidify for 48 hours. Each sample was characterized for textile strength, toughness, and flexural strength using an Ultimate Tensile Machine while Rockwell hardness was used for the hardness test.
The tensile strength ranged between 4.72 and 18.07MPa, while flexural strength lies between 2.98 and 21.70MPa. The range of Brinell hardness values was 55 – 70BHN and 0.194 – 1.77Nm for toughness. The sample made from 9% clay, 11% luffa, and 80% epoxy gave the highest tensile strength (18.074MPa) and toughness (1.770Nm). Sample with the composition of 20% luffa, 15% clay, and 65% epoxy has the minimum tensile strength (4.723MPa) and hardness of 65BNH. The addition of luffa particles and white clay has been shown to enhance the tensile strength, hardness, and flexural strength of epoxy resin. The reinforced resin can be used for the production of car bumpers
Soft computing based load forecasting using artificial neural networks: a case study of Lagos, Nigeria
Get PDFThis study introduces a soft computing approach using Artificial Neural Networks (ANN) for load forecasting, specifically focusing on predicting the minimum and maximum load power. The goal is to efficiently allocate the expected power to suitable load centers. The analysis utilizes a 3-year historical dataset of load consumption in Lagos, a city in Western Nigeria. A Multi-layered Perceptron (MLP) network is employed to generate short-term load forecasts for the area. The inputs for the network include monthly data, while the output parameters are load data obtained from the energy company, which are used to predict power needs in the geographical area. The ANN training employs supervised learning and the back-propagation algorithm, implemented through MATLAB & SIMULINK. The input and target data are preprocessed and normalized within the range of -1 and 1. The network is continuously trained until desirable regression values and a disparity graph are achieved. The study demonstrates significant success with regression values of 0.96, 0.97, and 0.97 obtained over three consecutive years (2021/2022, 2022/2023 and 2023/2024) which indicate that the model accurately predict the load of year 2024. The developed model holds promise for independent power companies in Nigeria to enhance load allocation planning and forecast expected revenue
Performance evaluation of Osprey optimization algorithm-based proportional integral derivative controller for speed control of a brushless direct current motor
Get PDFABSTRACT Owing to diversity of application, speed regulation of Brushless Direct Current (BLDC) motor is essential in order to achieve best performance of the motor. In this paper, an appropriately tuned controller such as Proportional Integral Derivative (PID) is employed to achieve effective speed control of the motor. In tuning the parameters of PID controller, conventional techniques often pose great difficulties due to non-linearity often exhibited by DC motors. As a solution, metaheuristic optimization techniques are adopted to optimally tune the PID controller parameters for optimal performance of the BLDC motor in terms of speed. Thus, Osprey Optimization Algorithm (OOA) tuned PID controller (OOA-PID) was used to achieve better performance of BLDC motor speed. Kirchoff’s Voltage Law and Newton’s second law of motion were employed to derive the BLDC motor mathematical model. The PID mathematical equation was also described and an optimization model was formulated using the Integral of Time Multiplied Absolute Error (ITAE) and optimized using OOA. The performance of the OOA-PID controller with BLDC motor was evaluated using performance metrics such as rise time, settling time, overshoot and steady state error. Simulations were done using MATLAB (R2021b). Simulation result shows that an OOA-PID controller gave better response when compared with existing ziegler Nichols PID (ZN-PID) used for the same purpose.
 
Application of Box-Behnken Design for the Optimization of the production of pyrolytic bio-oil from Udara seed in a fixed bed reactor through pyrolysis process
No full textThis work optimized the production of bio-oil from udara seeds using pyrolysis. Response surface methodology (RSM) was utilized to analyze the concurrent impact of temperature, particle size diameter, and inert gas flow rate on the percentage yield of bio-oil during the pyrolysis of udara seed. A three-variable, five-level Box-Behnken design (BBD) consisting of 17 experimental runs was employed to formulate a quadratic model for optimizing pyrolysis conditions. The optimum pyrolysis parameters for achieving the highest bio-oil production were a temperature of 422.9 °C, a particle size diameter of 2.5 mm, and an inert gas flow rate of 1.42 L/min. Under these conditions, the bio-oil yield was determined to be 59.73%. The model validation revealed no substantial discrepancy between anticipated and observed values. GC-MC analysis indicates that the predominant monounsaturated fatty acid made up 45.55% of the total fatty acid content, which depicts that the oil belongs to the linoleic acid group. The FTIR analysis reveals that the alkene group contributes to increased reactivity and combustion efficiency, boosts the octane number of the bio-oil, and decreases the boiling point of the oil. FTIR and GC-MS analysis findings confirm that the bio-oil was within ASTM specifications
Predicting customer purchase patterns in online retail using a cnn-based deep learning model
Get PDFAccurately predicting customer purchase patterns in online retail enables personalized recommendations, targeted marketing, and improved business decision-making. However, challenges such as high-dimensional transactional data, class imbalance, and the limitations of traditional Machine Learning (ML) models often hinder predictive performance. In this study, a Convolutional Neural Network (CNN) based model was designed to predict customer purchase behavior from online retail transaction data. CNNs are particularly effective at learning complex patterns and feature relationships, making them well-suited for structured data representation. The experiment was conducted on an online retail dataset comprising customer purchase patterns obtained from the University of California, Irvine repository, one of the most widely used benchmark datasets for evaluating ML algorithms. The performance of the CNN model was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), achieving 93.6% accuracy, 100.0% precision, 91.1% recall, 95.4% F1-score, and an AUC-ROC of 0.98. These results demonstrate that deep learning can effectively model customer purchasing behavior, offering valuable insights for online retail platforms aiming to anticipate customer actions and optimize engagement strategies
Development of a Coati-Optimized Convolutional Neural Network for infected citrus fruit detection and classification system
Get PDFPest and disease management plays a significant role in minimizing losses to crops, particularly in citrus fruit production. Traditional methods for detecting and classifying infected citrus fruits are complex and tasking, while Convolutional Neural Networks (CNNs) offer promising solutions but still face challenges such as high computational requirements and data dependency. Therefore, this study developed an improved convolution neural network for infected citrus fruit detection and classification system using Coati Optimization Algorithm (COA). A dataset of 1,790 citrus images, containing samples of black spot, greening spot, citrus canker, and healthy fruits, was acquired from www.kaggle.com. The images underwent preprocessing involving cropping to remove unwanted elements, conversion to grayscale to simplify processing, normalization to enhance data consistency and reduce redundancy, and filtering to minimize noise. An optimized CNN model was formulated using COA to tune the hyperparameters (weight and learning rate) of CNN to produce Coati Optimization Algorithm–based Convolutional Neural Network (COA-CNN). The preprocessed images serve as input to the COA-CNN model. The COA-CNN was used for the extraction of edges, corners, texture, patterns and shapes, and classification of citrus fruits as infected or healthy. The developed system was implemented using MATLAB R(2023a). The system’s performance was evaluated using accuracy, false positive rate, sensitivity, specificity, and recognition time. A comparative analysis of CNN and COA-CNN was also carried out. The accuracy, false positive rate, sensitivity, specificity, and recognition time for CNN were 95.83%, 6.02%, 96.63%, 93.98% and 202.17 s, respectively, while the corresponding values for COA-CNN were 96.92%, 4.22%, 97.41%, 95.78% and 136.86 s. This research showed that COA-CNN performed better and is recommended for citrus disease detection and classification systems