ABUAD Journal of Engineering Research and Development

Robotic Assistant for Object Recognition Using Convolutional Neural Network

2024-02-12T11:30:30+00:00

Visually impaired persons encounter certain challenges, which include access to information, environmental navigation, and obstacle detection. Navigating daily life becomes a big task with challenges relating to the search for misplaced personal items and being aware of objects in their environment to avoid collision. This necessitates the need for automated solutions to facilitate object recognition. While traditional methods like guide dogs, white canes, and Braille have offered valuable solutions, recent technological solutions, including smartphone-based recognition systems and portable cameras, have encountered limitations such as constraints relating to cultural-specific, device-specific, and lack of system autonomy. This study addressed and provided solutions to the limitations offered by recent solutions by introducing a Convolutional Neural Network (CNN) object recognition system integrated into a mobile robot designed to function as a robotic assistant for visually impaired persons. The robotic assistant is capable of moving around in a confined environment. It incorporates a Raspberry Pi with a camera programmed to recognize three objects: mobile phones, mice, and chairs. A Convolutional Neural Network model was trained for object recognition, with 30% of the images used for testing. The training was conducted using the Yolov3 model in Google Colab. Qualitative evaluation of the recognition system yielded a precision of 79%, recall of 96%, and accuracy of 80% for the Robotic Assistant. It also includes a Graphical User Interface where users can easily control the movement and speed of the robotic assistant. The developed robotic assistant significantly enhances autonomy and object recognition, promising substantial benefits in the daily navigation of visually impaired individuals.

Healthcare Waste Management: An Overview

2024-06-21T12:28:19+00:00

Healthcare waste (HCW) is a vital global issue that cannot be overlooked due to its threat to humans and the environment stemming from its infectious and hazardous nature. This study examines previous works undertaken on healthcare waste management
(HCWM) practices around the world, notably the developing countries with a particular interest in segregation, collection,
transportation, treatment, and disposal of HCW. This study draws attention to the environmental hazards arising from each stage of
HCWM. Factors affecting HCWM practices have also been discussed. This study revealed evidence of poor HCWM practices in many
developing countries. It also showed the impacts of human and non-human factors on HCWM practices. Proper documentation,
sufficient budget, adequate supply of HCWM materials, frequent training of healthcare workers, and development of local manuals and
guides are essential if a country is determined to achieve an efficient and sustainable HCWM system. Liquid HCW needs to be
investigated as much as the solid HCW. Exploration of HCW minimization, reuse, and recycling opportunities is recommended for
future research. The use of Modern-day technology such as Artificial Intelligence and geographic information system (GIS) has
provided good results so far. However, they can be explored further for prediction, real-time monitoring, and reporting of HCW. The
present study can be adopted as a guide in discussing issues about HCWM.

Mechanical Characterization and Dynamic Mechanical Analysis of Recycled Low Density Polyethylene Filled Unsaturated Polyester Composite

2024-06-21T12:32:33+00:00

Unsaturated polyester resin (UPR) is widely used as matrix in composite development; however, it has poor toughness property. To solve this problem, many researchers have used different tougheners to modify the resin, but the use of recycled low-density polyethylene (RLDPE) has not been explored. This work is aimed at modifying unsaturated polyester resin (UPR) with recycled lowdensity polyethylene (RLDPE) as a toughener and establishing the effects on the mechanical and dynamic mechanical performance of the RLDPE-filled polyester composite. Unsaturated polyester resin was modified with 1.18 mm RLDPE at different proportions of 1-4 wt%. Casting method was used for the production and the mechanical and dynamic mechanical analyses of the produced composite materials were carried out using ASTM standards. UPR modified with 1.5 wt% RLDPE exhibited the best impact than the un-modified UPR. The control (un- modified) sample had the highest flexural and tensile strength of 18 MPa and 14.02 MPa respectively which was about 26% and 25% higher than UPR modified with 1 wt% RLDPE. The Dynamic Mechanical Analysis (DMA) result showed that the composite does not depend strongly on the modifier loading as no regular pattern was observed for storage modulus, loss modulus and damping factor respectively.

Development and Performance Evaluation of a Heart Disease Prediction Model Using Convolutional Neural Network

2024-06-21T12:38:37+00:00

Heart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics from
the World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people under
the age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction and
artificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learning
techniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employs
Convolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing the
model was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision,
recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracy
respectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positive
classes. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNN
and Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered.
Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learning
models. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future research
should consider increasing the data size for model training to achieve improved accuracy

Unlocking the Power of Waste Cooking Oils for Sustainable Energy Production and Circular Economy: A Review

2024-06-21T12:46:30+00:00

In the pursuit for sustainable energy solutions, biodiesel has come to prominence as an alternative to petroleum-derived diesel. This review delves into cutting-edge developments in production of biodiesel, emphasizing use of waste cooking oils (WCOs) as an environmentally friendly raw material. Incorporating waste cooking oils (WCOs) into the biodiesel production process not only tackles environmental issues associated with improper disposal but also adheres to the principles of a circular economy. This manuscript covers various methods and technologies for converting WCOs into high-quality biodiesel, emphasizing economic viability and environmental benefits. It discusses the potential of WCO-derived biodiesel to meet stringent fuel standards and reduce greenhouse gas emissions. Significant progress has been made in using waste cooking oils to generate sustainable energy, aligning with broader initiatives focused on renewable energy and circular economy principles. In summary, the utilization of waste cooking oils for biodiesel production presents an opportunity to shift away from reliance on fossil fuels, thereby fostering circular economy practices and sustainability goals.

Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis

2024-06-21T12:51:19+00:00

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults
in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in
conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic
and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and
time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to
categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models
exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest
accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover,
Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%,
and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

Harnessing Abuja's Municipal Solid Waste as a Renewable Energy Source: Scanning Electron Microscopy Analysis

2024-06-21T12:55:46+00:00

A study of Abuja’s municipal solid waste (MSW) samples using the scanning electron microscopy analysis was undertaken in this work. In the face of the severe energy poverty being experienced in Nigeria which largely depends on diminishing fossil fuel resources coupled with the associated problem of greenhouse gas emission, the energy potential available in municipal solid wastes needs to be investigated. Using MSW as a fuel source for electric energy production will also positively impact on Abuja’s waste management. This present study requires the analysis of the MSW with aim of confirming that products of its incineration will not be hazardous to the environment. ASTM E 1508 procedures for utilizing the scanning electron microscope (SEM) were followed to identify elements that would be contained in the bottom ash of the incineration process of samples of Abuja’s municipal solid wastes obtained from selected districts of the city. Elemental composition of the bottom ash that will be formed from incineration of Abuja’s MSW was obtained by the use of energy dispersive x-ray analysis. The micrographs plotted indicate that silicon and iron are the principal elements present in the samples with values for silicon and iron being highest at 49.5 and 19.55%, respectively, for the sample from Dutse-Alhaji. The tests also show the presence of silver in the organic wastes generated in Abuja, while presence of sulphur is very minimal. The silicon levels present in Abuja’s municipal solid waste compare well with values for Nigerian coals which have percent silicon contents ranging from 39.0 – 49.4% (Enugu coal – 39.0%; Okaba – 44.8%; Maiganga – 49.4%). The test results also show that Abuja’s MSW samples had grain sizes ranging from 3.5 mm 16 mm. The results indicate Abuja’s MSW combustion rate will be lower than for pulverised coal which is known to have much lower grain size in the range of 75 μm to 106 μm and will need shredding before firing since grain size is a very critical determinant factor in solid fuel combustion rate and burn-out time. The tests conclusively show that Abuja’s MSW will be a more environmentally friendly fuel than coal because of its lower sulphur content.

Development of an IoT Based Water Quality Monitoring Device for Domestic Fish Ponds

2024-06-21T13:00:58+00:00

This study focuses on developing an affordable IoT-based water quality monitoring system for domestic fish ponds. The
system aims to enable remote monitoring of critical water parameters, offering real-time data access through mobile or web interfaces.
It includes an alert system to notify the pond owners of any significant changes in water quality, allowing swift corrective action. The
initiative stems from challenges faced by aquaculture farmers due to insufficient knowledge about water pH levels. Understanding pH's
importance, especially within the optimal range of 6.5-9.0 for fish culture, is crucial for success. Tests conducted on the system's
performance in detecting various pH levels across different pond environments demonstrated its reliability in identifying low and
normal pH levels. However, anomalies were observed in detecting higher pH levels, indicating potential sensitivity limitations that need
further investigation for system refinement. While the system excelled in detecting low and normal pH levels accurately, improvements
are required for detecting higher pH thresholds to ensure comprehensive monitoring across diverse water conditions. This enhancement
is crucial for effective fish pond management and reducing losses for aquaculture farmers.

Reliability Analysis of a Typical 33kV Distribution Network Using MATLAB (A Case Study of Ile-Oluji 33kV Distribution Line)

2024-06-21T13:08:36+00:00

The significance of a dependable and sufficient electrical power supply in societal development cannot be overstated. The distribution of electrical power holds a crucial position in the power system chain, representing the final stage where it reaches consumers. At this phase, various types of losses, including technical loss and theft, among others are associated. The Ile-Oluji community is situated in Ondo State, a south-western part of Nigeria, A region which is host to federal, state, local and private institutions. This community grapples with inconsistent power supply experiencing intermittent trips without a clear originating cause from components in the Ile- Oluji injection substation and its feeder in Ondo. This study analyses the power distribution patterns in this locality focusing on three primary elements (transformer, switch gear and supply line) extracting data on; failures, outage time, numbers of customers and total hours for the year 2019 to 2022. Reliability indices were employed to assess its performance utilising the MATLAB software. Codes were crafted to extract these indices; Availability, Failure rates, Mean Time to Repair (MTTR), Mean Time Between failure (MTBF), System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI). The results show that there is a strong relationship among the reliability indices. It is adduced that the lower the failure rate, the higher the availability of a system. It was concluded that the power system switchgear recorded the highest failure rate amongst the three elements with 0.037341 hrs/year followed by supply line with 0.022541 hrs/year and the transformer with 0.0148 hrs/ year in 2022. In corollary, transformer has the highest availability of 0.94441 followed by supply line with 0.81681 and switchgear with 0.78004 in 2021, switchgear recorded the highest failure rates because it is responsible for executing both forced and planned outages.

Production and Characterization of Ackee Apple (Blighia sapida) Seeds and African Star Apple (Chrysophyllum albidum) Seeds Oil Mixtures and their Biodiesel

2024-06-21T13:13:50+00:00

This paper focused on the characterization of oils and biodiesels derived from Ackee apple seeds and African star apple seeds
obtained from local markets. The oils from individual seeds and their mixtures at varying ratios were characterised for relative density,
free fatty acid, acid, iodine, and saponification, which yielded 0.91 g/cm³ , 1.06 mg-KOH/g, 2.12 mg-KOH/g, 38.36mg-iodine/100g, and 195.74 mg-KOH/g of ackee seed oil, respectively. And 0.89 g/cm³ , 2.105 mg-KOH/g, 4.2 mg-KOH/g, 52.49 mg-iodine/100 g, and 227.7 mg- KOH/g of African star apple seed oil, respectively. The highest relative density of 0.9064 g/cm³ and free fatty acid of 3.73 mgKOH/g were achieved from the mixture of ackee apple and African star apple seeds’ oils at 80 and 20%, respectively, while the highest saponification of 221.264 mg-KOH/g and iodine of 49.66 mg-iodine/100 g were obtained from the oil mixture of 20 and 80%, respectively. Also, the oils extracted from the seeds, were subjected to a transesterification process to produce biodiesel. 144°C flash point, 206°C fire point, and 2.8°C cloud point were obtained from the biodiesel of the oil mixture of 20 and 80%, respectively. Further analysis of the mixtures showed low volatility and high resistance to fire due to their high flash and fire points. The highest value recorded for the flash point is lower when compared with some other seed oils flash points; however this value is higher than the standard flash point for biodiesels .Highest boiling point of 64°C was attained at an oil mixture ratio of 60 and 40%, respectively. This value is too low compared to the normal boiling point range of 315-350°C for biodiesels, and the high acid values recoded for the mixtures make the oils inedible. The oils, however, have advantages over other edible seed oils as they will serve as valuable ingredients in the soap-making industries since they are not competing with food resources.

Modelling and Simulation of Co-Gasification of Chlorella Vulgaris and High-density Polyethylene Using Aspen Plus

2024-06-21T13:23:44+00:00

A technical innovation that holds promise for producing renewable fuel and decreasing waste disposal is the production of syngas from the co-gasification of waste materials and biomass. In this present study, a new simulation model for co-gasifying highdensity polyethylene (HDPE) and microalgae using Aspen plus V10 was built. Several operating parameters, including operating temperature, air equivalence ratio (ER), biomass blending ratio, steam-to-biomass ratio (S/B), and air/steam ratio, were investigated for their influence on the yield and composition of H₂ , CO, CO₂ , and CH₄ . Results indicated that these operating parameters had significant impacts on the gaseous products. High gasifier temperatures (1000°C) for the co-gasification process favored the formation of H₂ and CO and increased their yields. Also, the yield of H₂ significantly decreased when the value of the equivalence ratio was increased. According to simulation results, increasing the steam-to-biomass ratio favored the synthesis of H₂ and CO up to a point. In addition, waste plastic (HDPE) in the feedstock should be kept at a minimum to favor the production of hydrogen-rich gas. The findings show that the model results agree with previous experimental studies. This research study has proven the air-steam co-gasification of microalgae and HDPE as a suitable process for the production of syngas rich in hydrogen.

Influence of Extraction Temperature on the Quality of Neem Seed Oil: Preliminary Investigation

2024-06-21T13:41:11+00:00

The storage of neem oil for a long time before usage comes along with challenge of quality retain ability of the oil; and the extraction methods can affect the quality of neem seed oil. This research work compared the mechanical expression method to the solvent extraction method to find a better method that will give high-grade neem oil for long-term storage. A plant with a capacity of 50 kg/day of neem seed kernel was used to extract oil from neem seed using ethanol as extraction solvent. The increase of extraction temperature from 20 oC (mechanically expressed) to 78 ^oC leads to decrease of iodine value from 62.70 to 60.10 gI2/100 g; increase of acid value from 3.4 to 4.2 mg KOH/g and increase of saponification value from 158.74 to 210.18 mgKOH/g. The Fames standard method was used for the GC – MS analysis and the percentage composition of the polyunsaturated components in the 20 oC (mechanically expressed), 50^oC, 55^oC, 60^oC and 78 oC oils were 21.58, 6.33, 3.09, 1.83 and 0.21% respectively. The changed of extraction temperature from 20^oC to 78^oC brings about reduction of polyunsaturated components from 21.58% to 0.21%. The increase of extraction temperature leads to conversion of unsaturated components to saturated components due to auto – oxidation process. This is clearly seen as the extraction temperature increased from 20^oC to 78 ^oC, the percentage composition of the saturated components increased from 22.40% to 43.70% and the polyunsaturated component decreased from 20.47% to 0%. The fatty acid composition associated with the 78 ^oC oil are: Oleic acid, 46.61%; Stearic acid, 11.83%; Palmitic acid, 16.54%; 11 – Octadecenoic acid, 3.58%; Cis – Vaccenic acid, 5.90%; Cyclopropaneoctanal, 11.19%; Squalene, 0.21% and Trimethylsilyl – di(timethylsiloxy) – silane, 4.14%. The functional groups identified in the 78^oC oil were C – H, C = O, C – C and C – O. Based on the lowest iodine value, lowest percentage composition of the polyunsaturated component value and high percentage composition of saturated component, the neem oil obtained at 78^oC from the miscella is considered as the high grade neem oil because it is less reactive due to lowest percentage composition of polyunsaturated and can be stored for long time before usage. Furthermore, the results from this work will assist manufacturers in selecting the extraction temperature for particular application of the neem seed oil. The extracted oil is recommended for soap production due to its high saponification value.

Evaluation and Multi-Objective Optimisation of Cutting Parameters in Turning of AISI 1020 Mild Steel using Formulated Cutting Fluid

2024-06-21T13:52:14+00:00

Input parameter like the cutting fluid is one of the requirements for minimal surface roughness, cutting temperature, tool wear
and optimal material removal rate coupled with improved machinability and productivity. The evaluation of the optimal factors of
surface roughness, material removal rate, cutting temperature and tool wear in the turning of AISI Mild Steel with the use of ecofriendly fluids. Concerns has been raise globally about the non - biodegradability and non-recyclability of the conventional fluids in the
research space. This prompted the research interest in replacing the mineral oil based fluids with eco-friendly cutting fluid such as
castor seed oil based cutting fluid (CBCF). The locally sourced castor seed oil was investigated for its physiochemical properties as well
as its fatty acid composition (FAC). The cutting fluid was formulated using ratio 1:9 of oil with additives to distilled water and then
characterized. In turning of AISI 1020 Mild Steel, the evaluation of surface roughness, material removal rate, cutting temperature and
tool wear under the CBCF compared to the mineral oil based cutting fluid (MBCF) were carried out using Taguchi experimental design
and Grey Relational Analysis (GRA) for multi-response optimization. The formulated cutting fluid showed pH value of 8.47, viscosity of
0.830 mm²/s, good resistance to corrosion, good stability and milkfish in colour. From the GRA, the multi-response optimal factor combination under the CBCF is (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut, all at level 3 while under the MBCF, it also shows (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut all at level 3. The parameters from Taguchi and GRA results are in agreement with results from other vegetable oil based fluids and this study also contributes and improves the science of machining.

Design and Construction of Voice-Controlled Home Automation using Arduino

2024-06-21T14:00:20+00:00

System automation has been widely researched in the twenty-first century due to its essential role in daily life. The fundamental advantage of an automated system is its ability to reduce human stress and minimize errors. Over the past few years, there has been a swift shift from traditional switches to switches equipped with remote controls. Currently, the presence of conventional wall switches distributed around the house poses challenges in terms of use, especially for individuals who are elderly or have physical disabilities. Due to technological advancements, mobile smartphones are now affordable for all individuals. Android devices are becoming equipped with applications that aid in multiple ways. Another new technology is the Google speech recognition APIs, which enable voice-based system command and control. This study demonstrates the implementation of voice-controlled home automation using the Arduino Uno microcontroller. Users of this system will be able to exercise full authority over every domestic device through spoken commands. The control circuit consists of an Arduino Uno microcontroller that receives and interprets voice commands from an Android smartphone equipped with the corresponding application. While the Bluetooth module shares signal data after establishing a wireless link between the microcontroller and the smartphone, the relay regulates device switching.

Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data

2024-06-21T14:08:29+00:00

Cardiovascular disease (CVD) remains the leading global cause of death, highlighting the urgent need for accurate risk assessment and prediction tools. Machine learning (ML) has emerged as a promising approach for CVD risk prediction, offering the potential to capture complex relationships between clinical and biometric data and patient outcomes. This study explores the application of support vector machines (SVMs), ensemble learning, and artificial neural networks (NNs) for predictive modeling of CVD in patients. The study utilizes a comprehensive dataset comprising demographic and biometric data of patients, including age, gender, blood pressure, cholesterol levels, and body mass index, features. SVMs, ensemble learning, and NNs are employed to construct predictive models based on these data. The performance of each model is evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The results demonstrate that all three models achieve accuracy performance in predicting CVD events, with AUC values ranging from 0.85 to 0.92. Ensemble learning exhibits the highest overall accuracy, while SVM and ANN demonstrate strengths in specific aspects of prediction. The study concludes that Machine learning algorithms, particularly ensemble learning, hold significant promise for improving CVD risk assessment. The integration of ML-based predictive models into demographic practice can facilitate early intervention, personalized treatment strategies, and improved patient outcomes.

Mechanical and Water Barrier Properties of Inhomogeneous Clay NanoParticles Reinforced Thermoplastic Starch

2024-06-21T14:21:04+00:00

This research investigated the development of biodegradable bioplastic as a possible replacement for petroleum-based plastics, which constitute a serious environmental hazard. These hazards include but are not limited to flooding resulting from blocked sewage and danger to aquatic life in marine environments. Solution casting method was used to blend inhomogeneous kaolinite clay nano-particles with distilled water, starch, dilute acetic and nitric acids to produce different compositions of thermoplastic starch (TPS)/Clay composites with clay reinforcements ranging from 2.5 to 10 wt.%. The composites were characterized using X-ray diffraction (XRD), and the mechanical and water absorption properties were determined. The result revealed a 9-fold improvement in the tensile strength (0.72 MPa), flexural strength increased 5-fold (3.34 MPa), and hardness increased 2-fold (23.56 HVN) as well as a reduction in water absorption by 3-fold (6.63%) when compared to the control. Furthermore, the 10 wt.% clay content composite showed the highest mechanical properties. The significant improvement in the listed properties was attributed to a reduction in crystallinity and the formation of new chemical bonds between the thermoplastic starch and the nano-clay. It was observed that the properties of the composites can be further enhanced if a synchronized machine blender (such as an extruder) is employed.

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

2024-06-21T14:30:42+00:00

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

Development of a 1.0 KVA Fuelless Generator

2024-06-21T14:42:13+00:00

A 1.0KVA fuelless generator has been developed. The crave for sustainable energy solution has led to the exploration of
emerging technologies targeted at reducing reliance on fossil fuels and reducing environmental impact. One such technology is the
development of fuelless generators, which harness renewable energy sources or utilize unconventional mechanisms to generate
electricity. This power producing mechanism is aimed to develop a targeted 1KVA power capacity through innovative design strategies.
Detailed graphical modeling of the orthographic projection and isometric views brought about enhanced machine components
development for stability and reliability. Careful selection of conductor materials and design considerations ensured efficient power
transmission, minimizing losses within the system. Integration of power factor correction capacitors and advanced control algorithms
contributed to achieving a power factor close to unity, optimizing energy utilization. Comprehensive performance testing validated the
functionality and reliability of the developed generator under various load conditions. The development of a fuelless generator with a
battery power capacity of 0.85 Kw, torque of 14.48Nm, resistance of 48.35 ohms, current of 4.55 Amperes and a power factor of 0.85
signifies a significant breakthrough in sustainable energy generation. The performance test showed that that an input response brought
about an increase in load (W). The success of developing the generator not only demonstrates the feasibility of clean and sustainable
energy solutions but also underscores the potential for further advancements in fuelless generator technology towards a more reviving
energy prospect.

Feasibility of Wind Energy Utilization for Sustainable Power Generation in Ilorin, Kwara State, Nigeria's North-Central Region

2024-06-21T14:58:05+00:00

The escalating energy demands across Nigeria, especially in remote rural areas, have outpaced the capacity of the national electricity grid, necessitating the development of independent and sustainable energy sources. Among the renewable options, wind energy stands out as a promising solution. This study focuses on assessing the potential of wind energy in Ilorin, located in Kwara State, within Nigeria's north-central region. Utilizing data collected from 2007 to 2021 by the Nigerian Meteorological Agency, the research examines monthly average wind speeds at two specific coordinates in Ilorin, considering variations in air density. The study utilizes a 15-year set of monthly average wind velocities obtained from the Nigerian Meteorological Agency (NiMet) Headquarters in Abuja, measured at a height of 10 meters above ground level. By employing the 2-coefficient Weibull statistical model and extrapolation principles across different altitudes ranging from 150 to 900 meters above ground level, the study reveals distinct seasonal patterns of wind speeds ranging from 1.1 to 5.1 m/s in Ilorin. Furthermore, wind power density values ranging from 6.7 to 39.20 W/m2 are identified, with optimal wind attributes observed at altitudes exceeding 900 meters. These findings provide valuable insights for assessing the feasibility of wind energy utilization and designing efficient systems in Nigeria's north-central regions, aiding in the sustainable energy transition.

Development of Water Level Controller with SMS Capability

2024-06-24T09:02:52+00:00

Water is considered a valuable resource and covers about 70% of the earth's surface. Tight schedule prevents regular checking of the domestic water tank level while refilling the used ones. This often leads to waste of water and increased monthly revenue payment to power utility company. Consequently, this paper presents a development of water level controller with SMS capability aimed at measuring water level smartly such that when tank is filled, message would be automatically transmitted as an alert to the owner using GSM for immediate response. The method involves interconnection of a water container that serves as tank with another open container serving as Bore-hole and a microcontroller-based device with its integrated development to generate a communication message. A flowchart that shows the procedural steps involved was developed. On completion of the prototype, components and subcircuits effectiveness as well as experimental testing of the system effective operation were carried out. Some anticipated water level (AWL) were selected at interval of 2 centimeters (cm) starting from 3cm and the corresponding Message Operating water level (MOWL) were obtained with error deviations not greater than 0.17cm. Thus the average AWL, average MOWL and the error deviation are 11cm, 10.7cm and 0.1cm. The prototype device thus operated effectively

Discovering the Macro-Elements Presence in Biochar Produced Indigenously

2024-06-24T09:16:11+00:00

Biochar boosts soil fertility and helps plants to withstand drought. Its production locally has been a challenge and that is why an Indigenous Biochar Production Kiln (IBPK) was conceived, designed and fabricated at the Workshop of the Agricultural Technology Department, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria. IBK convert biomass to carbon-rich organic material through thermal energy. The IBPK has two drums, the Internal Retort Drum (IRD) and External drum of diameters and heights of 350 mm x 600 mm and 500 mm x 800 mm respectively. The total weight of the IBPK was 82.50 kg. The IRD of 116 kg/m3 volume was loaded with 55 kg biomass from wood waste, covered, and placed inside the external drum. The space between the outer wall of the IRD and the inner wall of the outer drum was 75 mm enough to contain firewood lighted and covered to produce the heat needed for the wood waste inside the IRD to convert it to Biochar. Smoke from the IBK escaped through the chimney attached to the external drum’s lid. The operating time for the carbonization was 182 minutes and the conversion efficiency of the IBPK was 71 %. The average temperature of the IBPK during the conversion was 269 °C. The test carried out on the produced Biochar showed the presence of macro elements that included Nitrogen (2.95%), Phosphorus (21.79%), Potassium (4.95%) and Carbon (70.31%). The fabrication cost was Fifty-Two Thousand, Two Hundred Naira only (₦52,200:00). The IBPK is recommended for farmers to produce Biochar as needed for improved farm yield, and young graduates who want to go into Biochar production as a way out of unemployment.

Taguchi Optimization of Screw Flight Bending Operation

2024-06-24T09:22:20+00:00

The optimizations of screw flight bending operating parameters have been successfully carried out. The optimization of the screw flight bending operation, aims at determining optimal values for key parameters using Taguchi Design and Genetic Algorithm (GA) optimization tools. The parameters investigated include bending radius, diameter of screw, flight thickness, and bending force. Through the systematic application of Taguchi methodology and GA optimization, optimal values of 79.99 mm for bending radius, 69.997 mm for diameter of screw, 5.005 mm for flight thickness, and 232.62 N for bending force were identified. The effectiveness of the optimized parameters was assessed through analysis of variance (ANOVA), revealing an R-squared value of 84.78 % and an adjusted Rsquared value of 75.64 %. These results indicate that the developed model explains a significant portion of the variability in the response variable, providing confidence in the reliability and significance of the optimized solutions. Overall, the integration of Taguchi methodology with GA optimization has proven to be a powerful approach for systematically exploring parameter space and identifying optimal solutions in screw flight bending operations. The optimized parameter values offer the potential for enhanced performance, accuracy, and efficiency in the bending process, contributing to improved product quality and manufacturing productivity.

Assessing the Impact of Media Stream Packet Size Adaptation on Wireless Multimedia Applications

2024-06-24T09:40:38+00:00

Multimedia applications constitute greater percentage of traffic in wireless networks. Thus, require investigation of factors influencing effective delivering of media contents in the future, which will include not only conventional multimedia broadcast, but also video streaming to users on demand while meeting the expected quality requirements. In this article, analysis of effect of media packet
size adaptation on quality performance of multimedia application is presented. Experiments were performed using standard test media
sequences. The encoded media streams at different packet sizes were transmitted over wireless channel at different channel conditions.
The quality performance of received media streams were measured using Peak to Signal Noise Ratio (PSNR) software tool to assess the
impact of media packet adaptation on quality performance of multimedia applications. A comparative quality performance under same
poor channel condition, shows that small media packet size of 256 bytes recorded the highest received quality performance of 22.52dB,
compared to the quality performance of 21.87dB for 384 bytes, 21.37dB for 512 bytes, 20.68dB bytes for 640 bytes and 19.47dB for 768
byes, respectively. The findings show media packet size and channel conditions have significant impact on the quality performance of
wireless multimedia applications

Performance Analysis of Inverter Fed Single Phase Induction Motor Drive

2024-06-24T09:52:40+00:00

This paper presents performance analysis of effects of modulation index on cascaded multilevel inverter fed single phase induction motor drive using closest level control technique. The modulation index is varied to analyze its performance. The effects on inverter output voltage, current, induction motor speed, electromagnetic torque and total harmonic distortion (THD) are considered. Both the load torque and modulation index are varied to determine the motor performance. The Closest (Nearest) control method is applied for generating firing pulses for the cascaded multilevel inverter power switches. By the application of this method, the switching losses are greatly mitigated when compared with high switching frequency of Pulse Width Modulation (PWM) schemes. MATLAB/SIMULINK software is used to obtain the system simulation results. The performance evaluation of this work was targeted on the modulation index range from 0.7 to 1.2 and for selected load torque values of 0 Nm, 2 Nm and 4 Nm. It is observed that modulation index affects the motor performance. When modulation index is 1.0 under different load torques, 6.2 % - 6.7 % range of voltage THD was obtained. When the modulation index becomes 1.2, motor speed and electromagnetic torque resulted to 1327 RPM and 20 Nm respectively with stability time of 0.7 sec under load torque of 4 Nm

Investigation of Flexural Strength of African Copalwood (Daniella Oliveri) as Reinforcement in Concrete Slab

2024-06-24T10:22:29+00:00

This study investigates the potential of African Copalwood as a reinforcing material in concrete slabs. The timber rods were randomly selected from the timber market because there is always variation in timber properties with position in stem, location and soil condition. Various test specimen were prepared according to the Code of practices BS EN 408:2003 using structural size specimens. Thirty (30) slabs of size 75 mm x 300 mm x 700 mm were cast in five sets (1 to 4%) for Timber-reinforced concrete slab (TRCS) and Steel-reinforced slab (SRS) with a mix ratio of 0.5:1:2:4 denoting water-cement ratio, cement, fine and coarse aggregate respectively. Longitudinal bars were varied in 1% to 4% slab cross-sectional area in different TRCS samples while the transverse bar was restricted to be 3% by standard and made constant in all the slab samples specimen. A 10 mm diameter steel bar is used as reinforcement in the SRS. Findings revealed that the mean failure stress for Tensile strength parallel to grain was 41.80 N/mm², Bending strength parallel to grain was 37.05 N/mm², Compression parallel to grain was 13.48 N/mm², Compression perpendicular to grain was 8.88 N/mm², Local Modulus of Elasticity (MOE) was 3800.17 N/mm², Apparent MOE was 59.42 N/mm². Also, with regards to TRCS, the flexural strength test at 28 days for 1% to 4% reinforcement in concrete slabs is 3.61, 5.29, 5.49 and 7.22 N/mm² respectively. The SRS has a flexural strength of 11.54 N/mm² at 87.5% composition in slabs. These findings indicate enhancement in the strength properties of the concrete slabs with the incorporation of the African Copalwood reinforcement.

Design and Performance Analysis of 500KVA Pumped-Water-Energy Storage Solar Integrated Power Plant

2024-06-24T10:37:25+00:00

No Abstract

L-Index-Based Technique for Voltage Collapse Prediction and Voltage Stability Enhancement in Electrical Power Systems

2024-06-24T11:47:47+00:00

Recent years have witnessed a notable increase in the occurrence of blackouts, especially in developing nations, attributed to the continuously growing demand on modern power networks. Given that the demand shows no signs of abating and is projected to increase further in the coming years, additional research on power system stability is imperative. This study, therefore, investigates voltage stability assessment in power systems using the L-index methodology, focusing on the Nigerian 28-bus system and the IEEE system. The L-index offers a practical means of identifying weak buses and evaluating voltage stability margins. Calculating L-index values for load buses under diverse conditions identifies critical points, with higher values indicating vulnerability. The research investigates injecting reactive power at load buses to prevent collapse, comparing outcomes with and without compensation. Analyzing the L-index's performance across varied loading scenarios confirms its precision in predicting breakdown points and identifying critical buses. Load flow analysis of the Nigerian 28-Bus system reveals that only bus 16 exceeds voltage limits, while line analysis shows total power losses. Increasing loadability exposes bus 16 as the weakest, supported by its low voltage magnitude. The research confirms bus 16 as the system's weakest point, guiding corrective measures to enhance stability and prevent collapse. Utilizing Matlab for implementation, this study contributes valuable insights into system vulnerability and provides a framework for improving voltage stability in power systems.

Design and Fabrication of a Bicycle Sprayer

2024-06-24T12:21:47+00:00

The bicycle sprayer designed for crop spraying is a pragmatic and environmentally friendly choice for small-scale farmers, providing both effectiveness and long-term viability. Engineered to be attached on a regular bicycle, this device enables farmers to easily traverse fields and agricultural area with minimal physical effort. The sprayer features a 20-liter tank, suitable for small-scale farms, equipped with a slide-crank pumping system and a precision nozzle assembly to ensure efficient spraying. Made from lightweight and sturdy materials, it is both cost-efficient and highly portable. The rear wheel's rotation is converted into translation motion of the rod and pump by the use of a slider-crank reciprocating mechanism. The bicycle sprayer doubles as a mode of transportation for farmers or users traveling to and from the farm, enhancing its utility. The bicycle sprayer provides an environmentally-friendly option for small-scale farmers, enhancing crop productivity and minimizing the risks associated with liquid chemicals for farmers or operators. Based on the performance outcome, the recently created sprayer is capable of covering an area of one hectare in one hour. It demonstrates improved spray consistency and achieves a 75% efficiency rate, as confirmed through a sequence of tests conducted on specific farms.

Development of a Dual Stripping and Screening Machine for Separating Oil Palm Fruit Bunch

2024-06-24T12:40:38+00:00

The purpose of this study was to design, fabricate, and evaluate a dual stripping and screening machine for oil palm fruits in Nigeria, with the aim of improving efficiency, reducing damage to the fruits, and minimizing injuries to processors. The study involved the design and fabrication of a machine that could easily handle palm fruit bunches (PFB), separate the fruits from the chaff, and minimize damage to the fruits. The evaluation was conducted using a 3×3×3 factorial experiment, with three levels of PFB sizes, three levels of operating speed, and three replications. The evaluation results showed that the optimal operating speed for the machine was 1000 rpm. At this speed, the machine achieved a stripping efficiency of 98.6% for small PFB sizes, 89.93% for medium PFB sizes, and 85.68% for large PFB sizes. The screening efficiency was 98.62%, 96.23%, and 95.68% for small, medium, and large PFB sizes, respectively. The percentage of damage to the fruits was 2.25%, 2.11%, and 2.55% for small, medium, and large PFB sizes, respectively. The output capacity of the machine was 131.25 kg, 139.30 kg, and 165.36 kg for small, medium, and large PFB sizes, respectively. This study presents a novel dual stripping and screening machine for oil palm fruits, addressing the time-consuming and labor-intensive manual post-harvest operations associated with the local method. The machine's design and evaluation provide valuable insights for improving efficiency and reducing damage in oil palm industries in Nigeria.

Optimisation of an On-grid Hybrid Energy System: A Case Study of the Main Campus of the University of Abuja, Nigeria

2024-06-24T13:02:43+00:00

The challenge of meeting the energy demands of institutions and organisations in an economically viable and environmentally friendly manner is becoming more and more complex especially in developing countries like Nigeria. This work presents a resilient hybrid renewable energy system to supply the electric power requirement of the main campus of the University of Abuja, Nigeria, estimated as 900 kW at a consumption rate of 6300 kWh/day. HOMER software was used as the modelling tool for simulations, optimizations, and sensitivity analyses carried out to explore the feasibility of utilizing Abuja’s (MSW) in hybrid with the mini hydro power potential of River Wuye and solar PV resources to meet the load demand of the campus. The hybrid plant has the following component specifications: hydro resource nominal flow rate is 14.5 m³/s; maximum head is 10 m and potential capacity is 885 kW; MSW plant specifications were determined to be 500 kW capacity, waste treatment of 2.3 ton/day; lower calorific value for MSW of 15.84 MJ/kg with the solar PV component having a capacity of 500 kW. Total installation cost for the hybrid plant for the 2 MW hybrid plant was determined to be ₦5.44 billion (US$7.225 million) with annual energy generation calculated to be 799,000 kWh/yr. The net present cost for the simulated system was found to be ₦ 9.37 billion ($12,486,120) with the corresponding LCOE being ₦55.2/kWh ($0.0736/kWh). The carbon emission was estimated to be 7.33 g per day which approximates to a net zero emission, demonstrating the environmental friendliness of renewable energy sources utilised. Sensitivity analysis performed on the system using project life span, inflation rate, solar irradiance, MSW’s lower heating value (LHV), capacity shortage and the annual average volumetric flow rate of River Wuye showed that the net present cost increased with increasing plant life while the levelized cost of energy reduces with increasing life from ₦55.02/kWh for plant life of 25 years to ₦43.73/kWh for 30 years.

Characterization and Beneficiation of Fanibi Laterite for Nickel Metal Recovery Using Froth Flotation Method

2024-06-24T13:26:45+00:00

This study investigates the characterization of Fanibi Laterite and the efficiency of froth flotation to beneficiate nickel from its
host rock sourced in Akure, Ondo State, Nigeria. A sample of the laterite was obtained and characterized by the use of an X-ray
fluorescence Spectrometer (XRFS), X-ray diffractometer (XRD), and Scanning electron microscope with Energy dispersive spectroscope
(SEM/EDS). The ore was comminuted to its liberation size of -125 + 90 µm. The froth flotation process was done using Sodium
hydroxide and Hydrogen tetraoxosulphate (VI) acid as hydrogen potential (pH) modifier within the range of 4,5,7,9 and 10. However,
other reagents are sodium oleate as the collector, potassium dichromate as the depressant, and oleic acid as the frother. The froth and
depressed obtained were dewatered and characterized using XRFS. The result revealed that the ore contained 35.65% Si, 8.24% Al,
10.87% Fe, and 0.07% Nickel. It also included some other associated minerals such as Zn, Mg, Ti, Co, and Mo in trace form of less than
1 %. XRD analysis reveals nickel as nickel iodate. The result reveals nickel assays of 0.14, 0.20, 0.15, 0.25, and 0.12%, while their
recoveries are 70.82, 67.80, 86.70, 98.04, and 86.98%, respectively. The optimum nickel recovery of 98.04% was attained at a pH of
nine (9). It was concluded that froth flotation could successfully beneficiate Fanibi laterite ore for nickel. At the same time, other
minerals such as Titanium, Molybdenum, Tin, and Tungsten were present to be extracted for Metallurgical applications.