Department of Mechanical Engineering

Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/129

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Start date: 2023Has files: Yes

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    Characterization and assessment of selected agricultural residues of Nigerian origin for building applications
    (COGENT ENGINEERING, 2024-12-22) Esther Nneka Anosike-Francis; Gina Odochi Ihekweme; Paschal Ateb Ubi; Ifeyinwa Ijeoma Obianyo; Seun Jesuloluwa; Adekunle Akanni Adeleke; Prabhu Paramasivam; Azikiwe Peter Onwualu; Rasoamalala Vololonirina
    The high rate of agricultural residue generation in Nigeria in recent times poses a serious environmental hazard. Thus, there is a need to valorize these residues for various engineering applications. Five Nigerian agricultural residues (okro, plantain, jute, kenaf, and sisal) were studied to determine their potential for forming natural fiber composites for building applications. The samples were subjected to a process of peeling and immersion in water for 15–20 days to facilitate the degradation of microbial cells and ease the extraction of fibers. Proximate and lignocellulose analyses of the samples were conducted according to the American Standard for Testing and Materials (ASTM) specifications. The physico-mechanical and thermal properties of the agricultural residues were examined using an Intron universal testing machine and a thermogravimetric analyzer. The fiber phase analysis revealed a crystallinity index range of 41.20–66.08% and a crystallite size of 30.79–84.00 nm, indicating that the fibers were thermally stable above 280 °C. Fourier Transform Infrared analysis provided conclusive evidence of the presence of distinct chemical compositions and their associated functional groups. The study contributes a reliable database for agricultural residues in Nigeria, particularly for construction applications. It is also being utilized to inform the design and implementation of manufacturing processes for roofing tiles and boards intended for general applications
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    Understanding casting behaviour of low carbon high manganese steel through detailed characterization of mould powder and mould top slag
    (Taylor and Francis, 2023-02-02) D. Paswan; J. K. Ansu; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; C. T. Christopher; T. K. Roy; P. Palai; M. Malathi
    This study focused on multistage characterization techniques in developing an understanding of the abnormal casting behaviour of low carbon high manganese (LCHMn) steel. In addition to raw mould powder used for casting LCHMn steel, mould top slag samples were also collected for normal and abnormal casting conditions. Raw mould powder and top slag samples were characterized using XRF, XRD, and SEM-EDS to determine chemical composition, crystallinity and morphology. The chemical composition results revealed deviation of normal and abnormal behaviours from the mould powder due to the pickup of oxides of Al, Mn, and Ti. The SEM analyses of raw mould powder showed different granular particle sizes while pores and glassy/crystalline structure were seen for normal and abnormal behaviour at casting. CaF2, CaSiO3, and Na2CaSi3O9 were revealed as the mineralogical phases. There was a modified crystalline phase present in the abnormal behaviours at casting due to pickup of other oxides.
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    DEVELOPMENT AND ASSESSMENT OF PARTICLE REINFORCED ABRASIVE GRINDING DISCS FROM LOCALLY SOURCED MATERIALS
    (Journal of Chemical Technology and Metallurgy, 2024-09-09) Jamiu Kolawole Odusote; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Timothy Adekanye; Adeiza Avidime Samuel; Chinedum Oji
    Management of waste materials is a serious concern to researchers and scientists. Waste materials cause health and environmental hazards. Hence, they should be properly managed. The aim of this study is to develop a grinding disc using agricultural wastes (palm kernel shell and snail shell), granite, aluminium oxide, and polyester resin. The particles of snail shell, palm kernel shell, aluminium oxide (abrasive) and granite (friction modifier) were measured in percentages varying between 8 - 29 wt. % and were mixed with 27 wt. % polyester resin (binder), 3 wt. % methyl ethyl ketone peroxide (hardener) and 3 wt. % cobalt naphthalene (accelerator) to produce a grinding disc. The micrograph, hardness, wear rate, and water absorption tests were carried out on the grinding disc samples. The result showed that the composition with the highest palm kernel shell particle content (29 wt. %) had the best values for hardness and wear resistance, making it the most suitable material for grinding discs. The environmentally-friendly palm kernel shell-based discs could be used for soft metals, wood grinding and finishing processes.
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    Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model
    (Hindawi, 2023-02-06) Moses Oluwatobi Fajobi; Olumuyiwa A. Lasode; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Ayokunle O. Balogun; Prabhu Paramasivam
    One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.
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    Machine Learning Model for the Evaluation of Biomethane Potential Based on the Biochemical Composition of Biomass
    (BioEnergy Research, 2023-09-30) Adekunle Akanni Adeleke; Jude A. Okolie; Chukwuma C. Ogbaga; Peter Pelumi Ikubanni; Patrick U. Okoye; Olugbenga Akande
    Biomethane potential (BMP) is often used to evaluate the biogas potential during anaerobic digestion (AD). However, BMP tests are complex and time-consuming. Therefore, the present study presents a hybrid model in machine learning (ML) for evaluating and predicting BMP based on biomass biochemical composition. Generative adversarial network (GAN) is combined with different ML models to model and predict BMP for different biomass materials. The models were trained on 64 experimental datasets (original datasets) and a combination of a GAN and the original datasets (augmented datasets). The gradient boost regression (GBR) model performed very well on the training set with both datasets compared to the support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). RF and GBR models performed very well when trained with the combined GAN and original datasets, with RF slightly outperforming GBR on the test datasets (R2 score of 0.9106 vs. 0.9177). This indicates that the models benefit from the additional data generated by the GAN. The GBR model trained with the GAN and original datasets combined outperformed the RF model on the test set, with an R2 score of 0.9177 vs. 0.9106. A comparison between three different hyperparameter optimization methods (grid search, particle swarm optimization, and Bayesian optimization) showed that the grid search optimized model offers a balanced performance with an R2 score of 0.9994 and a marginal improvement on the test set with an R2 of 0.9213. Feature analysis results demonstrate that cellulose has the most influence on BMP.
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    Mechanical properties and stress distribution in aluminium 6063 extrudates processed by equal channel angular extrusion technique
    (Taylor and Francis, 2023-08-08) T.M. Azeez; L. O. Mudashiru; Tesleem B. Asafa; Adekunle Akanni Adeleke; Adeyinka Sikiru Yusuff; Peter Pelumi Ikubanni
    This study investigated the effect of temperature and load on the mechanical properties and stress distribution in ECAE processed Al6063. Samples were extruded at temperatures of 350, 425 and 500 under applied loads of 1000, 1100 and 1200 kN and ram speed of 5 mm/s. Hardness and tensile strength of all the extrudates were measured with Rockwell hardness tester and universal testing machine, respectively. Stress distribution in the extrudates was simulated with qform software under different extended applied loads and temperatures. Experimental results showed that billet temperature influenced tensile strength and hardness more significantly than applied load. The grains in the extrudates also became more refined as billet temperature increased. Simulation results indicated that more uniform stress distribution with lower magnitude was observed in the extrudates with increased billet temperature. The hardness and tensile strength of the extrudates as revealed through the uniform stress distribution were enhanced.
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    Metal Matrix Reinforcements - Fabrication, Applications, and Properties: A Review
    (IEEE, 2023-11-01) Adeiza Avidime Samuel; Ishiaka Shaibu Arudi; Sambo Markus; Adekunle Akanni Adeleke; Temitayo Samson Ogedengbe; Seun Jesuloluwa; Mazeedah Aladejana; Osagie O. Jahswill; Samuel Lawrence Chijioke; Ayodeji Emmanuel Adeyelu
    Metal matrix composites (MMCs) have emerged as a transformational class of materials, demonstrating exceptional promise for increasing mechanical, thermal, and specialized characteristics across varied applications. This study gives a detailed assessment of current improvements in metal matrix reinforcements, concentrating on their effects, production processes, and applications. Particulate, fiber, and whisker reinforcements are examined for their influence on mechanical, thermal, and specialized characteristics. Various production processes, including solid-state fabrication and liquid-state fabrication, are examined. The evaluation focuses on applications in the aircraft and automotive industries. Addressing obstacles and future prospects in scalable manufacturing and innovative reinforcements, the article gives insights into the growing environment of metal matrix composites over the past years.
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    Green Corrosion Inhibition Practices
    (IEEE, 2023-11-01) Petrus Nzerem; Adekunle Akanni Adeleke; Ayuba Salihu; Esther Nneka Anosike-Francis; Adeiza Avidime Samuel; Adebayo Isaac Olosho; India Chiazokam Odezugo; Jachimike Agbo Samuel; Peter Pelumi Ikubanni
    Corrosion poses significant challenges for industries worldwide, causing financial losses, safety risks, and environmental issues. To address these concerns, there has been a shift towards sustainable corrosion prevention techniques. This review presents a summary of corrosion, corrosion inhibitors, and specifically focuses on green corrosion inhibitors. It discusses relevant literature exploring various types of green inhibitors to mitigate corrosion. Additionally, it highlights recent progressions in the application of green corrosion inhibitors. The insights presented in this paper enable researchers, engineers, and business experts to adopt sustainable corrosion prevention solutions.
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    Influence of sawdust ash on the microstructural and physicomechanical properties of stir‑cast Al6063/SDA matrix composite
    (The International Journal of Advanced Manufacturing Technology, 2023-02-02) Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Jamiu Kolawole Odusote; Boluwatife B. Olujimi; Jude A. Okolie
    Mechanical, physical, and corrosion properties of pure aluminum cannot meet the requirements of the modern industries. This has led to increase in demand for aluminum alloys and aluminum matrix composites with enhanced properties. These properties make them suitable for most applications. This article analyzes the physicomechanical and microstructural properties of stir cast Al6063 alloy matrix reinforced with different weight fractions (2, 4, and 6 wt.%) of sawdust ash (SDA). The density, porosity, hardness, tensile strength, and impact strength of the unreinforced alloy and developed composite samples were evaluated while microstructural analysis was also carried out. The results showed reduced density values with increased SDA contents while percentage porosity ranged between 1.56 and 2.23%. The hardness (88.3–106.93 BHN) and tensile strength (112.13–132.71 MPa) of the composites were 21.09% and 18.35% better than those of Al6063 alloy. However, the impact strengths (45.48–35.51 J) of the composites were lower when compared to the unreinforced Al6063 alloy with a reduction of 21.92%. Microstructural images showed evenly distributed reinforcement particles within the matrix, while the XRD analysis also revealed the presence of different intermetallic phases in the composite samples. The micrographs of the composites showed plastic deformation during straining. The findings from the study indicate that SDA particulates incorporated into alloy matrix influenced the properties with increased hardness and tensile strength and reduced impact strength. Hence, the aluminum matrix composites will be suitable for use in lightweight engineering applications.
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    Formed Coke from Coal and Plastic: A Review
    (IEEE, 2023-02-28) Adekunle Akanni Adeleke; Petrus Nzerem; Ayuba Salihu; Jamiu Kolawole Odusote; Adebayo Isaac Olosho; Peter Pelumi Ikubanni; Yazeed Abubakar Mohammed; Samuel Chijoke Lawrence; Temitayo Samson Ogedengbe; Adeiza Avidime Samuel