Department of Mechanical Engineering
Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/129
Browse
96 results
Search Results
Item 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 VololonirinaThe 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 applicationsItem 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. MalathiThis 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.Item Chemical and Mechanical Properties of Reinforcing Steel Bars from Local Steel Plants(Springer, 2019-06-06) Jamiu Kolawole Odusote; Wasiu Shittu; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Olumide AdeyemoSteel bars are important engineering materials for structural application. In Nigeria, due to incessant building collapse occurrences, it is important to further investigate some of the mechanical and chemical properties of reinforcing steel bars produced from scrap metals in order to ascertain their compliance with the required standard. Three diameters (10, 12 and 16 mm) of the reinforcing steel bars were chosen from each of the eight steel plants (A–H). Chemical composition analyses and mechanical tests (yield strength, ultimate tensile strength and percentage elongation) were performed using optical emission spectrometer and Instron Satec Series 600DX universal testing machine, respectively. Hardness values of the samples were obtained by conversion of tensile strength based on existing correlation. The results showed that carbon contents, hardness values, yield and ultimate tensile strengths of some of the steel bars were found to be higher than the BS4449, NIS and ASTM A706 standards. The steel bar samples were also found to possess good ductility with samples from steel plants C and D. By observation, all the 12 mm steel bars from steel plants A to H met the required ASTM and BS4449 standards except samples from plant G. This study revealed that most of the investigated reinforcing steel bars have reasonable yield strength, ultimate tensile strength, ductility and hardness properties when compared with the relevant local and international standards. Therefore, they are suitable for structural applications where strength and ductility will be of paramount interestItem Inhibition efficiency of gold nanoparticles on corrosion of mild steel, stainless steel and aluminium in 1M HCl solution(Elsevier, 2021-01-01) Jamiu Kolawole Odusote; Adeolu Adesoji Adediran; R.A. Yahya; Adekunle Akanni Adeleke; J.G. Oseni; J.M. Abdul; Tesleem B. Asafa; S.A. AdedayoIn this study, the influence of gold nanoparticles (AuNPs) on corrosion behavior of mild steel, aluminium and stainless steel in 1.0 M HCl was investigated. The nanoparticles were previously characterized using FTIR, UV–Vis and TEM. Five concentrations of AuNPs solution (0 µg/ml, 5 µg/ml, 10 µg/ml, 15 µg/ml, 20 µg/ml) were added to 1M HCl. The corrosion rates of the metal samples and inhibition efficiency of the nanoparticles were analyzed using gravimetric (weight loss) and potentiodynamic polarization techniques. After 2000 h of exposure, gravimetric study showed that weight loss was reduced by ∼75% translating to ∼85% reduction in corrosion rate for the solution containing 20 µg/ml of AuNPs. The equivalent inhibition efficiency was 88%, 98% and 96% for aluminium, mild steel and stainless steel, respectively. Furthermore, potentiodynamic polarization results showed that the presence of AuNPs modified the mechanism of anodic dissolution by the formation of adsorption layer on the surface of the metal samples. These results indicated that AuNPs can be incorporated into existing inhibitors towards minimizing corrosion rate.Item Essential basics on biomass torrefaction, densification and utilization(Wiley, 2020-09-24) Adekunle Akanni Adeleke; Jamiu Kolawole Odusote; Peter Pelumi Ikubanni; Olumuyiwa A. Lasode; Madhurai Malathi; Dayanand PaswanTorrefaction and densification are crucial steps in upgrading biomass as feed-stock for energy generation and metallurgical applications. This paperattempts to discuss essential basics on biomass torrefaction and densification,which can propel developing nation to take full advantage of them. The mostpromising clean energy sources that have found applications in various areasare biomass materials, that is, both the lignocellulosic and non-lignocellulosi c.However, high moisture contents, low energy density, hydrophilic nature, poorstorage and handling properties are the major drawbacks limiting its useful-ness. Therefore, torrefaction as one of the major thermal pre-treatment pro-cesses to upgrade biomass in terms of improved energy density, hydrophobic,moisture content and grindability has been discussed. The influence of temper-ature, residence time, particle sizes and gas flow rates on the properties of tor-refied biomass has also been discussed. The advantages and disadvantages ofvarious torrefaction technologies have also been highlighted. The possibleareas of application of torrefied biomass especially densification into pelletsand briquettes alongside the equipment required for it have been reviewed inthis paper. The torrefied biomass can be deployed in the metallurgical indus-tries as reducing agent in the development of sponge iron from iron ores ofvarious grade including lean ones. The information gathered in this paperfrom peer-reviewed articles will reduce the burden of seeking to understandthe preliminaries of torrefaction process and its importanceItem Electrochemical Studies of the Corrosion Behavior of Al/SiC/PKSA Hybrid Composites in 3.5% NaCl Solution(MDPI, 2022-09-30) Peter Pelumi Ikubanni; Makanjuola Oki; Adekunle Akanni Adeleke; Olanrewaju Adesina; Peter Omoniyi; Esther AkinlabiThe corrosion behavior of metal matrix composites (MMCs) is accelerated by the inclusion of reinforcements. Hence, this study investigates the corrosion behavior of MMCs produced from Al 6063 matrix alloy with reinforcement particulates of silicon carbide (SiC) and palm kernel shell ash (PKSA) inclusion at different mix ratios. The MMCs were synthesized using the double stir casting technique. The corrosion behaviors of the composites in NaCl solutions were studied via gravimetric analysis and electrochemical measurements. The gravimetric analysis showed fluctuating dissolution rate of the samples in NaCl solution to indicate flawed film as well as corrosion product formation over the surface of the specimens. The observed corrosion mechanism of the samples was general and pitting corrosion. The presence of reinforcements within the Al6063 matrix acted as active sites for corrosion initiation. The range of values for Ecorr and Icorr obtained in 3.5% NaCl at 24 h was between −220.62 and −899.46 mV and between 5.45 and 40.87 µA/cm2, respectively, while at 72 h, the Ecorr values ranged from 255.88 to −887.28 mV, and the Icorr ranged from 7.19 to 16.85 µA/cm2. The Nyquist and Bode plots revealed the electrochemical corrosion behavior of the samples under investigation, with predominant reactions on the surface of the samples linked to charge transfer processes. The relative resistance to corrosion of the samples depends on the thin oxide film formed on the surface of the samples.Item 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 OjiManagement 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.Item 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 ParamasivamOne 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.Item Assessment of Suitability of Nigerian Made Steel Bars for Structural Applications(The Journal of the Association of Professional Engineers of Trinidad and Tobago, 2016-10-02) Abdul Ganiyu F. Alabi; Akintunde O. Ayoade; Jamiu Kolawole Odusote; Adekunle Akanni AdelekeThe mechanical properties of selected reinforcing steel bars produced from two rolling mills in Osun State, Nigeria were studied. An optical emission spectrometer was used for chemical composition analysis while the tensile test was carried out using a Universal Testing Machine. Izod v-notched was used for impact test, while the hardness values were obtained from Brinnel hardness tester. Scanning Electron Microscope was used for the fractured surface fractography. The Ultimate Tensile Strengths (UTS) of all the samples are higher than BSS4449:2005+A2:2005 standard and are also in close proximity to A707M-15 standard. Samples A12, B10 and B12 possessed higher yield strengths than samples A16 and B16 but lower than those of BSS4449:2005+A2:2005 and A707M-15 standards. Ductile property of the samples doubled the recommended Nst-65-Mn standard values while the hardness and ductility properties are higher than the recommended A707M-15 and BS4449 standards. The results showed that the investigated reinforcing bar samples possessed reasonably high strength and ductility when compared with available standards. Consequently, these bars would be suitable for structural applications where strength and ductility are critical properties. They would also be used in steel reinforcement applications that would require continuous and repetitive loading such as in buildings and bridges.Item 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 AkandeBiomethane 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.