Department of Mechanical Engineering

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Has files: YesSubject: search.filters.subject.Biomedical Engineering

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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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    Synthesis and Characterization of Eggshell-derived Hydroxyapatite for Dental Implant Applications
    (EDP Sciences, 2023-01-01) Jamiu Kolawole Odusote; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Peter Omoniyi; Tien-Chien Jen; G. Odedele; Jude A. Okolie; Esther Titilayo Akinlabi
    Hydroxyapatite (HAp) production from eggshells for dental implant purposes involved a novel approach utilizing a wet chemical precipitation technique. The eggshells, finely ground to a size below 250 μm, underwent calcination at a high temperature of 900°C for 2 hours. This thermal treatment facilitated the conversion of calcium carbonate into calcium oxide (CaO) while eliminating any organic components in the eggshell. To initiate the synthesis of HAp, a solution comprising 0.6 M phosphoric acid was added to the CaO dispersed in water. The resulting mixture was allowed to undergo aging at different time intervals ranging from 0 to 24 hours, promoting the formation of HAp. Subsequently, the HAp particles were oven-dried at 100°C for 2 hours to remove residual moisture. Finally, the dried particles were sintered at 1200°C in a muffle furnace to achieve the desired properties for dental implant applications. XRD peaks at 25, 33, 40, and 50° confirm the synthesized material as HAp. Vibrational modes of phosphate (PO43-), hydroxyl (OH-), and carbonate (CO32-) groups indicate carbonated HAp. Synthesized HAp holds potential for biomedical applications.
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    Combustion characteristics of fuel briquettes made from charcoal particles and sawdust agglomerates
    (Elsevier, 2019-10-14) H. A. Ajimotokan; A.O. Ehindero; Kabiru Sulaiman AJAO; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Y. L. Shuaib-Babata
    The combustion characteristics of fuel briquettes made from Idigbo (Terminalia ivorensis) charcoal particles, pinewood (Pinus caribaea) sawdust and their agglomerates using gelatinized cassava peels were investigated. The charcoal particles and pine sawdust were blended in the mixing ratios of 90:10, 80:20, 70:30, 60:40, and 50:50, respectively and vice-versa. More so, briquettes were produced from pure charcoal particles and pine sawdust separately for the purpose of comparison with the blended briquettes. The gelatinized binder was 5% of the total briquettes weight. The briquettes were produced using a pressure of 5 MPa with a dwelling time of 5 min in a hydraulic briquetting machine. Proximate, elemental compositions and heating value analyses were carried out on the raw charcoal, sawdust, cassava peel, and their briquettes. The results showed that variations in the mixing ratios of the bio-residues had significant effects on all the properties investigated. An increase in the charcoal particles led to an increase in the fixed carbon content and heating value of the briquettes. Conversely, higher pine sawdust content in the briquette resulted in higher volatile matter content and lower heating value. The briquette made from pure charcoal particles had the highest heating value (24.9 MJ/kg) and ash content (6.0%). Its carbon, hydrogen, and oxygen contents were in the range of 44.6–50.1%, 5.1–5.6% and 34.4–41.5%, respectively. The proximate analysis, elemental composition analysis, and heating values of the produced fuel briquettes depicted that they have better combustion properties when compared to the raw charcoal, pine dust, and cassava peel. Thus, the produced briquettes would serve as good fuel for domestic and industrial applications.