Research Articles in Mechanical Engineering

Permanent URI for this collectionhttps://repository.nileuniversity.edu.ng/handle/123456789/130

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Author: search.filters.author.Prabhu ParamasivamSubject: search.filters.subject.Engineering (General). Civil engineering (General)

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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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    Electrochemical investigation of the corrosion susceptibility of hybrid reinforced Al6063 with SiC and PKSA in 1.0 M sulfuric acid environment
    (Wiley, 2023-07-24) Peter Pelumi Ikubanni; Makanjuola Oki; Adekunle Akanni Adeleke; Akintunde Sunday Onaolapo; Prabhu Paramasivam
    The recycling of agro-waste as complementary reinforcements has received significant recognition in the development of aluminium matrix composites. Hence, this study examines the corrosion behavior of Al6063 reinforced with hybrid SiC/PKSA (palm kernel shell ash) particles. Through various ratios of SiC and PKSA particles in Al6063 alloy, composites were fabricated by double stir casting. Samples were cut and metallographically prepared for 1 M H 2 SO 4 solution corrosion experiments. Gravimetric, potentiodynamic polarization and electrochemical impedance spectroscopic analyses were employed. The composites corroded initially at relatively high rates, gradually declining during long immersion times in the acidic solution. The intersection of reinforcements at the general surfaces of the composites where flawed oxide skins predominate acted as active sites for corrosion initiation. From potentiodynamic polarization studies, the corrosion currents increased with time for all specimens, with A9 being 1075.65 μA/cm 2 at 72 h as against 857.99 μA/cm 2 at 24 h of measurement. The corrosion potentials for all the specimens hovered around −654.00 to −647.22 mV. Bode plots revealed similar electrochemical reactions over all the substrates’ surfaces. The relative corrosion resistance by the specimen depends on the oxide films’ nature as the cathodic interfacial reinforcements dropped off into the acidic environment.