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.Ayokunle O. BalogunSubject: 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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    Physico-chemical characterization, thermal decomposition and kinetic modeling of Digitaria sanguinalis under nitrogen and air environments
    (Elsevier, 2021-06-12) Ayokunle O. Balogun; Adekunle Akanni Adeleke ; Samuel Oluwafikayo Adegoke; Armando G. McDonald; Peter Pelumi Ikubanni; Abdulbaset M. Alayat
    The study undertook the thermal degradation of a tropical grass species, Digitaria sanguinalis, in nitrogen (pyrolysis) and air (combustion) atmospheres through thermogravimetric analysis as well as comparative kinetic investigation. The differential (Friedman) and integral (Flynn-Wall-Ozawa and Straink) isoconversional methods in conjunction with the Coats-Redfern method were utilized. This was to obtain the kinetic parameters and also predict the probable reaction mechanisms involved in the decomposition process. Before the thermal and kinetic investigations, the grass was analyzed for its physical, chemical, and structural properties utilizing diverse wet-chemistry and spectroscopic techniques. This research attempt is part of a larger project designed to investigate a couple of local grass species, which are invasive by nature, as potential energy crops for pyrolytic and combustion applications. The grass had a fixed carbon content of 17.85% and a calorific value of 13.7 MJ kg−1. The fatty acids detected were from C12 (lauric acid) to C24 (lignoceric acid), with the three most abundant being palmitic (94 mg/g extract), linoleic (27 mg/g extract), and oleic (19 mg/g extract) acids. The average residual weight in air (25.3%) was relatively less than in nitrogen (38.7%), affirming the higher rate of reaction in an oxidative process (combustion). The activation energy profiles in both atmospheres were markedly different, as shown by the Flynn-Wall-Ozawa technique for a conversion ratio of 0.1–0.2 (nitrogen, 149 kJ/mol; air, 177 kJ/mol) and 0.65–0.8 (nitrogen, 366 kJ/mol; air, 170 kJ/mol). Of all the models tested, the model-fitting technique indicates that the chemical reaction and diffusional models play predominant roles in the thermal decomposition of the grass under investigation. The thermal degradation of Digitaria sanguinalis proceeded mainly as complex multi-step reaction mechanisms. Aside from the potential suitability of the grass species for bioenergy applications and biofuels production, it also demonstrated huge capability for biochemical extraction. Future work will incorporate the kinetic data for the associated thermochemical processes development, and the design and optimization of reactors/combustors.