Browsing by Author "Moses Oluwatobi Fajobi"

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Effect of biomass co-digestion and application of artificial intelligence in biogas production: A review
(Taylor and Francis, 2022-06-20) Moses Oluwatobi Fajobi; Olumuyiwa A. Lasode; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Ayokunle Olubusayo Balogun
Energy is an essential bedrock, which plays a high impact role in the running of domestic and industrial activities. Most energy used for these activities is majorly from conventional sources, which after combustion result in ecological imbalance, climatic affray, health hazards, and degradation of natural resources. Therefore, the quest for eco-friendly energy has made researchers to investigate on alternative energy, such as biogas. This review study presents a comprehensive analysis of various biomass used for biogas production considering the effects that co-digestion of these materials has on biogas yield, as well as the technology involved. It further evaluated the applicability of artificial intelligence for modeling and optimization of the anaerobic digestion process including the blend ratios, process parameters and so on. These indices determine the percentage methane yield from biomaterial. The review effort revealed that methane content of biomaterials digested without pre-treatment varies from 3.6 ± 0.7 to 443.55 ± 13.68NL kg 1 VS while the yield from biomaterials pre-treated using various methods varies from 301.38 mL CH4/g VSadded to 0.73–5.87 L/week. Anaerobic digestion of the blends of cow dung, mango pulp, and Chromolaena odorata was reportedly necessary, as information is scantily available on it. The modeling of the resulting experimental data using different machine learning techniques such as an adaptive-neuro-fuzzy inference system and ANFIS for predicting biogas yield is a major information gathered in this study. The AI models reviewed have high correlation factors ranging from 0.8700 to 0.9998. This information gathered in this paper will motivate the production of useful fuel to complement the existing energy sources while offering a near-term and practical means for reduction of environmental pollution.
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.
Simulation Technology in Renewable Energy Generation: A Review
(International Conference on Multidisciplinary Engineering and Applied Sciences (ICMEAS), 2023-11-01) Adekunle Akanni Adeleke; Petrus Nzerem; Ayuba Salihu; Esther Nneka Anosike-Francis; Adebayo Isaac Olosho; Kpabep Kerein Kalenebari; Yuguda Abdullahi Muhammad; Waliyi Adekola Adeleke; Moses Oluwatobi Fajobi
The escalating energy consumption rates and the alarming environmental impacts associated with fossil fuel usage have driven global attention towards alternative energy sources. While nuclear power has emerged as one such alternative, concerns about past reactor accidents and the health effects of radiation release have limited its widespread adoption. Renewable energy, on the other hand, offers a promising solution with minimal environmental harm compared to nuclear power. However, the intermittent nature of renewable energy sources and their inability to consistently supply power present significant challenges for nations aiming to harness these abundant resources. To address these challenges, the integration of simulation technology into energy generation processes has proven instrumental. By employing simulation tools, it becomes possible to identify, control, and even eliminate factors that may hinder energy generation and efficiency. Furthermore, simulation technology enables accurate predictions of the expected energy output from renewable sources. This paper presents a comprehensive review of the recent advancements and applications of simulation technology in renewable energy generation. It elucidates how simulation technology has been successfully integrated into renewable energy systems and discusses its potential to enhance the efficiency of renewable energy generation.