Browsing by Author "Olumuyiwa A. Lasode"

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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.
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 Paswan
Torrefaction 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 importance
Evaluation of Non-Isothermal Kinetic Parameters for Pyrolysis of Teak Wood using Model-Fitting Techniques
(TRENDS IN SCIENCES, 2021-12-21) Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Jamiu Kolawole Odusote; Thomas Orhadahwe; Olumuyiwa A. Lasode; Samuel Adegoke; Olanrewaju Adesina
Teak wood is one of the prominently used raw material in the construction industry, thus contributing extremely to the biomass waste available in Nigeria. These wastes are usually used for energy generation that requires upgrade into better fuel before application. Hence, the present study evaluates the non-isothermal kinetic parameters for pyrolysis of teak wood using model-fitting techniques. Teak wood dust was subjected to proximate, ultimate and calorific value analyses based on different ASTM standards. The thermal degradation and decomposition behaviour of the teak wood dust was examined using a thermogravimetric analyzer. Pulverized teak (6.5 mg) was heated from 30 to 800 ºC at varying heating rates (5, 10 and 15 ºC) in an environment where 100 mL/min of nitrogen gas was charged in continuously to maintain an inert condition. Avrami-Erofeev, Ginstling-Broushtein (GB) and Mampel models were used to evaluate the kinetic parameters of the pyrolysis of teak wood dust. The teak wood dust contained 7.25 % moisture, 79.26 % volatile matter (VM), 1.74 % ash and 11.75 % fixed carbon. The calorific value of the wood dust was 18.72 MJ/kg. The results of the thermogravimetric analyses depicted that heating rate has no effect on weight loss during the reactive drying zone. However, as the thermal treatment progressed into the active pyrolysis and passive pyrolysis zones, the weight loss decreased with increase in heating rate. The devolatilization parameters also increased with heating rates except for the maximum conversion. The results of the kinetic parameters evaluation revealed that the GB model was best fit to evaluate the kinetic parameters of teak in the active pyrolysis zone while GB and Mampel models were considered most appropriate for the evaluation of the kinetic parameters in the passive pyrolysis zone. Model-fitting method has the capacity to capture a wide range of fractional conversion at a glance
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.