Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model Moses Oluwatobi Fajobi, Olumuyiwa Ajani Lasode, Adekunle Akanni Adeleke, Peter Pelumi Ikubanni, Ayokunle Olubusayo Balogun, et al. Journal of Engineering United Kingdom, 2023 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.
Investigation of physicochemical characteristics of selected lignocellulose biomass M. O. Fajobi, O. A. Lasode, A. A. Adeleke, P. P. Ikubanni, A. O. Balogun Scientific Reports, 2022 The beneficial effects of biofuels as components of the worldwide energy supply are unquantifiable because they have versatile applications. However, an adequate understanding of the chemical properties of typical biomass is an integral aspect of maximizing the energy potentials because it is susceptible to biomass behavior during the conversion process, especially anaerobic digestion. Therefore, this study investigated the physicochemical characteristics of selected lignocellulose biomass, namely; cow dung, mango pulp, and Chromolaena odorata of Nigerian origin. The raw biomasses were characterized by proximate, calorific, ultimate, compositional, and microbial (for cow dung only) analyses using ASTM standards and equipment. Raw biomass characterization showed that cow dung, mango pulp, and Chromolaena odorata leaves recorded percentages; fixed carbon, volatile matter, and ash contents in addition to calorific values in the ranges of 6.22–7.25%, 5.02–7.79%, 1.14–1.91,% and 13.77–16.16 MJ/kg, respectively. The ultimate analysis of cow dung, mango pulp and Chromolaena odorata recorded carbon (43.08, 39.98, 41.69%); hydrogen (7.87, 6.74, 9.86%); nitrogen (1.53, 1.34, 1.51%); sulphur (0.46, 0.12, 0.25%) and oxygen (47.06, 51.82, 46.69%), respectively. Compositional analysis of the biomass gave percentages in the range of 7.47–11.37 for hemicellulose, 0.22–6.33 for lignin, and 3.71–12.03 for cellulose, while the microbial analysis of cow dung gave total bacteria counts of 5.78 × 108 and 3.93 × 105 cfu/g on wet and dry bases, respectively, which implied that it was rich in microbial colonies, evidently from the various species found, such as Escherichia coli, Staphylococcus aureus, Bacillus cereus, Pseudomonas aureginosa, Proteus morganii, and Micrococcus spp. In this regard, the physicochemical properties of selected biomass of Nigerian origin were established to conform with those of the literature and thus can be regarded as suitable feedstock for anaerobic digestion resulting in methane-rich biogas products.
Effect of biomass co-digestion and application of artificial intelligence in biogas production: A review Moses Oluwatobi Fajobi, Olumuyiwa Ajani Lasode, Adekunle Akanni Adeleke, Peter Pelumi Ikubanni, Ayokunle Olubusayo Balogun Energy Sources Part A Recovery Utilization and Environmental Effects, 2022 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 ± 13.68 while the yield from biomaterials pre-treated using various methods varies from 301.38 mL /g 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.
Optimization of Densification Parameters for Lean Grade Subbituminous Coal Petroleum and Coal, 2022
Evaluation of non-isothermal kinetic parameters for pyrolysis of teak wood using model-fitting techniques Adekunle Adeleke, Peter Ikubanni, Jamiu Odusote, Thomas Orhadahwe, Olumuyiwa Lasode, et al. Trends in Sciences, 2021 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.
 HIGHLIGHTS
 
 Arrhenius parameters in terms of activation energy and pre-exponential factor for the pyrolysis of teak wood while comparing 4 different model-fitting techniques were obtained
 The α-temperature plot for solid state reaction of teak wood dust was a bell-shape (sigmoidal model)
 The Avrami-Erofeev and SSS models were unable to capture the overlapping multiple reactions that took place simultaneously at the active pyrolysis zone
 Higher energy input is needed for devolatilization of teak wood dust to give 10 - 80 % conversion due to higher activation energy at the active pyrolysis zone
 Ginstling-Broushtein was found to be the best model for evaluating the kinetic parameters at the active pyrolysis zone as it had the highest R2 value
 
 GRAPHICAL ABSTRACT
Ash analyses of bio-coal briquettes produced using blended binder A. A. Adeleke, J. K. Odusote, P. P. Ikubanni, T. A. Orhadahwe, O. A. Lasode, et al. Scientific Reports, 2021 The behaviour of ash of fuel affects its thermal efficiency when in use. The ash analyses of bio-coal briquettes developed from lean grade coal and torrefied woody biomass have received limited intensive study. Therefore, the present study aims at analysing the ashes of briquette made from lean grade coal and torrefied woody biomass using blended coal tar pitch and molasses as the binder. Bio-coal briquettes were produced from coal and torrefied biomass in various hybrid ratios. Ashing of various briquettes was done in a muffle furnace at 850 °C for 3 h. Mineral phases of the ash were identified using an X-ray Diffractometer (XRD), while the mineral oxides were obtained using an X-ray Fluorescence Spectrometer. The AFT700 Furnace was used with its AFT700 software to evaluate the ash fusion temperatures of the ashes. The XRD patterns look similar, and quartz was found to be the dominant mineral phase present in the raw coal and bio-coal briquettes. The SiO2 (57–58%), Al2O3 (19–21%), and Fe2O3 (8–9%) were the major oxides observed in the ashes. The final fusion temperatures of the ashes range from 1300–1350 °C. The compositions of the ashes of the bio-coal briquettes are classified as detrital minerals. It was concluded that the addition of torrefied biomass (≤ $$10\\%)$$ 10 % ) and blended binder ($$\\le $$ ≤ 15%) to coal gave a negligible impact on the ashes of the resultant bio-coal briquettes.
Physical and mechanical characteristics of composite briquette from coal and pretreated wood fines Adekunle Adeleke, Jamiu Odusote, Peter Ikubanni, Olumuyiwa Lasode, Madhurai Malathi, et al. International Journal of Coal Science and Technology, 2021 Melina wood torrefied at 260 °C for 60 min was agglomerated with lean grade coal fines into composite briquettes using pitch as binder. Torrefied biomass (3%–20%) and coal fines (80%–97%) were blended together to produce the composite briquettes under a hydraulic press (28 MPa). The briquettes were cured at 300 °C. Density, water resistance, drop to fracture, impact resistance, and cold crushing strength were evaluated for the composite briquettes. The proximate, ultimate, and calorific value analyses were carried out according to different ASTM standards. Microstructural studies were carried out using scanning electron microscope and electron probe microanalyzer equipped with energy dispersive x-ray. Fourier Transform Infrared Spectrophotometer (FTIR) was used to obtain the functional groups in the raw materials and briquettes. The density of the composite briquettes ranged from 0.92 to 1.31 g/cm3 after curing. Briquettes with < 10% torrefied biomass has good water resistance index (> 95%). The highest cold crushing strength of 4 MPa was obtained for briquettes produced from 97% coal fines and 3% torrefied biomass. The highest drop to fracture (54 times/2 m) and impact resistance index (1350) were obtained for the sample produced from 97% coal and 3% torrefied biomass. The fixed and elemental carbons of the briquettes showed a mild improvement compared to the raw coal. The peaks from FTIR spectra for the briquettes shows the presence of aromatic C=C bonds and phenolic OH group. The composite briquettes with up to 20% torrefied biomass can all be useful as fuel for various applications.
Circular economy: Nigeria perspective Saheed A. Aremu, David O. Olukanni, Olubunmi A. Mokuolu, Olumuyiwa A. Lasode, Michael A. Ahove, et al. Circular Economy Global Perspective, 2019
Convective heat transfer in horizontal elliptic ducts in parallel mode rotation Heat and Technology, 1995
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