Browsing by Author "Oderinde J. O"

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    Investigating impacts of ammonium phosphate on ash yield from co-combustion of sugarcane bagasse and banana leaves
    (Faculty of Engineering and Technology, LAUTECH, Ogbomosho, 2024) Oladosu K. O.; Olawore A. S; Ponle E. A; Adeniran I. S; Oderinde J. O
    High ash yield from the co-combustion of sugarcane bagasse (SGB) and banana leaves (BL) presents significant challenges for efficient biomass combustion in a grate furnace. This study aimed to explore the potential of ammonium phosphate (NH4H2PO4) as an additive to reduce ash yield during the co-combustion of SGB and BL in a muffle furnace. An I-Optimal design of the Combined Methodology (OCD), embedded in Design Expert (version 13.0.5), was employed to design experiments for optimizing ash yield, considering various particle sizes, additive concentrations, and temperatures. The NH4H2PO4 additive was most effective at concentrations between 4% and 7%, beyond which ash yields increased significantly. The optimal composition was determined to be 75% SGB, 20% BL, and 5% NH4H2PO4 at 950°C, resulting in the lowest ash yield of 6.46% and HHV of 22.4MJ/kg. The presence of NH4H2PO4 in the biomass mixture significantly reduced ash yield. The input and output relationship of the biomass additive mixture was modeled using OCD, resulting in R² and adjusted R² values of 0.9825 and 0.9099, respectively, indicating accurate determination of the model coefficients. This study demonstrates that ammonium phosphate additive has great potential in mitigating ash-related problems in biomass combustion.
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    OPTIMIZATION AND MODELLING OF BIO-OIL YIELD FROM THE PYROLYSIS OF JATROPHA CURCAS SEED USING OPTIMAL DESIGN AND ARTIFICIAL NEURAL NETWORKS
    (LAUTECH Journal of Engineering and Technology, 2023) Oladosu K. O; Amoloye T. O; Mustapha K; Oderinde J. O; Babalola S
    Better quality and quantity of pyrolysis products from biomass can be obtained by regulating the input parameters of the pyrolysis process. Pyrolysis of Jatropha Curcas seed mixed with alumina catalyst was carried out in a fixed bed reactor to study the effects of temperature, time, and particle size on bio char and bio-oil yield. The bio-oil yields were optimized using the Optimal Design (OD) under the Combined Methodology of the Design-Expert Software (12.0). The input and output parameters were modeled and validated using Artificial Neural Networks (ANN) based on 40 experimental data generated by the OD. The optimum bio-oil yield of 15.6 wt. % was obtained at 650 °C, 30 min, and 1 mm particle size. The Correlation Coefficient (R2) of the model for the bio-char and bio-oil yield under the OD were 0.998 and 0.996, respectively. The optimized ANN architecture employed the in-built Levenberg-Marquardt training algorithm in MATLAB software. Random division of the data into training, validation and testing sets followed 70:15:15 percentage proportions with 15 hidden layers. This resulted in the minimum Mean Square Error (MSE) of 2.15e-05 and (R2) of 0.96394 for the bio-oil yield. The FTIR spectra indicated that bio-oil contained phenols, esters, and acids compound while its Gas Chromatography analysis showed the presence of pyrrolidine, pyrimidine, and aldehydes. These properties signified the bioenergy and biochemical capabilities of the pyrolytic oil obtained. The prediction accuracy indicates that both the ANN and OD can be deployed for accurate prediction.