Department of Aeronautic Engineering

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Browsing Department of Aeronautic Engineering by Author "Aderibigbe A. A."

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    An Evaluation of Predictive Modeling and Error Analysis for Physicochemical Characteristics of Gasoline-Bioethanol Blends in a Gasoline Engine
    (American Journal of Engineering Research (AJER), 2024-12-07) Ogunsola A. D.; Alajede A. M.; Olafimihan E. O.; Sangotayo E. O.; Aderibigbe A. A.; Sulaiman A. O.
    The integration of bioethanol into gasoline blends has gained significant attention for improving engine performance and reducing environmental impacts. This study evaluated predictive modeling and error analysis of the physicochemical characteristics of gasoline-bioethanol blends (E0, E5, E10, and E15) in a four-stroke gasoline engine. The importance of optimizing blend ratios for efficiency and sustainability underscores the relevance of this research. The physicochemical characteristics, including cetane number, viscosity, density, carbon residue, and heating values, were measured using a data logger and analyzed across blend ratios ranging from 0% to 15%. Profiles of these characteristics were created to assess their relationship with bioethanol content. Predictive models were developed using SAS software, with R-squared and RMSE values evaluated as performance metrics to assess model accuracy and fitness. The results indicate that the coefficient of performance (COP) demonstrated higher sensitivity to bioethanol content in the 0-10% range, stabilizing about10%, suggesting an optimal blend ratio of around 10%. Brake power efficiency decreased linearly with increasing bioethanol content due to the lower energy density of higher ethanol blends. However, the enhanced combustion efficiency of bioethanol compensated for some efficiency losses. Notably, the E10 blend achieved the highest brake power of 391.65 W, Model evaluation revealed robust predictive capabilities, with R-squared and RMSE values for COP at 0.964 and 0.585, respectively, and for braking thermal efficiency at 0.996 and 0.133, respectively. These metrics confirm the model's high accuracy and reliability in predicting engine performance characteristics across blend ratios. Future research should explore the impact of higher ethanol concentrations on engine durability and further optimization of bioethanol-gasoline formulations for enhanced sustainability and effectiveness.