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    Biodiesel from Biomass Waste Feedstock Prosopis Juliflora as a Fuel Substitute for Diesel and Enhancement of Its Usability in Diesel Engines Using Decanol
    (Wiley-V C H Verlag Gmbh, 2023) Duraisamy, Boopathi; Velmurugan, Kandasamy; Venkatachalapathy, V. S. Karuppannan; Madheswaran, Dinesh Kumar; Varuvel, Edwin Geo
    Biomass-based biofuel production is a promising solution to the decline of fossil fuels. Prosopis juliflora seed-derived vegetable oil, known as Prosopis juliflora methyl ester (JFME), offers a potential feedstock for biodiesel. To enhance its properties, the addition of Decanol is investigated, a higher-order alcohol similar to Diesel. Experiments are conducted on a 5.2 kW compression ignition (CI) engine using JFME blended with different decanol concentrations (5%, 10%, 15%, and 20%). Fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry analysis confirm its compliance with fuel standards. The findings reveal that the 20% decanol blend (D20) achieves a brake thermal efficiency of 29.9% at full load, with reduced NO, smoke, and hydrocarbon (HC) emissions compared to diesel. D20 shows NO emissions of 1265 ppm, smoke opacity of 53%, and HC emissions of 69 ppm, while diesel records 1320 ppm, 69%, and 75 ppm, respectively. The CO emissions for D20 are 0.359 vol%, slightly higher due to decanol's higher latent heat of evaporation. Moreover, D20 exhibits improved combustion with a higher mass fraction burnt and faster heat release rates. These results indicate the potential of using JFME blended with 20% decanol as an alternative fuel for CI engines, offering higher performance and reduced emissions.
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    Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas
    (Elsevier ltd, 2025) S, Priya; Feenita, C.; Goel, Uday; T, Manoranjitham; Duraisamy, Boopathi; Subramanian, Balaji; Ganeshan, Kavitha; Bai, Femilda Josephin Joseph Shobana; Albeshr, Mohammed F.; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    Internal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of data-driven methodologies in contemporary engine design and operational methods
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    Effect of amyl alcohol addition in a CI engine with Prosopis juliflora oil - an experimental study
    (Taylor and Francis, 2021) Duraisamy, Boopathi; Velmurugan, Kandasamy; Venkatachalapathy, V. S.Karuppannan; Subramanian, Thiyagarajan; Varuvel, Edwin Geo
    This study aims to replace diesel with Prosopis Juliflora seed oil (JPO) in a compression ignition (CI) engine. The high viscosity of JPO promotes inferior performance and combustion. Brake thermal efficiency of JPO is 28.3%, which is less compared to 30.7% for diesel. This also leads to higher brakespecific energy consumption, HC, CO, and smoke emissions. JPO was converted to its biodiesel (Prosopis Juliflora methyl ester) (JPME) through the transesterification process. The physical properties were improved posttransesterification process. Brake thermal efficiency was improved to 29.3% for JPME. Higher NOx emission with reduced HC, CO, and smoke emissions was observed with JPME in comparison with JPO. The test engine employed for the investigations has a single-cylinder configuration with the maximum power of 5.2 kW enabled with water cooling. Furthermore, amyl alcohol was added with JPME in various proportions of 5%, 10%, 15%, and 20% by volume and experiments were conducted. The addition of amyl alcohol in the volume mentioned earlier has improved the thermal efficiency at higher loads; added to this NO and smoke emission were lowered simultaneously for all the loading conditions. Except with the 5% volume of amyl alcohol, HC and CO emissions have increased for all other volume compositions. JPME with 20% volume amyl alcohol exhibits the highest peak pressure and heat release rate. The brake thermal efficiency of JPME + A20 is on par with diesel. NO and smoke were reduced by 7% and 29%, respectively, for JPME + A20 in comparison with diesel. The study shows that the addition of 20% amyl alcohol with JPME has performance and emission characteristics similar to diesel. Further increase in amyl alcohol led to poor cold starting condition and may also lead to knocking. Hence, it was concluded to use only up to 20% of amyl alcohol to avoid any operational complications.