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    Efficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and diesel
    (Pergamon-Elsevier Science Ltd, 2023) Venkatesh, S. Naveen; Sugumaran, V; Thangavel, Venugopal; Balaji, P. Arun; Vijayaragavan, Mathanraj; Subramanian, Balaji; Josephin, J. S. Femilda
    Emission created by combustion of fossil fuels are a major concern of the world for the past few decades. The stringent emission norms have impacted the automobile manufacturers to work on exhaust emissions and its impact. This research focused on using machine learning regression models to evaluate the efficacy of experimental results for a dual fuel compression ignition (CI) engine operating on hydrogen and diesel. In the present study, engine emissions were estimated using 29 regression algorithms. A total of 5 input data namely, concentration of hydrogen, engine load, diesel intake, speed and equivalence ratio were considered in the study to estimate various emissions like oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbon (HC) and smoke. Correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error were adopted as the performance metrics in the present study. Amongst the algorithms considered, pace regression, radial basis function regressor, multilayer perceptron regressor and alternating model tree produced the highest correlation coefficient of 0.9985, 0.8958, 0.9950 and 0.9256 in estimating the engine emissions like CO2, smoke, NOx and HC respectively. Additionally, an attempt was made to establish an individual algorithm that can estimate all the emissions was identified as multilayer perceptron regressor with correlation coefficient values of 0.9977 (CO2), 0.9950 (NOx), 0.8501(smoke) and 0.8731(HC) respectively. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Experimental investigation of features of CI engine fueled with blends of camphor oil with biomass waste simarouba glauca oil
    (Taylor & Francis Inc, 2024) Gurusamy, Manikandaraja; Vijayaragavan, Mathanraj; Varuvel, Edwin Geo
    This research article examines engine performance characteristics using three different volumetric blending ratios of high-viscosity Simarouba glauca seed oil and low-viscosity camphor oil: 30% Simarouba glauca oil with 70% camphor oil (S30C70), 50% Simarouba glauca oil with 50% camphor oil (S50C50), and 70% Simarouba glauca oil with 30% camphor oil (S70C30). At full load, the thermal efficiency of S30C70 was found to be 8.18, 5.64, and 4.09% higher than that of S70C30, S50C50, and diesel fuel. In comparison to S70C30, S50C50, and diesel, the energy usage for brakes was determined to be 7.54, 5.34, and 3.64% lower. At high loading circumstances, S30C70 emits 57% less CO than the basic fuel value. Similar to the basic fuel, smoke and hydrocarbon emissions are trending downward. In comparison to base diesel, NO emission for the S30C70 fuel mix was about 20.33% higher under heavier loading situations. The maximum in-cylinder peak pressure and rate of pressure increase are exhibited in S30C70, which has a lower cetane number. The S30C70 fuel blend offers higher fuel exergy, relative efficiency, sustainability index, and exergy efficiency due to its low viscosity. The S30C70 fuel blend was found to have lower entropy than all other combinations tested. When a higher volume of camphor oil is added to the blended fuel, the performance characteristics of the diesel engine increase significantly.