Yazar "Martin, M. Leenus Jesu" seçeneğine göre listele

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    Combined effects of various strategies to curtail exhaust emissions in a biomass waste fueled CI engine coupled with SCR system
    (Elsevier B.V., 2021) Praveena, V.; Martin, M. Leenus Jesu; Geo, V. Edwin
    This study is an exhaustive investigation on engine performance and combustion features of a conventional diesel engine charged with biodiesel produced from biomass waste. Grapeseed oil methyl ester resulted in poor thermal efficiency with enormous quantity of harmful emissions. As an effort to reduce the engine pollutants, Grapeseed oil methyl ester was doped with zinc oxide nano particles, engine cylinder shape modification and exhaust gas recirculation method was used. Hydrocarbons, carbon monoxide and smoke emissions reduced considerably, whereas reduction in nitrogen oxide emissions was low. Selective catalytic reduction technique at optimized mass flow rate of aqueous urea solution minimized the nitrogen oxide emissions by 76.9% compared to grapeseed oil methyl ester without compromise in brake thermal efficiency.
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    Prediction of combustion, performance, and emission parameters of ethanol powered spark ignition engine using ensemble Least Squares boosting machine learning algorithms
    (Elsevier Sci Ltd, 2023) Godwin, D. Jesu; Varuvel, Edwin Geo; Martin, M. Leenus Jesu
    This research concentrates on the application of machine learning techniques to predict combustion, performance, and emission parameters in a dual-fuel spark ignition (SI) engine powered by neat gasoline and E20 ethanol dual fuel. The goal is to overcome the limitations posed by repeated engine experiments and nonlinear test results. In order to optimise engine parameters, the research seeks to develop efficient machine learning models with high generalizability and employ an optimisation strategy to determine the optimal engine settings. Input for training and evaluating machine learning algorithms, such as Artificial Neural Networks (ANN), and Ensemble LS Boosting was derived from experimental data from a combustion test engine, which includes Neat gasoline, and Ethanol dual fuel blend E20 at various load conditions. The dataset includes engine combustion, performance, and emission indices such as brake thermal efficiency (BTE), Exhaust Gas Temperature (EGT), Hydrocarbons (HC), Carbon monoxide (CO), Carbon dioxide (CO2), and Nitrogen oxides (NOx), under various operating conditions. Load and brake-specific fuel consumption (BSFC) were training input attributes. Using a comprehensive experimental database of input-output engine parameters, the Artificial Neural Network (ANN) and Ensemble LS Boosting were constructed. The training data points were resampled to generate multiple training datasets for training different models. 50 test samples were used to evaluate the generalisation capability of the machine learning models, while BTE, EGT, CO, CO2, HC and NOx, were the primary parameters subject to prediction. The optimal machine learning method was determined by comparing R-squared (R-2) values, root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Using multiple hyper-parameter tuning iterations, the agreement between actual and predicted values for diverse Ensemble LS Boost algorithms was evaluated. The Ensemble LS Boost model exhibits the maximum level of agreement between predicted and experimental engine parameters across all datasets when compared to the other ANN models. This finding was corroborated by additional research based on test datasets, specifically the test sample interpolation data, which measures generalisation ability. The study also focuses on developing and applying two unique, interactive Simulink models for the Spark Ignition (SI) engine that are tailored for Neat Gasoline and Ethanol E20 test fuels under all loads. The key component of the model-based development technique in MATLAB and Simulink was the incorporation of sophisticated machine learning algorithms, i.e., Ensemble Least-Squares (LS) Boosting, to the model-based development workflow which produced reliable results. Implementing an Ensemble LS Boost machine learning framework is therefore highly recommended as an efficient method for predicting and optimising the combustion, performance, and emission characteristics of dual-fuel gasoline engines utilising Ethanol-based dual-fuel blends.
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    Quantitative assessment of small-bore gasoline direct injection engine for homogenous stoichiometric lean condition with hydrogen addition in dual fuel mode
    (Pergamon-Elsevier Science Ltd, 2024) Stanley, M. Jerome; Varuvel, Edwin Geo; Martin, M. Leenus Jesu
    The global hunt for the sustainable fuel for the energy and transportation has reached the newer dimensions. The advancement in the engine technology makes the viable solution to fit the hydrogen as the working fuel for both CI and SI engines. The physio-chemical chemical properties of hydrogen have the apt fitment for the SI engine. With the advancement in the automotive electronics and precision control of the fuel handling system makes hydrogen a workable option for the replacement for the conventional fuels. In this research hydrogen was inducted into the intake manifold and Gasoline is injected directly into the cylinder of the small-bore Gasoline Direct Injection (GDI) Engine at wide open throttle condition. The intake manifold is slightly modified to accommodate the hydrogen induction system (0-8 Litres Per Minute), where premixed mixture of hydrogen and air is formed in the intake manifold; the equivalence ratio of 1.0, 1.02 and 1.08 was set for the given hydrogen induction. The engine conditions were set as 3000 rpm with five hydrogen addition fractions of 0 %, 1.5 %, 2.8 %, 4.2 % and 5.8 % respectively. The hydrogen induction improves the combustion rate and mean effective pressure; shorten the flame propagation, maximize the heat release rate and peak pressure attainment. Maximum of 16 % increase in the peak pressure is attained for the lambda = 1 for the 5.8 % hydrogen addition fraction. On the emission side, consistent decrease in CO and HC emission; and increase of NO emission, since the mean gas temperature has increased.
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    Synergistic effect of hydrogen and waste lubricating oil on the performance and emissions of a compression ignition engine
    (Pergamon-Elsevier Science Ltd, 2023) Kiran, S.; Martin, M. Leenus Jesu; Sonthalia, Ankit; Varuvel, Edwin Geo
    The demand for energy is increasing every year. For a long time, fossil fuels have been used to satiate this energy demand. However, using hydrocarbon-based fossil fuels has led to an enormous rise of carbon dioxide levels in the atmosphere resulting in global warming. It is therefore necessary to look for alternatives to fossil fuels. The research carried out till date have shown biomass and waste-derived fuels as plausible alternatives to fossil fuels. The biomass feedstock includes jatropha oil, Karanja oil, cottonseed oil, and hemp oil among others and wastes include used cooking oil, used engine oil, used tire and used plastics etc. In this study, the authors aim to explore waste lubrication oil as a fuel for the diesel engine. The used lubrication oil was pyrolyzed and diesel-like fuel with 80% conversion efficiency was obtained. A blend of the fuel and diesel in the ratio of 80:20 on volume basis was prepared. Engine experiments at various load conditions was carried out with the blend. As compared to diesel, a 2% increase in thermal efficiency, 6.3%, 16.1% and 13.6% decrease in smoke, CO and HC emissions & 3.2% and 1.8% increase in NOx and CO2 emission were observed at full load with the blend. With an aim to further improve the engine perfor-mance and reduce the overall emissions from the engine exhaust, a zero-carbon fuel namely gaseous hydrogen was inducted in the intake manifold. The flow rate of hydrogen was varied from 3 to 12 Litres per minute (LPM). As compared to diesel, at maximum hydrogen flow rate the thermal efficiency increased by 12.2%. HC, CO and smoke emissions decreased by 42.4%, 51.6% and 16.8%, whereas NOx emissions increased by 22%. The study shows that the combination of pyrolyzed waste lubricant and hydrogen were found to be suitable as a fuel for an unmodified diesel engine. Such fuel combination can be used for stationary applications such as power backups.& COPY; 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.