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    Development of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil - Hydrogen on PCCI operation
    (Pergamon-Elsevier Science Ltd, 2023) Varuvel, Edwin Geo; Seetharaman, Sathyanarayanan; Bai, Femilda Josephin Joseph Shobana; Devarajan, Yuvarajan; Balasubramanian, Dhinesh
    Identifying the suitable alternative fuel and optimum blend concentration for diesel engine combustion is critical as most biodiesel emits excess smoke and has a lower thermal efficiency due to its high viscosity and carbon residue. In the previous work, rubber seed oil was tested in a single-cylinder diesel engine, and its performance and emission results were compared with those of pure diesel, an RSO-diesel (70:30 by volume) blend, RSOmethyl ester, RSO-diethyl ether, RSO-ethanol, and RSO-hydrogen in a dual fuel operation. The testing was performed at a constant speed of 1500 rpm, with the engine loads varying at 25% step intervals. Results showed that smoke and nitrogen oxides were significantly reduced for RSO, and engine performance was enhanced when RSO was operated with hydrogen and diethyl ether in dual fuel mode. In this study, the experimental results were employed to develop an artificial neural network and response surface methodology model. Brake thermal efficiency, rate of pressure rise, carbon monoxide, hydrocarbon, oxides of nitrogen, and smoke were predicted using response surface methodology and artificial neural network. Though artificial neural network produced the best R2 values (0.87264-0.99929), mean absolute percentage error was relatively lesser in response surface methodology. Thus, the authors conclude that response surface methodology is the best suitable artificial intelligence tool to optimize the engine for accomplishing desired responses.
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    A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engine
    (Elsevier Sci Ltd, 2023) Bai, Femilda Josephin Joseph Shobana
    To lower the carbon dioxide and other emissions from a single cylinder common rail direct injection (CRDI) engine, it is important to investigate the combinations of several methods. Lemon peel oil (LPO) and camphor oil (CMO), which are low carbon content biofuels, are the methods that are used and are induced by hydrogen in the intake manifold and zeolite-based after-treatment system. At full load, the injection of hydrogen decreased CO2 and smoke emissions by 39.7% and 49%, respectively. Even though the NO emission increases with hydrogen induction, it was decreased with zeolite after-treatment system. Predictions can be made using machine learning techniques, which will reduce the amount of time and money needed for engine trials. This work focuses on the prediction of engine emissions like CO2, Nitrogen Oxides (NO), Smoke, Brake Thermal Efficiency (BTE), Hydrocarbons (HC) using the ensemble learning techniques. The predictions are made using the ensemble learning methods like Extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (LGBM), CatBoost, and Random Forest (RF). The CatBoost model has produced high accuracy predictions which was followed by XGBoost, RF and LightGBM models. The predicted and actual values are compared each other and the performance of the algorithms were analysed using the evaluation metrics like R-Square(R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
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    Production of Raphanus Sativus Biodiesel and Its Performance Assessment in a Thermal Barrier-Coated Agriculture Sector Diesel Engine
    (Wiley-V C H Verlag Gmbh, 2023) Ravikumar, Venkatachalam; Senthilkumar, Duraisamy; Vellaiyan, Suresh; Saravanan, Chidambaram Ganapathy; Vikneswaran, Malaiperumal; Bai, Femilda Josephin Joseph Shobana; Varuvel, Edwin Geo
    Herein, in order to estimate the optimized process specifications, an empirical study is done for biodiesel production, performance of standard and coated engine, combustion, and discharge aspects of Raphanus sativus (radish) biodiesel. Optimization process parameters of biodiesel production are done using the response surface method. The importance of this study is that optimized biodiesel production is used to improve the biodiesel properties and fatty acid content to find suitable vegetable oil as well as a new coating material for sustainable development of the country in the field of agriculture and ecological conditions. The mechanism used in this research work is a coating for internal combustion engine components done with partially stabilized zirconia, aluminum-20% silicon carbide (Al-20%SiC), and titanium dioxide that acts as a ceramic composite settled above the coating that has a thickness of 450 mu m by the technique called air plasma spray for test purpose. To increase the performance of an engine and reduce the emission particles like smoke density, carbon monoxide, and hydrocarbon except NOx, the partially stabilized zirconia, aluminum-20% silicon carbide (Al-20%SiC) coated engine is used under various compositions of radish biodiesel and clean diesel. To reduce NOx, the engine operation is carried out along with the TiO2 coated combustion chamber steam, and water injection technique is added to overcome the NOx formation. From this study, the resulting factor shows that adding 25% radish biodiesel (B25) and 75% clean diesel shows a reasonable reduction in emissions with comparatively better performance and combustion in the engine under consideration.