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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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    Impact of hydrogen-assisted combustion in a toroidal re-entrant combustion chamber powered by rapeseed oil/waste cooking oil biodiesel
    (Elsevier ltd, 2025) Thiagarajan, S.; Seetharaman, Sathyanarayanan; Lokesh, R.; Prasanth, G.; Karthick, B.; Bai, Femilda Josephin Joseph Shobana; Ali Alharbi, Sulaiman; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    This study investigates the performance and emission characteristics of biodiesel blends of rapeseed oil and waste cooking oil in a toroidal re-entrant combustion chamber (TCC) compression ignition engine. Hydrogen was allowed into the engine in dual fuel mode to enhance the engine performance. The presence of oxygen in the biodiesel and hydrogen induction increased the peak pressure and heat release rate significantly for all the engine loads. At a peak load of 4.88 kW, the maximum brake thermal efficiency (BTE) of 31.77% was recorded for the D70R20W10 (diesel 70%, rapeseed oil 20%, waste cooking oil 10%) biodiesel blend. Furthermore, hydrogen induction enhanced the BTE by around 3%. The biodiesel blending substantially lowered the emissions of unburnt hydrocarbons, carbon monoxide, and smoke opacity. Additionally, hydrogen supplementation facilitated 5-10% carbon monoxide reduction over biodiesel blends by enabling more complete oxidation. However, higher temperatures generated due to complete combustion resulted in more NOx formation. Thus, the authors propose that biodiesel blends of rapeseed oil, waste cooking oil, and diesel with hydrogen induction improve engine performance and reduce regulated emissions.
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    Influence of hydrogen-assisted combustion in compression ignition engines fueled with fuel blends of pine oil and waste cooking oil biodiesel using toroidal combustion chamber
    (Pergamon-elsevier science LTD, 2024) Thiagarajan, S.; Damodaran, Ajith; Seetharaman, Sathyanarayanan; Varuvel, Edwin Geo
    In this research study, fuel blends of pine oil and waste cooking oil biodiesel (P/WCO) were examined for combustion analysis, engine performance, and emission characteristics tests. Improved combustion and engine performance were achieved using a toroidal re-entrant combustion chamber (TCC) and hydrogen supply as a dual fuel. The combustion behavior of fuel blends and hydrogen fuel was examined by varying the engine load at a constant speed. The results revealed that significant reduction in the specific fuel consumption for rich pine oil. Thus, a slightly lesser energy share from the fuel blends and a higher energy share from the hydrogen fuel was required for the engine to maintain the same brake power. Lower viscosity, higher flash point, and presence of oxygen in the pine oil enhance the combustion rate and brake thermal efficiency. Furthermore, hydrogen induction in the engine improves the flame velocity. A lesser crevice volume in the TCC can trap the unburnt fuel which can further increase the combustion efficiency. Thus, rich pine oil with the support of hydrogen induction in TCC causes advanced ignition, improved combustion, and more heat release during the combustion. The investigation results revealed that hydrogen induction increases the heat release rate and peak pressure by 7.4% and 2.67%, respectively. Similarly, the maximum of 24.55% increase in BTE and 18.22% reduction in BSFC was observed due to a constant 10 lpm hydrogen induction. Furthermore, hydrogen fuel significantly reduces the emissions such as CO, HC, CO2, and smoke. However, more NOx was generated due to more heat release rate during combustion. Thus, pine oil and waste cooking oil biodiesel blends with the support of hydrogen induction in TCC improve the engine performance and mitigate the toxic pollutants and can be a suitable alternative to diesel fuel.
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    Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology
    (Pergamon-elsevier science, 2024) Seetharaman, Sathyanarayanan; Suresh, S.; Shivaranjani, R. S.; Dhamodaran, Gopinath; Bai, Femilda Josephin Joseph Shobana; Alharbi, Sulaiman Ali; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R 2 between 0.99426 and 0.9998 was observed by ANN whereas R 2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN 's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.