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    A comparative analysis of advanced machine learning models for the prediction of combustion, emission and performance characteristics using endoscopic combustion flame image of a pine oil–gasoline fuelled spark ignition engine
    (Elsevier Ltd., 2024) Godwin, D. Jesu; Varuvel, Edwin Geo; Jesu Martin, M. Leenus; Jasmine R, Anita; Josephin JS, Femilda
    This research focuses on using machine learning to predict the spark ignition engine's combustion, performance, and emission parameters with bio-fuel blends such as pine oil blend, which significantly diminishes the environmental impact of traditional fuels, reduces the limitations of repeated engine experimentation and addresses the nonlinearities in engine test results contributing to sustainable cleaner fuel and energy solutions. The models used were Ensemble Decision Tree Bagging, Ensemble Least Squares Boosting, Gaussian Process Regression and Support Vector Machine Regression, with good generalization ability. Brake Specific Fuel Consumption data from the test engine trials and endoscopic image flame area data after spark timing at different crank angles (320, 400, 480, 560, and 640 after Spark Timing) were fed into the machine-learning models as predictors. The response variables were Brake thermal efficiency, Unburnt Hydrocarbons, Carbon monoxide, Carbon dioxide, Oxides of nitrogen, maximum In-cylinder pressure, and maximum heat release rate. The bootstrap technique was used to generate numerous datasets from the experimental data for data-driven model training and tested using both interpolative and extrapolative data. The experimental and predicted values for all these algorithms were subjected to repeated hyperparameter optimization trials and the best machine learning method was identified using the performance and error metrics. The Ensemble Least Squares Boost model showed the overall best correlation (R2) in the range of 0.97–0.99 for gasoline and pine oil PN20 blend for the predicted versus experimental engine parameters. The root-mean-squared error (RMSE) at maximum load ranged between 0.0086 and 0.3044 for gasoline and 0.0049–0.2046 for the Pine oil PN20 fuel blend respectively. Therefore, employing an Ensemble Least Squares Boosting machine learning framework can effectively predict the characteristics of gasoline engines using pine oil and blends. This approach serves as a virtual engine model, efficiently overcoming the limitations and complexities inherent in conventional engine experiments. © 2024 Elsevier Ltd
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    Experimental investigation of ammonia gas as hydrogen carrier in prunus amygdalus dulcis oil fueled compression ignition engine
    (Elsevier Ltd, 2024) Sonthalia, Ankit; Geo Varuvel, Edwin; Subramanian, Thiyagarajan; Josephin JS, Femilda; Almoallim, Hesham S.; Pugazhendhi, Arivalagan
    The present study aims to utilize ammonia gas as a hydrogen carrier with prunus amygdalus dulcis (sweet almond oil)-fueled single-cylinder compression ignition (CI) engine. Due to the high viscosity of sweet almond oil, a transesterification procedure was used to convert it to biodiesel. The diesel fuel was completely replaced with biodiesel to assess the performance, emission, and combustion characteristics of the CI engine running at a constant speed of 1500 rpm under different load conditions. Poor performance and combustion were exhibited with biodiesel in comparison to diesel. Lower brake thermal efficiency with higher fuel consumption and lower nitrous oxides (NOx) emissions were observed with biodiesel in comparison to diesel. While hydrocarbon (HC), carbon monoxide (CO), and smoke emissions were higher with biodiesel, to further improve the performance, hydrogen gas was introduced at different flow rates (10–30 LPM). Hydrogen improved the brake thermal efficiency with reduced carbon emissions. At maximum load condition, with 30 LPM hydrogen brake thermal efficiency is improved by 15 %. However, NOx emissions were higher with hydrogen induction compared to base fuels at all load conditions. NOx emissions were increased from 1274 ppm with biodiesel to 1451 ppm with 30 LPM hydrogen addition at maximum load. Although hydrogen is one of the most promising techniques to improve the performance of biodiesel, its higher NOx emissions and safety aspects make its practical application questionable. Hence, ammonia gas was used as a hydrogen carrier, and tests were conducted in dual fuel mode with biodiesel at different flow rates. It is observed that performance parameters in ammonia dual fuel mode are on par with those of biodiesel with reduced carbon and NOx emissions. Hence, ammonia can be considered a viable option to replace hydrogen as its carrier to meet global energy demands and also for its safer use. © 2024 Elsevier Ltd