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    Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
    (Edp Sciences S A, 2024) Cinarer, Goekalp; Yesilyurt, Murat Kadir; Agbulut, Uemit; Yilbasi, Zeki; Kilic, Kazim
    This study applies three different artificial intelligence algorithms (Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) to estimate CO2 emissions in Turkiye's transportation sector. The input parameters considered are Energy consumption (ENERGY), Vehicle Kilometers (VK), POPulation (POP), Year (Y), and Gross Domestic Product Per Capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO2 emissions using statistical indicators (R-2, RMSE, MSE, and MAE). Across all scenarios and algorithms, R-2 values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful in estimating CO2 emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer the potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO2 emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector's environmental impact. It also contributes to the understanding of AI-assisted CO2 emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.
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    Wastes to energy: Improving the poor properties of waste tire pyrolysis oil with waste cooking oil methyl ester and waste fusel alcohol-A detailed assessment on the combustion, emission, and performance characteristics of a CI engine
    (Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Yesilyurt, Murat Kadir; Saridemir, Suat
    The core objective of this study is to pull back the worsened combustion, emission, and performance characteristics of a CI engine fuelled with waste tire pyrolysis oil diesel fuel blends. Four fuels are tested in the experiments. These are (1) 100% diesel fuel, (2) 20% waste tire pyrolysis oil ? 80% diesel fuel, (3) 10% pyrolysis oil and 80% diesel fuel containing 10% waste biodiesel, and finally, (4) 10% waste tire pyrolysis oil and 80% diesel fuel containing 10% waste fusel oil. The tests are performed at a constant engine speed of 2400 rpm, and varying engine loads from 3 to 12 Nm with intervals of 3 Nm. In the results, it is noticed that using of waste tire pyrolysis oil diesel fuel blend is reducing the brake thermal efficiency down to 9.13% for waste tire pyrolysis oil diesel fuel blends, however, this reduction is being pulled back by 7.51%, and 3.82% with the addition of waste biodiesel, and fusel oil, respectively as compared to that of diesel fuel. On the other hand, waste tire pyrolysis oil diesel fuel blend increased the brake specific fuel consumption by 21.78%, however, this increase is being pulled back by 8.89%, and 12.57% for waste biodiesel, and fusel oil, respectively. The increase in carbon monoxide for waste tire pyrolysis oil-diesel fuel is 7.09% in comparison with that of diesel fuel. However, with the addition of biofuels, carbon monoxide is being dropped by 7.69% for waste biodiesel, and 19.23% for fusel oil due to the high oxygen contents of waste biofuels. Moreover, waste tire pyrolysis oil-diesel fuel blend is increasing nitrogen oxide by 7.09%, but this increase by 4.64% with the addition of waste biodiesel. On the other hand, the addition of fusel oil is converting the increasing trend of nitrogen oxide into a reduction of 3.09% owing to fusel oil?s water content. As a consequence, this research is proving that the waste biofuels are able to improve poor combustion, emission, and performance characteristics of binary waste tire pyrolysis oil diesel blend with the doping of waste biofuels, and suggesting ternary blends rather than waste tire pyrolysis oil alone for diesel engines. Moreover, it is noticed that burning waste products is a very effective tool for both waste management and alternate to fossil fuels. ? 2021 Elsevier Ltd. All rights reserved.