Optimizing Bio-Diesel Combustion and Emissions Through Artificial Intelligence Techniques.

Abstract: The global transition toward sustainable energy has accelerated interest in alternative fuels such as biodiesel, valued for its renewability and environmental advantages. This study investigates the performance and emission characteristics of biodiesel in comparison to conventional diesel, emphasizing the application of Artificial Intelligence (AI) for predictive modeling. Machine learning techniques—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests—are utilized to model key engine parameters such as brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and emissions including carbon monoxide (CO), and nitrogen oxides (NOx). The findings reveal that while biodiesel blends tend to produce slightly lower power output, they achieve notable reductions in CO and hydrocarbon emissions, with a corresponding increase in NOx levels. The AI models demonstrate strong predictive capabilities across diverse operating conditions, facilitating the optimization of fuel blends and engine settings. This integration of AI into biodiesel research presents a promising pathway for enhancing the environmental and operational performance of internal combustion engines.

Biodiesel, AI, ANN, SVM, Random Forests

1. Knothe, G. (2005). Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters. Fuel Processing Technology, 86(10), 1059–1070. [Google Scholar] [Crossref]

2. Demirbas, A. (2009). Progress and recent trends in biodiesel fuels. Energy Conversion and Management, 50(1), 14–34. [Google Scholar] [Crossref]

3. Rakopoulos, D. C., et al. (2006). Performance and emissions of a bus engine using blends of biodiesel and diesel fuel. Energy Conversion and Management, 47(4), 388–404. [Google Scholar] [Crossref]

4. Sayin, C., et al. (2010). Performance and emission characteristics of a diesel engine using soybean oil biodiesel. Renewable Energy, 35(3), 539–543. [Google Scholar] [Crossref]

5. Gupta, H., et al. (2015). ANN-based prediction of engine performance and emissions for biodiesel blends. Renewable and Sustainable Energy Reviews, 52, 1105–1115. [Google Scholar] [Crossref]

6. Kumar, P., et al. (2017). Prediction and optimization of CI engine performance using SVM and genetic algorithm for biodiesel blends. Applied Energy, 204, 206–215. [Google Scholar] [Crossref]

7. Al-Durra, A., & Omar, W. (2019). Real-time modeling and control of biodiesel engines using ANN. IEEE Transactions on Industrial Informatics, 15(4), 2030–2041. [Google Scholar] [Crossref]

8. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall. [Google Scholar] [Crossref]

9. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer. [Google Scholar] [Crossref]

10. Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22. [Google Scholar] [Crossref]

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