Role of hydrogen-enrichment on performance and emission characteristics of a diesel engine fuelled with metal oxide nanoparticles added biodiesel/diesel blends:A combined neuro Fuzzy-Gaussian Mixture Model analysis.

Increasing environmental concerns and the need for sustainable energy sources have driven the researchers into alternative fuels. The study employs advanced statistical methods, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS)-Gaussian Mixture Model (GMM) and k-Means Cluster Analysis. By integrating these techniques, the research aims to identify patterns and trends that lead to optimized fuel formulations. The performance metrics and emission parameters are outcomes parameters. ANFIS prediction models identified the Gaussian membership function as the most accurate, prompting its integration with GMM. The study demonstrates that the addition of Aluminium oxide nanoparticles significantly enhances the BTE, while reducing Hydrocarbon (HC) emissions. The optimal input combination was found to be Load is 100.00 %, Blend percentage is 45.71 %, NPC is 27.14, Hydrogen supply is 8.57 for which ideal outcomes are BTE 35.14 %, BSEC 1.07, HC 80.71 ppm, and NOx 128.57 ppm. • Addition of Aluminium oxide enhance the performance characteristics of biodiesel. • Hydrogen infusion was found to further improve combustion efficiency. • The GMM outperformed k-means clustering in accurately capturing performance outcomes. • ANFIS prediction models identified the Gaussian membership function as most accurate. [ABSTRACT FROM AUTHOR]