Experimental assessment and multi-response optimization of diesel engine performance and emission characteristics fuelled with Aegle marmelos seed cake pyrolysis oil-diesel blends using Grey relational analysis coupled principal component analysis

This research focuses on the detailed experimental assessment of compression ignition (CI) engine behavior fuelled with Aegle marmelos (AM) seed cake pyrolysis oil blends. The study on effects of engine performance and emission a characteristic was designed using L25 orthogonal array (OA). These multi-objectives were normalized through gray relational analysis (GRA). Likewise, the principal component analysis (PCA) was performed to assess the weighting values respective to every performance and emission characteristics. The variability induced by using the input process parameters was allocated using analysis of variance (ANOVA). Hence, GRA-coupled PCA were employed to determine the optimal combination of CI engine control factors. The greater combination of engine characteristics levels were selected with F5 and W5. The higher brake thermal efficiency (BTE) have been obtained for F20 fuel as 22.01% at peak engine load, which is 11.43% for diesel. At peak load condition, F20 fuel emits 14.99% lower HC and 18.52% lower CO as compared to diesel fuel. The improved engine performance and emission characters can be attained by setting the optimal engine parameter combination as F20 blend at full engine load condition. The validation experiments show an improved average engine performance of 67.36% and average lower emission of 64.99% with the composite desirability of 0.8458.