Department of Mechanical Engineering, Government College of Technology, Coimbatore, 641013, Tamil Nadu, India.
Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Environ Sci Pollut Res Int. 2019 Mar;26(7):6980-7004. doi: 10.1007/s11356-019-04164-8. Epub 2019 Jan 15.
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 L 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 F and W. 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.
本研究侧重于使用 Aegle marmelos（AM）种子饼热解油混合物作为燃料的压缩点火（CI）发动机性能的详细实验评估。使用 L 正交数组（OA）设计了对发动机性能和排放特性影响的研究。这些多目标通过灰色关联分析（GRA）进行归一化。同样，进行主成分分析（PCA）以评估每个性能和排放特性的加权值。使用方差分析（ANOVA）分配由输入工艺参数引起的可变性。因此，使用 GRA 耦合 PCA 来确定 CI 发动机控制因素的最佳组合。使用 F 和 W 选择具有更高发动机特性水平的更大组合。在峰值发动机负荷下，F20 燃料的制动热效率（BTE）更高，达到 22.01%，而柴油为 11.43%。在峰值负载条件下，与柴油相比，F20 燃料的 HC 排放降低了 14.99%，CO 排放降低了 18.52%。通过在全发动机负荷条件下设置最佳发动机参数组合 F20 混合物，可以实现发动机性能和排放特性的改善。验证实验表明，复合理想度为 0.8458 时，发动机性能平均提高 67.36%，排放平均降低 64.99%。
