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基于响应面法对氧化铜纳米颗粒增强麻花生物柴油/柴油燃料的CI发动机燃烧进行研究以提高性能和降低排放

Investigation on CuO nanoparticle enhanced mahua biodiesel/diesel fuelled CI engine combustion for improved performance and emission abetted by response surface methodology.

作者信息

Muniyappan Sinnappadass, Krishnaiah Ravi

机构信息

School of Mechanical Engineering, VIT University, Vellore, 632014, Tamil Nadu, India.

出版信息

Sci Rep. 2024 Nov 6;14(1):26882. doi: 10.1038/s41598-024-77271-3.

DOI:10.1038/s41598-024-77271-3
PMID:39505918
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11542028/
Abstract

In this study, the characteristics of diesel engines were tested with in-house produced mahua biodiesel blended with diesel and copper oxide nanoparticles (CuO NP) catalyst. The preliminary investigation used mahua biodiesel-diesel blends (M10, M20, and M30) among them M20 outperformed. Further M20 and CuO NP with concentrations of 25, 50, and 75 ppm are studied. Finally, the response surface methodology (RSM) was used to determine the appropriate NP concentration for M20. The findings showed that the blend of M20 with 60 ppm NP at 80% load had the highest desirability (0.9740), and the developed RSM model predicted engine responses with a mean absolute percentage error (MAPE) of 3.0962% to the confirmation test confirming the model's accuracy. The optimized M20NP60 blend demonstrated superior combustion, performance and emission characteristics.

摘要

在本研究中，使用自制的麻疯树生物柴油与柴油以及氧化铜纳米颗粒（CuO NP）催化剂混合，对柴油发动机的特性进行了测试。初步研究使用了麻疯树生物柴油 - 柴油混合燃料（M10、M20和M30），其中M20表现最佳。进一步研究了浓度为25、50和75 ppm的M20与CuO NP。最后，采用响应面法（RSM）来确定M20的合适纳米颗粒浓度。研究结果表明，在80%负荷下，M20与60 ppm纳米颗粒的混合物具有最高的可取性（0.9740），并且所开发的RSM模型预测发动机响应的平均绝对百分比误差（MAPE）为3.0962%，验证试验证实了该模型的准确性。优化后的M20NP60混合物表现出卓越的燃烧、性能和排放特性。

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