机器学习预测用于提高发动机性能和排放使用氧化物纳米添加剂在鱼生物柴油中的预测
Murugu Nachippan1, P Pathmanabhan1, Beemkumar Nagappan2
1Department of Automobile Engineering, Easwari Engineering College, Chennai, Tamilnadu, India.
Scientific reports
|October 21, 2025
概括
将氧化纳米颗粒添加到鱼生物柴油混合物中,可以提高发动机性能,减少一氧化碳 (CO),碳化合物 (HC) 和氧化 (NOx) 等有害排放. 这种可持续的方法可以提高燃油经济性和燃烧效率.
科学领域:
- 绿色化学与可持续能源
- 纳米技术在燃料中的应用.
- 燃烧工程 燃烧工程
背景情况:
- 对化石燃料耗尽和排放的日益担忧推动了对更清洁的替代燃料的需求.
- 来自麻油等非食用来源的生物柴油提供了一个可持续的选择,但需要提高性能.
- 提高燃烧效率和减少排放是广泛采用生物柴油的关键.
研究的目的:
- 为了研究氧化纳米添加剂对压缩点火发动机中B30子生物柴油混合物的影响.
- 为了提高燃烧性能,减少点火延迟,并减轻排放 (CO,HC,NOx).
- 应用机器学习来优化发动机参数和减少排放.
主要方法:
- 从麻油中合成生物柴油通过转化.
- 在B30生物柴油混合物中加入氧化纳米颗粒.
- 在不同负载下对Kirloskar柴油发动机进行发动机性能测试.
- 机器学习算法的应用 (随机森林,XGBoost) 用于数据分析.
主要成果:
- 与传统柴油相比,纳米添加剂注入的生物柴油显示出更好的燃油经济性,原子化,蒸发和燃烧效率.
- 观察到车特定燃油消耗 (BSFC) 的降低和CO,HC和NOx排放的显著缓解.
- XGBoost算法被确定为最准确的引擎优化预测工具.
结论:
- 氧化纳米添加剂有效地提高了子生物柴油混合物的性能.
- 这种整合符合绿色化学原则,提供可持续的能源解决方案.
- 先进的数据分析,包括机器学习,可以优化发动机性能并最大限度地减少对环境的影响.
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