在使用棕生物柴油燃料的柴油发动机中,实验和ML辅助的注射时间和EGR的优化
Jayashri N Nair1, T Srinivasa Rao2, M B S Sreekara Reddy3
1Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana 500090 India jayashri@vnrvjiet.in.
RSC advances
|February 2, 2026
概括
棕甲基乙 (POME) 生物柴油混合物优化柴油发动机的性能. 数据驱动的建模,特别是XGBoost,准确地预测最佳设置以提高效率和减少NOx排放,支持可持续运输.
科学领域:
- 可持续能源解决方案 可持续能源解决方案
- 可再生燃料是可再生的燃料.
- 燃烧工程是指燃烧的工程.
背景情况:
- 越来越多的环境问题和排放法规需要更清洁的燃烧和可持续的能源.
- 棕甲基乙 (POME) 是一个有前途的生物柴油,具有可再生来源和有利的燃料性能.
- 柴油发动机需要优化,以实现更清洁,更可持续的应用.
研究的目的:
- 为了研究使用POME混合物的柴油发动机的性能,燃烧和排放特性.
- 为了优化POME混合物的注射时间和废气循环 (EGR).
- 为了比较响应表面方法 (RSM) 与机器学习模型 (XGBoost,随机森林) 的预测准确度.
主要方法:
- 使用基于可取性的多目标优化框架进行的实验性发动机测试.
- 响应表面方法 (RSM) 用于实验设计和分析.
- 对RSM与极端梯度提升 (XGBoost) 和随机森林机器学习模型进行比较.
主要成果:
- 确定为POME20的最佳发动机设置,注射时间为23°bTDC和EGR.
- 优化条件导致车热效率 (BTE) 提高,氧化物排放量显著减少.
- 在RSM和随机森林中,XGBoost表现出更高的预测准确度,由更高的R2和更低的MSE和MAPE证明.
结论:
- 可再生燃料 (POME) 的协同潜力利用和数据驱动的建模,以优化柴油发动机.
- 这些发现为可持续运输提供了更清洁,高效的燃烧系统的途径.
- XGBoost显示为预测使用替代燃料发动机性能的一种强大工具.
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