混合XGBoost-RF-MLP模型和PSO优化CI发动机的性能和排放,使用废生物柴油混合物
M S Gad1, M Sami Soliman2, Emad B Helal2,3
1Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt. msm19@fayoum.edu.eg.
Scientific reports
|December 12, 2025
概括
废食用油 (WCO) 生物柴油提供了一个可持续的替代燃料. 一个混合机器学习模型优化了发动机条件,减少了排放和燃料消耗,以实现高效使用.
科学领域:
- 可再生能源工程可再生能源工程
- 计算流体动力学的流体动力学.
- 机器学习应用 机器学习应用
背景情况:
- 废食用油 (WCO) 是生物柴油生产的可持续原料.
- 生物柴油混合物 (B25-B100) 根据ASTM标准准备和验证.
- 优化替代燃料的发动机性能和排放对于环境可持续性至关重要.
研究的目的:
- 开发一个混合机器学习框架,与粒子群优化 (PSO) 集成,用于预测使用WCO生物柴油的发动机性能和排放.
- 确定最佳的发动机运行条件 (负载和生物柴油混合百分比),以平衡燃油效率和减排.
- 评估混合模型与单个机器学习模型的预测准确度.
主要方法:
- 转化化的过程,将WCO转化为甲基 (生物柴油).
- 组合XGBoost,随机森林和MLP的堆叠组合混合模型的开发,使用基模型预测作为元特征.
- 将混合动力模型与PSO集成,以找到最佳的发动机参数.
- 对生物柴油混合物的实验验证和发动机性能/排放测量.
主要成果:
- 纯WCO生物柴油 (B100) 显著降低了CO (25%),HC (43%) 和烟雾 (45%) 的排放,但与充满载荷的柴油相比,增加了NOx (23%) 和制动特定燃油消耗 (22%).
- 与单个MLP,RF和XGBoost模型相比,混合堆叠组合模型实现了更高的预测准确性 (MSE~10−7).
- 公共服务局确定了在86%的发动机负载和26%的生物柴油混合物 (B26) 的最佳运行条件,以最大限度地发挥平衡的健身功能.
结论:
- 拟议的混合机器学习和PSO框架为优化WCO生物柴油发动机性能提供了强大而准确的方法.
- 世界机关生物柴油是一个可行的替代燃料,特别是在优化混合物中使用时,如B26,平衡减排和燃油效率.
- 这项研究表明了先进的计算建模的潜力,以加速可持续生物燃料的采用.
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