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基于人工神经网络的预测柴油发动机性能和使用废弃生物柴油的排放
1Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt.
Scientific reports
|September 20, 2024
概括
废食用油 (WCO) 可以转化为生物柴油,为化石燃料提供可持续的替代品. 生物柴油混合物减少了碳排放和烟雾等有害排放,但可能会影响发动机性能,尽管人工神经网络 (ANN) 模型可以准确预测这些效应.
科学领域:
- 环境科学 环境科学
- 机械工程 机械工程
- 化学工程是化学工程的重要组成部分.
背景情况:
- 化石燃料的燃烧释放有害排放,推动了对可持续替代品的搜索.
- 生物柴油,特别是来自废油 (WCO),是一个有前途的可再生燃料选择.
- 了解基于WCO的生物柴油混合物对发动机性能和排放的影响至关重要.
研究的目的:
- 调查WCO-甲基生物柴油混合物对柴油发动机性能和排放的影响.
- 开发和验证人工神经网络 (ANN) 模型,以根据混合比率和发动机转速预测发动机性能和排放.
- 评估WCO生物柴油作为传统柴油燃料的替代品的可行性.
主要方法:
- WCO被转化为甲基,并以不同百分比的比例与生物柴油混合 (25%,50%,75%,100%).
- 测量了发动机性能 (制动功率,热效率,特定燃料消耗,空气-燃料比,体积效率) 和排放 (CO,HC,烟雾,NOx).
- 开发了一个人工神经网络 (ANN) 模型来预测性能和排放参数.
主要成果:
- 与柴油相比,生物柴油混合物显示车功率和热效率降低.
- 生物柴油混合物的特定燃料消耗增加.
- 生物柴油可显著减少CO,HC和烟雾排放.
- 氧化 (NOx) 排放量在较高负载和特定发动机转速时增加.
- 在所有参数上,ANN模型表现出高预测精度 (r > 0.99,R2 > 0.98).
结论:
- 基于WCO的生物柴油是柴油发动机技术上可行的替代燃料.
- 生物柴油可以大幅减少某些有害排放.
- ANN建模提供了一个有效的工具,用于预测WCO-生物柴油混合物的发动机性能和排放.
- 进一步的研究可能会优化混合物,以平衡性能和排放效益.
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