混合生物柴油的人工神经网络模型开发
Sanika R Raut1, Sashwat Kumar Singh1, Supriyo Kumar Mondal1
1Department of Chemical Engineering, Institute of Chemical Technology Marathwada Campus Jalna, Maharashtra- 431203, Jalna, India.
Environmental science and pollution research international
|January 24, 2026
概括
研究人员开发了一个人工神经网络 (ANN) 模型来预测生物燃料排放,减少了昂贵的物理测试. 这种AI模型准确地预测生物柴油及其混合物的氧化 (NOx),氧化碳 (COx) 和碳化合物 (HC).
科学领域:
- 可持续能源研究 可持续能源研究
- 计算化学和工程 计算化学和工程
- 环境科学环境科学
背景情况:
- 寻找可持续生物燃料对于减少环境影响至关重要.
- 对新生物燃料的实验测试是昂贵的,资源密集的.
- 预测建模可以克服实验生物燃料研究的局限性.
研究的目的:
- 开发一个人工神经网络 (ANN) 模型来预测生物柴油及其混合物的排放.
- 为了将燃料组成,性能和发动机条件与排放输出 (NOx,COx,HC) 相关联起来.
- 为低排放燃料的发现提供了物理测试的经济有效的替代方案.
主要方法:
- 使用了一个包含424个生物柴油变体的综合数据集,其中有17个输入变量.
- 优化了一个ANN模型,具有60/20/20数据划分,0.005学习率,批量大小64,25个隐藏的神经元和200个时代.
- 采用了缩放结合梯度 (SCG) 算法,将其性能与莱文伯格-马奎特 (LM) 算法进行比较.
主要成果:
- 获得了0.977的高整体确定系数 (R2),平均平方误差 (MSE) 约为10−7.
- 在基于原料的预测中表现出强大的预测准确性 (R2=0.980,MSE=2.34×10−7).
- 显示了混合生物柴油 (R2=0.911,MSE=5.29×10−5) 的良好的预测能力,尽管数据变化.
结论:
- 开发的ANN模型有效地预测了生物柴油及其混合物的排放.
- 这种人工智能方法提供了一种低成本,高效的方法来发现新的低排放生物燃料.
- 该模型可以加速向更可持续和环保的燃料选择过渡.
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