关于评估生物柴油的表面张力
Farzaneh Rezaei1, Mohammad Reza Arab Juneghani1, Mostafa Keshavarz Moraveji2
1Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|August 6, 2024
概括
人工智能模型准确地预测生物柴油表面张力,为实验室测试提供更快的替代方案. 数据处理组方法 (GMDH) 模型在估计这一关键物理性质方面表现出卓越的准确性.
科学领域:
- 能源科学 能源科学
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 不断增长的能源需求和化石燃料不可再生性推动了对生物柴油等替代燃料的研究.
- 准确测量生物柴油的物理性质对于其有效利用至关重要.
- 传统的实验室方法用于物业测量是昂贵和耗时的.
研究的目的:
- 开发准确的数值模型来预测生物柴油表面张力.
- 为了比较这个预测任务的数据处理组方法 (GMDH) 和基因表达编程 (GEP) 的性能.
- 提供简单的数学方程,用于估计在各种温度和分子重量的表面张力.
主要方法:
- 利用了两个人工智能白盒模型:数据处理的组方法 (GMDH) 和基因表达编程 (GEP).
- 收集并随机将78个实验室数据点分为培训 (80%) 和测试 (20%) 集.
- 输入参数包括脂肪酸乙烯的质量分数和温度 (T);被按分子量 (Mw1,Mw2,Mw3) 分类.
主要成果:
- 两种GMDH和GEP模型都使用简单的数学方程成功预测了生物柴油表面张力.
- GMDH模型的准确性更高,平均绝对相对误差为0.97,与GEP的1.89相比.
- 灵敏度分析表明,温度对生物柴油表面张力具有最显著的反向影响;五个数据点被确定为异常值.
结论:
- GMDH和GEP提供了高效和准确的AI驱动的替代方案来实验确定生物柴油表面张力.
- 开发的GMDH模型提供了一个可靠的工具,用于在不同条件下预测表面张力.
- 温度是影响生物柴油表面张力的关键因素,观察到的是相反的关系.
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