使用人工智能方法,可靠的预测框架用于以生物燃料的导热率
Walid Abdelfattah1, Ramdevsinh Jhala2, Ramachandran Thulasiram3
1Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia.
Scientific reports
|October 15, 2025
概括
这项研究开发了先进的机器学习模型,以准确预测生物燃料的液体导热率 (LTC). 这些模型为优化生物燃料应用提供了更好的概括性和准确性.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 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:
- 乙生物燃料是主要的可再生能源.
- 准确的液体导热率 (LTC) 数据对于优化生物燃料能源系统至关重要.
- 现有的LTC预测模型在不同的生物燃料和条件中缺乏通用性.
研究的目的:
- 开发强大的机器学习模型,用于预测生物燃料的LTC.
- 为了解决现有的经验相关性的局限性.
- 为生物燃料的热性估计提供准确和可通用的工具.
主要方法:
- 使用了1641个实验LTC测量的数据集,用于22种基生物燃料.
- 使用的机器学习算法:支持矢量机 (SVM),决策树 (DT) 和遗传编程 (GP).
- 基于温度,压力,关键热力学特性和摩尔重量的预测LTC.
主要成果:
- 在未见的数据上,SVM模型实现了高精度 (R2=99.53%,MAPE=0.60%).
- 总理模型提供了一个明确的,可解释的相关性 (R2=97.94%,MAPE=1.16%).
- 两种模型都与实验数据强烈一致,并将温度确定为影响LTC的主要因素.
结论:
- 开发的智能模型比现有的实证相关性提供了更高的准确性和更广泛的适用性.
- 这些模型增强了对生物燃料热性质的预测能力.
- 这些发现支持将乙生物燃料有效地整合到能源系统中.
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