精确预测密度,粘度和声音的速度在水性亚利法生物基聚胺溶液使用数据驱动的建模
Suleiman Ibrahim Mohammad1,2, Hamza Abu Owida3, Asokan Vasudevan4,5
1Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Zarqa, Jordan.
Scientific reports
|January 8, 2026
概括
这项研究开发了一种机器学习框架,用于预测形生物基聚胺 (ABP) 溶液的热物理性质. 机器学习模型准确地预测了声音的密度,粘度和速度,为实验提供了有效的替代方案.
科学领域:
- 物理化学 物理化学
- 计算化学计算化学
- 生物化学工程 生物化学工程
背景情况:
- 阿利法生物基聚氨酸 (ABP) 在生物化学和工业过程中至关重要.
- 准确预测热物理性质 (粘度,声速,密度) 对于这些应用是必不可少的.
- 确定这些属性的现有实验方法可能是耗时和昂贵的.
研究的目的:
- 开发和验证一个数据驱动的框架,用于预测水性ABP溶液的热物理性质.
- 评估各种机器学习模型在预测密度,动态粘度和声音速度方面的性能.
- 用灵敏度分析来确定影响这些属性的关键因素.
主要方法:
- 使用198个实验测量数据点进行培训和验证.
- 实施并比较了五种机器学习模型:K-最近邻居 (KNN),集体学习 (EL),卷积神经网络 (CNN),自适应提升 (AdaBoost) 和多层感知器人工神经网络 (MLP-ANN).
- 用于超参数优化和灵敏度分析的蒙特卡洛方法的合模拟化 (CSA).
主要成果:
- 在密度和粘度方面,MLP-ANN实现了最高的预测准确度.
- 在预测声音速度方面,EL表现出了卓越的表现.
- 灵敏度分析确定ABP类型和摩尔质量是对热物理性质最有影响的因素.
结论:
- 机器学习为模拟ABP解决方案的热物理性质提供了一种可靠,高效和具有成本效益的方法.
- 开发的框架可以显著帮助理解和优化涉及这些化合物的过程.
- 这项研究强调了计算方法在化学研究中补充或取代传统实验技术的潜力.
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