通过先进的白盒机器学习模拟离子液体粘度
Sajad Kiani1, Fahimeh Hadavimoghaddam2,3, Saeid Atashrouz4
1Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN, UK.
Scientific reports
|April 15, 2024
概括
准确预测离子液体 (IL) 粘度对于工业过程至关重要. 这项研究开发了使用遗传编程和GMDH的机器学习模型,比预测IL粘度的现有方法表现出更高的性能.
科学领域:
- 物理化学 物理化学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 离子液体 (ILs) 在工业中越来越重要,需要准确的模型来确定其物理化学性质.
- 由于复杂的分子间相互作用和缺乏通用理论框架,预测IL粘度具有挑战性.
- 过程优化依赖于可靠的粘度数据,推动了对先进预测模型的需求.
研究的目的:
- 开发和评估用于预测纯离子液体粘度的白盒机器学习模型.
- 将基因编程 (GP) 和数据处理组方法 (GMDH) 模型的性能与现有的理论和实证方法进行比较.
- 确定影响IL粘度的关键参数,并评估开发模型的适用性领域.
主要方法:
- 利用遗传编程 (GP) 和数据处理的群组方法 (GMDH) 作为机器学习技术.
- 通过使用2813个实验粘度值的综合数据集,为45种离子液体在各种温度和压力下开发模型.
- 研究了五,六和七个输入参数的模型,以平衡准确性和公式简单性.
主要成果:
- 使用七个输入的GMDH模型实现了最高的准确性,平均绝对相对偏差 (AARD) 为8.14%,R2为0.98.
- 所有七输入模型都显示出高精度,相比于五和六输入模型,它们提供了更简单的公式.
- 拟议的GMDH和GP模型在预测IL粘度方面明显优于现有的理论和经验模型.
- 统计分析证实了GMDH和GP模型实验数据的高质量和适用性领域.
结论:
- 开发的GMDH和GP模型提供了对离子液体粘度的准确和可靠的预测.
- 这些机器学习方法为耗时且昂贵的实验粘度测量提供了强大的替代方案.
- 温度被确定为影响离子液体粘度的最有影响的因素,正如相关性因子分析所示.
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