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用机器学习工具预测泡风病学模型参数的预测
Jawad Al-Darweesh1, Murtada Saleh Aljawad1,2, Zeeshan Tariq3
1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals (KFUPM), Dharahan 31261, Saudi Arabia.
ACS omega
|May 13, 2024
概括
这项研究使用机器学习来预测泡液粘度,发现XGBoost模型准确地确定了学模型常数. 这种方法可以降低粘度测量的实验成本.
科学领域:
- 风病学和材料科学 材料科学
- 计算机建模和模拟.
- 化学工程是化学工程的组成部分.
背景情况:
- 预测泡流体粘度传统上依赖于风湿学模型,但在各种条件下实验性地确定模型常数是复杂的.
- 泡类风湿学受到许多因素的影响,包括剪切率,温度,压力,表面活性剂类型,气相组成和盐度.
- 精确的风湿学表征对于优化涉及泡液体的流程至关重要.
研究的目的:
- 调查各种参数对泡风湿学的影响.
- 开发和评估机器学习模型,用于预测学模型常量.
- 为此预测任务确定最有效的机器学习技术.
主要方法:
- 使用高压,高温风力计对泡风湿的实验测量.
- 将实验数据与已建立的风湿学模型相匹配 (Power-law,宾汉塑料,卡森).
- 应用和比较七种机器学习技术 (决策树,随机森林,XGBoost等). 为了预测模型常数.
主要成果:
- 卡森流体模型有效地描述了泡的质行为.
- 机器学习模型XGBoost (XGB) 在预测风湿常数方面表现出卓越的性能,在最佳条件下达到95%的准确性.
- 皮尔森的相关性分析表明,XGBoost在预测中使用了大多数功能,与其他模型不同.
结论:
- 机器学习,特别是XGBoost,提供了一种可靠且具有成本效益的方法,用于预测泡液体的湿度常数.
- 开发的方法可以显著减少调理参数确定所需的实验力度.
- 这种方法在涉及泡流体的应用中,为快速评估和优化提供了有价值的工具.
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