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机器学习反向传播预测和分析聚合物 (乙烯基酒精) 的热降解

Abdulrazak Jinadu Otaru¹, Zaid Abdulhamid Alhulaybi¹, Ibrahim Dubdub¹

¹Chemical Engineering Department, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia.

|February 10, 2024

概括

机器学习准确地预测了在低加热速度下聚合物 (乙烯基酒精) 的热降解. 这种方法模拟体重减轻,有助于开发各种应用的先进聚合物材料.

科学领域:

聚合物科学聚合物科学
材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.

背景情况:

热重力测量分析 (TGA) 对于理解聚合物的热行为至关重要.
现有的TGA研究往往强调高加热率,在低加热率数据中留下了一个空白.
聚乙烯醇 (PVA) 是一种广泛使用的聚合物,具有重要的工业应用.

研究的目的:

使用机器学习分析PVA在低加热速率 (2,5,10K/分钟) 的热降解.
开发基于加热速度和温度的PVA减重的预测模型.
在中等低加热速率下插入TGA数据.

主要方法:

使用热重力测量分析 (TGA) 来收集PVA的热降解数据.
使用人工神经网络反向传播 (机器学习算法) 来进行数据分析.
与实验数据的相关模型预测使用99.2%的相关系数.

主要成果:

增加的加热速率使降解温度变高,表明了变化的热行为.
由于分解,在205°C和405°C之间发生了显著的体重减轻.
机器学习模型准确地预测了PVA体重减轻,与实验结果高度相关.

结论:

机器学习反向传播提供了一种可靠的方法,用于预测低加热速率的PVA温度图.
这种方法可以插入TGA数据,支持实验发现和文献.
开发的模型可以为创建用于各种应用的基于PVA的新型材料做出贡献.

关键词:

在TGA上,TGA就是TGA.实验实验实验实验实验机器学习是机器学习.聚 (乙烯基酒精) 聚 (乙烯基酒精) 是一种

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