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机器学习对增强生物降解的Phoenix dactylifera L/HDPE复合热谱进行分析

Zaid Abdulhamid Alhulaybi¹, Abdulrazak Jinadu Otaru¹

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

|June 19, 2024

概括

这项研究研究了由枣树 (Phoenix dactylifera L.) 和高密度聚乙烯 (HDPE) 制成的可生物降解聚合物复合材料的热分解. 机器学习准确地预测材料的行为,优化热特性和减少实验需求.

科学领域:

材料科学材料科学材料科学
聚合物化学聚合物化学
计算科学计算科学

背景情况:

越来越多地关注废物回收和分解以获得经济价值和资源管理.
可生物降解的聚合物复合材料为可持续材料解决方案提供了潜力.
芬尼克斯甲状腺菌 L. (PD) 和高密度聚乙烯 (HDPE) 是适合复合材料开发的大量废物材料.

研究的目的:

探索增强生物降解聚合物矩阵 (PD/HDPE) 的热分解.
将机器学习分析应用于实验热重力测量分析 (TGA) 数据.
开发和验证PD/HDPE热行为的预测模型.

主要方法:

在不同的加热速率 (10-40°C·min-1) 和温度 (25-600°C) 下对PD/HDPE复合材料进行热重度测量分析 (TGA).
开发一个深度神经网络机器学习模型.
用实验TGA数据对学习算法的训练和验证.

主要成果:

TGA显示体重减轻取决于降解温度,温度图在更高的加热速度下变化.
PD/HDPE复合材料显示出大量的残余灰,HDPE比PD更具热稳定性.
机器学习模型实现了接近零的剩余误差 (0.025) 和与实验数据的完美相关性 (R2~1).

结论:

机器学习准确地预测PD/HDPE复合材料的热分解.
优化的算法和模型减少了对广泛实验测试的需求.
这种方法可以有效地预测和优化废物衍生聚合物的热特性.

关键词:

城甲状腺植物 L.对TGA分析进行了分析.高密度聚乙烯高密度聚乙烯机器学习是机器学习.

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