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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
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一种基于人工智能的融流速预测方法,用于分析聚合物特性.

Mohammad Anwar Parvez1, Ibrahim M Mehedi2

  • 1Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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|September 13, 2025
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概括
此摘要是机器生成的。

本研究引入了一种人工智能模型,用于实时预测聚合物化流速 (MFR). 开发的模型准确地预测了MFR,使得聚合物制造业的质量控制能够得到加强.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.融化流速预测速度的预测鱼优化算法的优化算法聚合物特性分析分析.

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科学领域:

  • 聚合物科学与工程 聚合物科学与工程
  • 材料科学 材料科学 材料科学
  • 人工智能在制造业中的应用

背景情况:

  • 由于高性能,聚合物越来越多地取代传统材料.
  • 融化流速 (MFR) 是聚合物质量和可加工性的关键指标.
  • 目前的MFR测量方法耗时,不适合实时的工业质量控制.

研究的目的:

  • 开发一个准确和可部署的人工智能模型,用于实时预测聚合物化流速 (MFR).
  • 解决工业环境中传统的线下MFR测量技术的局限性.
  • 加强聚合物质量监测和可加工性分析.

主要方法:

  • 采用了1044个聚合物样本的数据集,其中六个输入特征 (反应器温度,压力,-比,催化剂料率) 和MFR作为目标变量.
  • 对于输入特征的规范化,应用了最小-最大缩放.
  • 两种组合模型,内核极端学习机器 (KELM) 和随机矢量函数链接 (RVFL),使用Pelican优化算法 (POA) 开发和优化.

主要成果:

  • 拟议的LAIML-MFRPPPA模型实现了高预测精度,R2为0.965,MAE为0.09,RMSE为0.12,MAPE为3.4%.
  • 与传统和深度学习模型相比,该模型表现出优异的性能.
  • 基于SHAP的灵敏度分析确定了融温度和分子量作为影响MFR的主导输入特征.

结论:

  • LAIML-MFRPPPA模型为实时聚合物质量监测提供了强大而准确的解决方案.
  • 这种人工智能驱动的方法促进了高效的可加工性分析和聚合物制造中的质量控制.
  • 该模型能够实时预测MFR,为工业应用提供了显著的优势.