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Strength and Heat of Hydration

Strength and Heat of Hydration

The hydration of cement is an exothermic reaction in which heat is generated as cement hydrates. This heat of hydration is critical to cement's strength development. The rate at which this heat is generated affects the temperature rise, with a majority of the heat being released early in the hydration process, half within the first three days, and about 75% within the first week.
The heat of hydration for each cement compound is significant; for instance, tricalcium aluminate (C3A) and...

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Updated: May 23, 2025

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

Published on: January 25, 2019

机器学习预测了从多种固体废物中生产陶矿的烧结温度.

Guanqi Yu¹, Chuan Wang², Qianlan Zhuo³

¹College of Ecology and Environment, Inner Mongolia University, Hohhot, China; School of Environmental Science & Engineering, Tianjin University, Tianjin, China.

Waste management (New York, N.Y.)

|May 21, 2025

概括

一个机器学习模型使用化学成分准确预测陶矿烧结温度. XGBoost模型显示了高性能,识别了影响过程的SiO2和Al2O3等关键组件.

关键词:

陶矿是一种陶矿.化学成分化学成分机器学习是机器学习.这就是 SHAP SHAP 的意思.烧结温度是烧结的温度.

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Last Updated: May 23, 2025

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

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Published on: January 25, 2019

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科学领域:

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.
数据科学数据科学数据科学

背景情况:

从固体废物中生产陶石对于可持续性至关重要.
预测烧结温度是优化陶材料质量和生产效率的关键.

研究的目的:

开发一种高效的机器学习模型,用于预测陶矿烧结温度.
为了确定化学成分对烧结温度的影响.
评估预测模型的概括性和可靠性.

主要方法:

从236个陶石样本收集了实验数据.
定义了八个关键化学成分作为输入特征.
训练和评估了六个机器学习模型,包括XGBoost.
使用SHAP分析来确定特征的重要性和适用性领域分析来验证模型.

主要成果:

该XGBoost模型实现了高预测精度 (R2=0.950,RMSE=7.767).
发现SiO2和Al2O3会增加烧结温度,而CaO和MgO会降低烧结温度.
该模型在未见的数据上展示了强大的概括性和预测可靠性.

结论:

机器学习,特别是XGBoost,为预测陶矿烧结温度提供了一个强大的框架.
了解化学成分的影响,可以从固体废物中优化陶矿生产.
这种方法提高了在陶矿制造中的固体废物材料的可持续利用.