Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Strength and Heat of Hydration01:29

Strength and Heat of Hydration

201
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...
201

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Isolated unilateral pulmonary vein atresia with hemoptysis in a child: A case report and literature review.

Medicine·2018
Same author

Clinical factors associated with intestinal strangulating obstruction and recurrence in adhesive small bowel obstruction: A retrospective study of 288 cases.

Medicine·2018
Same author

[Determination of organic acids in 1, 2-butylene oxide products by valve switch-ion chromatography].

Se pu = Chinese journal of chromatography·2018
Same author

Disruption of Planar Cell Polarity Pathway Attributable to Valproic Acid-Induced Congenital Heart Disease through Hdac3 Participation in Mice.

Chinese medical journal·2018
Same author

Multispectral and large bandwidth achromatic imaging with a single diffractive photon sieve.

Optics express·2018
Same author

Immunization with Chlamydia psittaci plasmid-encoded protein CPSIT_p7 induces partial protective immunity against chlamydia lung infection in mice.

Immunologic research·2018

Related Experiment Video

Updated: May 23, 2025

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
06:53

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

Published on: January 25, 2019

14.1K

Machine learning predicting sintering temperature for ceramsite production from multiple solid wastes.

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
PubMed
Summary

A machine learning model accurately predicts ceramsite sintering temperature using chemical composition. XGBoost model shows high performance, identifying key components like SiO2 and Al2O3 that influence the process.

Keywords:
CeramsiteChemical compositionMachine learningSHAPSintering temperature

More Related Videos

Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization
09:46

Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization

Published on: May 19, 2019

8.1K
Fused Filament Fabrication FFF of Metal-Ceramic Components
08:43

Fused Filament Fabrication FFF of Metal-Ceramic Components

Published on: January 11, 2019

17.1K

Related Experiment Videos

Last Updated: May 23, 2025

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
06:53

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

Published on: January 25, 2019

14.1K
Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization
09:46

Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization

Published on: May 19, 2019

8.1K
Fused Filament Fabrication FFF of Metal-Ceramic Components
08:43

Fused Filament Fabrication FFF of Metal-Ceramic Components

Published on: January 11, 2019

17.1K

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Data Science

Background:

  • Ceramsite production from solid waste is crucial for sustainability.
  • Predicting sintering temperature is key to optimizing ceramsite quality and production efficiency.

Purpose of the Study:

  • To develop an efficient machine learning model for predicting ceramsite sintering temperature.
  • To identify the influence of chemical composition on sintering temperature.
  • To assess the generalizability and reliability of the predictive model.

Main Methods:

  • Collected experimental data from 236 ceramsite samples.
  • Defined eight key chemical components as input features.
  • Trained and evaluated six machine learning models, including XGBoost.
  • Utilized SHAP analysis for feature importance and applicability domain analysis for model validation.

Main Results:

  • The XGBoost model achieved high predictive accuracy (R²=0.950, RMSE=7.767).
  • SiO2 and Al2O3 were found to increase sintering temperature, while CaO and MgO decreased it.
  • The model demonstrated strong generalizability and predictive reliability on unseen data.

Conclusions:

  • Machine learning, particularly XGBoost, offers a robust framework for predicting ceramsite sintering temperature.
  • Understanding the impact of chemical constituents optimizes ceramsite production from solid waste.
  • This approach enhances the sustainable utilization of solid waste materials in ceramsite manufacturing.