Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models
The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C<sub>3</sub>S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multi...
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MDPI AG
2022-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/1/7 |
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| author | Taihao Han Sai Akshay Ponduru Arianit Reka Jie Huang Gaurav Sant Aditya Kumar |
| author_facet | Taihao Han Sai Akshay Ponduru Arianit Reka Jie Huang Gaurav Sant Aditya Kumar |
| author_sort | Taihao Han |
| collection | DOAJ |
| description | The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C<sub>3</sub>S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C<sub>3</sub>S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C<sub>3</sub>S in the undersaturated solvent. The DF model takes into account several variables, including the measurement method (i.e., <i>reactor connected to inductive coupled plasma spectrometer</i> and <i>flow chamber with vertical scanning interferometry</i>), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C<sub>3</sub>S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C<sub>3</sub>S. The coefficients and constant of the analytical model are optimized in two scenarios: <i>generic</i> and <i>alkaline</i> solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C<sub>3</sub>S when it is undersaturated and far from equilibrium. |
| first_indexed | 2024-03-09T13:50:18Z |
| format | Article |
| id | doaj.art-992c9346c28b45a8b231f1468fdc114a |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T13:50:18Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-992c9346c28b45a8b231f1468fdc114a2023-11-30T20:51:06ZengMDPI AGAlgorithms1999-48932022-12-01161710.3390/a16010007Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical ModelsTaihao Han0Sai Akshay Ponduru1Arianit Reka2Jie Huang3Gaurav Sant4Aditya Kumar5Department of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USACivil and Environmental Engineering, University of California, Los Angeles, CA 90095, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAThe dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C<sub>3</sub>S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C<sub>3</sub>S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C<sub>3</sub>S in the undersaturated solvent. The DF model takes into account several variables, including the measurement method (i.e., <i>reactor connected to inductive coupled plasma spectrometer</i> and <i>flow chamber with vertical scanning interferometry</i>), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C<sub>3</sub>S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C<sub>3</sub>S. The coefficients and constant of the analytical model are optimized in two scenarios: <i>generic</i> and <i>alkaline</i> solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C<sub>3</sub>S when it is undersaturated and far from equilibrium.https://www.mdpi.com/1999-4893/16/1/7tricalcium silicateanalytical modelion activitydissolution kineticsdeep forest |
| spellingShingle | Taihao Han Sai Akshay Ponduru Arianit Reka Jie Huang Gaurav Sant Aditya Kumar Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models Algorithms tricalcium silicate analytical model ion activity dissolution kinetics deep forest |
| title | Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models |
| title_full | Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models |
| title_fullStr | Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models |
| title_full_unstemmed | Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models |
| title_short | Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models |
| title_sort | predicting dissolution kinetics of tricalcium silicate using deep learning and analytical models |
| topic | tricalcium silicate analytical model ion activity dissolution kinetics deep forest |
| url | https://www.mdpi.com/1999-4893/16/1/7 |
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