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 (C3S), 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 C3S 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 C3S in the undersaturated solvent. The DF model takes into account several variables, including the measurement method (i.e., reactor connected to inductive coupled plasma spectrometer and flow chamber with vertical scanning interferometry), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C3S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C3S. The coefficients and constant of the analytical model are optimized in two scenarios: generic and alkaline solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C3S when it is undersaturated and far from equilibrium.
T. Han et al., "Predicting Dissolution Kinetics of Tricalcium Silicate using Deep Learning and Analytical Models," Algorithms, vol. 16, no. 1, article no. 7, MDPI, Jan 2023.
The definitive version is available at https://doi.org/10.3390/a16010007
Electrical and Computer Engineering
Materials Science and Engineering
Keywords and Phrases
Analytical Model; Deep Forest; Dissolution Kinetics; Ion Activity; Tricalcium Silicate
International Standard Serial Number (ISSN)
Article - Journal
© 2023 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
01 Jan 2023
National Science Foundation, Grant 1661609