Predicting calcium carbonate yield from wet carbonation of recycled cement paste using interpretable ensemble machine learning
File(s)
Published version
Author(s)
Li, Yining
Chanona, Ehecatl Antonio del Rio
Wong, Hong S
Myers, Rupert J
Type
Journal Article
Abstract
Wet carbonation is a promising method for enhancing the performance of recycled cement paste as a low carbon supplementary cementitious material, however, its optimisation for yield is hindered by an incomplete understanding of its mechanisms and uncertainty about the relative importance of various influencing factors. This study applies advanced machine learning techniques to optimise the wet carbonation of recycled cement paste to maximise calcium carbonate yield, utilising a dataset of 227 experimental records covering diverse process conditions. Six machine learning models—support vector regression, decision tree, random forest, gradient boosting regressor, extreme gradient boosting, and stacking—were trained and evaluated using R2, MAE, and RMSE as performance metrics. Model evaluation was performed using 10-fold cross-validation to enhance robustness and ensure generalisation across different data subsets. The stacking ensemble model achieved the highest predictive accuracy, with R2 values of 0.997 (training) and 0.948 (testing), improving prediction accuracy by 19.85 % compared to the least accurate model (decision tree testing). Additionally, the stacking model achieved MAE values of 0.0101 (training) and 0.0393 (testing), and RMSE values of 0.0125 (training) and 0.0536 (testing), demonstrating its superior predictive capability. This model effectively addressed the challenge of limited data, a common issue in applying machine learning in materials science. Shapley additive explanations identified the fineness of cement paste particles and carbonation time as critical factors affecting the calcium carbonate yield variability, contributing 0.14 and 0.11, respectively. Therefore, these results show that finer particle size, extended carbonation time, and the addition of extra OH- ions in the reactor can be applied to develop more efficient wet carbonation processes, enhancing the production of CaCO3 and CO2 uptake. This research highlights the potential of ensemble learning to optimise concrete recycling techniques, offering a sustainable solution for the construction industry.
Date Issued
2025-07-10
Date Acceptance
2025-05-14
Citation
Journal of Cleaner Production, 2025, 514
ISSN
0959-6526
Publisher
Elsevier
Journal / Book Title
Journal of Cleaner Production
Volume
514
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Subjects
CONCRETE
Engineering
Engineering, Environmental
Ensemble learning
Environmental Sciences
Environmental Sciences & Ecology
Green & Sustainable Science & Technology
Life Sciences & Biomedicine
Machine learning
Recycled cement paste
Science & Technology
Science & Technology - Other Topics
Shapley additive explanations
Technology
Wet carbonation
Publication Status
Published
Article Number
145727
Date Publish Online
2025-05-19