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Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
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Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
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Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
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Journal Article
Properties and Optimization Process Using Machine Learning for Recycling of Fly and Bottom Ashes in Fire-Resistant Materials
2025
Overview
Significant amounts of coal fly and bottom ash are generated globally each year, with especially large quantities of bottom ash accumulating in landfills. In this study, fly ash and bottom ash were used to create fire-resistant materials. A mix of 30 wt% gypsum, 9.5 wt% vermiculite, and 0.5 wt% polypropylene fibers was used, maintaining a constant water-to-solid ratio, with varying fly ash/bottom ash ratios (40/20, 30/30, and 20/40). The density, as well as various mechanical properties (compressive strength, flexural strength, and surface hardness), fire insulation capacity, and leaching behavior of both ashes were evaluated. When comparing the 40/20 and 20/40 compositions, a slight decrease in density was observed; however, compressive strength dropped drastically by 80%, while flexural strength decreased slightly due to the action of the polypropylene fibers, and fire resistance dropped by 8%. Neither of the ashes presented any environmental concerns from a leaching standpoint. Additionally, historical data from various materials with different wastes in previous works were used to train different machine learning models (random forest, gradient boosting, artificial neural networks, etc.). Compressive strength and fire resistance were predicted. Simple parameters (density, water/solid ratio and composition for compressive strength and thickness and the composition for fire resistance) were used as input in the models. Both regression and classification algorithms were applied to evaluate the models’ ability to predict compressive strength. Regression models for fire resistance reached r2 up to about 0.85. The classification results for the fire resistance rating (FRR) showed high accuracy (96%). The prediction of compressive strength is not as good as the fire resistance prediction, but compressive strength classification reached up to 99% accuracy for some models.
