Archived
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Location
Lexington, Kentucky
Start Date
5-5-2026 2:00 PM
End Date
5-5-2026 2:30 PM
Description
Coal combustion residues (CCRs), including fly ash, bottom ash, desulfurization gypsum, etc., on specification or off-specification, remain an abundant yet underutilized byproduct of power generation. While these materials have long been considered as supplementary cementitious materials (SCMs), their variable chemical and physical characteristics limit broader adoption in blended cement and concrete. This study introduces an AI-guided framework to accelerate the upcycling of CCRs into high-performance construction materials. By integrating materials informatics, machine learning models, and experimental validation, the framework enables rapid property prediction and performance-based preprocessing recommendation or mix design optimization for diverse CCR sources. Preliminary results demonstrate the ability of AI-driven approaches to predict reactivity, optimize blending proportions, and reduce greenhouse gas emissions while ensuring mechanical performance and durability. This work highlights the transformative role of digital tools in creating sustainable pathways for CCR valorization and supporting circular economy principles.
Document Type
Presentation
Archival?
Archival
Included in
Energy Systems Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Mining Engineering Commons, Oil, Gas, and Energy Commons, Structural Materials Commons, Sustainability Commons
AI-Guided Upcycling of Coal Combustion Residues in Blended Cement and Concrete
Lexington, Kentucky
Coal combustion residues (CCRs), including fly ash, bottom ash, desulfurization gypsum, etc., on specification or off-specification, remain an abundant yet underutilized byproduct of power generation. While these materials have long been considered as supplementary cementitious materials (SCMs), their variable chemical and physical characteristics limit broader adoption in blended cement and concrete. This study introduces an AI-guided framework to accelerate the upcycling of CCRs into high-performance construction materials. By integrating materials informatics, machine learning models, and experimental validation, the framework enables rapid property prediction and performance-based preprocessing recommendation or mix design optimization for diverse CCR sources. Preliminary results demonstrate the ability of AI-driven approaches to predict reactivity, optimize blending proportions, and reduce greenhouse gas emissions while ensuring mechanical performance and durability. This work highlights the transformative role of digital tools in creating sustainable pathways for CCR valorization and supporting circular economy principles.
