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Systematic review examines AI's role in enhancing sustainability in ceramic and refractory materials, indicating significant advancements.
The ceramic and refractory industries are undergoing a progressive transition toward more sustainable and resource-efficient manufacturing systems driven by increasing environmental regulations, rising energy demands, and the need to reduce dependence on virgin raw materials. In this context, artificial intelligence (AI) has emerged as a promising tool for improving material design, process optimization, predictive maintenance, and circular manufacturing strategies. This review provides a comprehensive analysis of recent advances in AI applications within ceramic and refractory systems, with particular emphasis on their role in enabling circular economy approaches and intelligent manufacturing environments. The study examines the integration of machine learning, deep learning, computer vision, digital twins, and Industry 4.0 technologies across multiple domains, including materials discovery, defect detection, waste classification, process control, and sustainability assessment. In addition, the review discusses the incorporation of secondary raw materials such as fly ash, slag, waste glass, ceramic sludge, and spent refractories into circular ceramic production systems. The analysis highlights the potential of AI-driven methodologies to improve resource efficiency, reduce environmental impact, and enhance process adaptability under complex industrial conditions. Furthermore, current limitations associated with data availability, model interpretability, industrial scalability, and integration with life cycle assessment frameworks are critically discussed. Finally, future research directions are identified, emphasizing the development of standardized datasets, hybrid experimental–AI methodologies, digital manufacturing ecosystems, and intelligent decision-making systems for next-generation sustainable ceramic and refractory technologies.
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Díaz-Tato et al. (2026) studied this question.
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