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<p>The optimization of cement kilns presents a significant challenge for the industry, where achieving operational efficiency and sustainability is paramount. This study introduces a Kiln Expert System (KES) that leverages symbolic artificial intelligence (AI), real-time data analysis, and adaptive algorithms to dynamically stabilize key variables such as calciner temperature and oxygen levels. By incorporating a higher proportion of alternative fuels (ATFs), KES not only reduces variable costs, but also minimizes the carbon footprint and strengthens the competitive positioning of operations. Deployed in a facility with a five-stage preheater and a multistage calciner, the KES fine-tunes combustion parameters in real time to maintain high production standards, improve performance, and adapt to changing operational conditions. This innovative approach demonstrates how AI-driven systems can transform process management by enabling precise control, optimizing resource use, and aligning industrial practices with environmental goals.</p>

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Optimizing cement kiln operation with expert systems

  • 期刊论文

  • 发表日期：

    2025年9月20日

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  • Raimundo Fernández Gassó,
  • Lorenzo Sevilla Hurtado,
  • Juan Miguel Cañero-Nieto

摘要

The optimization of cement kilns presents a significant challenge for the industry, where achieving operational efficiency and sustainability is paramount. This study introduces a Kiln Expert System (KES) that leverages symbolic artificial intelligence (AI), real-time data analysis, and adaptive algorithms to dynamically stabilize key variables such as calciner temperature and oxygen levels. By incorporating a higher proportion of alternative fuels (ATFs), KES not only reduces variable costs, but also minimizes the carbon footprint and strengthens the competitive positioning of operations. Deployed in a facility with a five-stage preheater and a multistage calciner, the KES fine-tunes combustion parameters in real time to maintain high production standards, improve performance, and adapt to changing operational conditions. This innovative approach demonstrates how AI-driven systems can transform process management by enabling precise control, optimizing resource use, and aligning industrial practices with environmental goals.