2026-05-28T08:41:56Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/411902026-02-03T11:05:14Zcom_10630_2254col_10630_37953

Optimizing cement kiln operation with expert systems Fernández Gassó, Raimundo Sevilla-Hurtado, Lorenzo Cañero-Nieto, Juan Miguel Hornos de cemento Industria del cemento 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. 2025-12-18T07:48:01Z 2025-12-18T07:48:01Z 2025-12-18T07:48:01Z 2025-09-20 journal article Fernández Gassó, Raimundo, Lorenzo Sevilla Hurtado, and Juan Miguel Cañero-Nieto. "Optimizing cement kiln operation with expert systems." The International Journal of Advanced Manufacturing Technology (2025): 1-13. https://hdl.handle.net/10630/41190 10.1007/s00170-025-16489-5 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ embargoed access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Springer Nature