130176 20260218073009.0 doi 10.1016/j.fuel.2023.130770 sideral 136100 ART-2024-136100 eng Compais, P. Promoting the valorization of blast furnace gas in the steel industry with the visual monitoring of combustion and artificial intelligence 2024 Access copy available to the general public Unrestricted The sustainability and decarbonization of processes in the steel industry are enhanced with the valorization of the gas generated during the chemical reactions produced in blast furnaces. However, the combustion of blast furnace gas (BFG) faces the drawback of lower flame stability, which increases the chance of operation shifts towards abnormal conditions and even the flashback or extinction of the flame. Thus, early detection and correction of regime deviations are needed to increase combustion efficiency, for which image-based systems have a high potential. This work focuses on monitoring an industrial furnace for steelmaking processes based on estimating O2 concentration in flue gases using color images captured inside the combustion chamber. An experimental campaign was performed in a 1.2-MW burner to develop the supervision system, using three fuel blends of BFG and natural gas. Images were processed to extract intensity and textural features, which were used to train predictive models based on machine learning algorithms: logistic regression, support vector machines, and artificial neural networks (multilayer perceptron). O2 concentration in flue gases was correctly estimated for at least 97 % of all the test samples and fuel blends. This study shows the potential of image-based systems for the automated control of BFG combustion at the industrial scale. info:eu-repo/grantAgreement/EC/H2020/820771/EU/ Boosting new Approaches for flexibility Management By Optimizing process Off-gas and waste use/BAMBOO This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 820771-BAMBOO info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 7.5 2024 ENGINEERING, CHEMICAL 22 / 176 = 0.125 2024 Q1 T1 ENERGY & FUELS 45 / 182 = 0.247 2024 Q1 T1 1.614 2024 Chemical Engineering (miscellaneous) 2024 Q1 Organic Chemistry 2024 Q1 Fuel Technology 2024 Q1 Energy Engineering and Power Technology 2024 Q1 14.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0003-3157-6267 Arroyo, J. Tovar, F. Cuervo-Piñera, V. (orcid)0000-0002-0704-4685 Gil, A. Universidad de Zaragoza 5004 590 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Máquinas y Motores Térmi. 362 (2024), 130770 [10 pp.] Fuel Fuel 0016-2361 1462421 http://zaguan.unizar.es/record/130176/files/texto_completo.pdf Postprint info:eu-repo/date/embargoEnd/2025-01-05 2969513 http://zaguan.unizar.es/record/130176/files/texto_completo.jpg?subformat=icon icon Postprint info:eu-repo/date/embargoEnd/2025-01-05 oai:zaguan.unizar.es:130176 articulos driver 2026-02-17-20:19:06 ARTICLE