000130176 001__ 130176
000130176 005__ 20240123091559.0
000130176 0247_ $$2doi$$a10.1016/j.fuel.2023.130770
000130176 0248_ $$2sideral$$a136100
000130176 037__ $$aART-2024-136100
000130176 041__ $$aeng
000130176 100__ $$aCompais, P.
000130176 245__ $$aPromoting the valorization of blast furnace gas in the steel industry with the visual monitoring of combustion and artificial intelligence
000130176 260__ $$c2024
000130176 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130176 5203_ $$aThe 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.
000130176 536__ $$9info:eu-repo/grantAgreement/EC/H2020/820771/EU/ Boosting new Approaches for flexibility Management By Optimizing process Off-gas and waste use/BAMBOO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 820771-BAMBOO
000130176 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000130176 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000130176 700__ $$0(orcid)0000-0003-3157-6267$$aArroyo, J.
000130176 700__ $$aTovar, F.
000130176 700__ $$aCuervo-Piñera, V.
000130176 700__ $$0(orcid)0000-0002-0704-4685$$aGil, A.$$uUniversidad de Zaragoza
000130176 7102_ $$15004$$2590$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Máquinas y Motores Térmi.
000130176 773__ $$g362 (2024), 130770 [10 pp.]$$pFuel$$tFuel$$x0016-2361
000130176 8564_ $$s1462421$$uhttps://zaguan.unizar.es/record/130176/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/semantics/openAccess
000130176 8564_ $$s2969513$$uhttps://zaguan.unizar.es/record/130176/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/semantics/openAccess
000130176 909CO $$ooai:zaguan.unizar.es:130176$$particulos$$pdriver
000130176 951__ $$a2024-01-23-08:17:37
000130176 980__ $$aARTICLE