127712 20241125101200.0 doi 10.1016/j.engappai.2023.106772 sideral 134837 ART-2023-134837 eng Compais, Pedro Detection of slight variations in combustion conditions with machine learning and computer vision 2023 Access copy available to the general public Unrestricted When monitoring combustion conditions, detecting minor variations, which may be complex even for the human eye, is critical for providing a fast response and correcting deviations. The aim of this study is to detect slight variations in combustion conditions by developing a flame monitoring system using machine learning and computer vision techniques applied to color images. Predictive models are developed for fuel blends with different heating values. The predictive models classify the combustion equivalence ratio based on multiple conditions, using a mean step size of 0.10 between states, a lower value than previously reported in related studies. Three machine learning algorithms are used for each fuel blend: logistic regression, support vector machine, and artificial neural network (multilayer perceptron). These models are fed the statistical, geometrical, and textural features extracted from the color images of the flames. The classification achieves accuracies from 0.78 to 0.97 in the detection of slight variations in the combustion conditions for all heating values. Thus, the monitoring system developed in this study is a promising alternative for implementation on an industrial scale and quick detection of changes in combustion conditions. 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/grantAgreement/EC/H2020/869939/EU/Implementation of a smart RETROfitting framework in the process industry towards its operation with variable, biobased and circular FEEDstock/RETROFEED This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 869939-RETROFEED info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 7.5 2023 AUTOMATION & CONTROL SYSTEMS 6 / 84 = 0.071 2023 Q1 T1 ENGINEERING, MULTIDISCIPLINARY 5 / 181 = 0.028 2023 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 25 / 353 = 0.071 2023 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 24 / 197 = 0.122 2023 Q1 T1 1.749 2023 Artificial Intelligence 2023 Q1 Control and Systems Engineering 2023 Q1 Electrical and Electronic Engineering 2023 Q1 9.6 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Arroyo, Jorge (orcid)0000-0003-3157-6267 Castán-Lascorz, Miguel Ángel Barrio, Jorge Gil, Antonia Universidad de Zaragoza (orcid)0000-0002-0704-4685 5004 590 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Máquinas y Motores Térmi. 126 , Part A (2023), 106772 [11 pp.] Eng. appl. artif. intell. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 0952-1976 1929684 http://zaguan.unizar.es/record/127712/files/texto_completo.pdf Versión publicada 2833285 http://zaguan.unizar.es/record/127712/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:127712 articulos driver 2024-11-22-12:11:11 ARTICLE