000127712 001__ 127712
000127712 005__ 20241125101200.0
000127712 0247_ $$2doi$$a10.1016/j.engappai.2023.106772
000127712 0248_ $$2sideral$$a134837
000127712 037__ $$aART-2023-134837
000127712 041__ $$aeng
000127712 100__ $$aCompais, Pedro
000127712 245__ $$aDetection of slight variations in combustion conditions with machine learning and computer vision
000127712 260__ $$c2023
000127712 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127712 5203_ $$aWhen 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.
000127712 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$$9info:eu-repo/grantAgreement/EC/H2020/869939/EU/Implementation of a smart RETROfitting framework in the process industry towards its operation with variable, biobased and circular FEEDstock/RETROFEED$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 869939-RETROFEED
000127712 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000127712 590__ $$a7.5$$b2023
000127712 592__ $$a1.749$$b2023
000127712 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b6 / 84 = 0.071$$c2023$$dQ1$$eT1
000127712 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b5 / 181 = 0.028$$c2023$$dQ1$$eT1
000127712 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b25 / 353 = 0.071$$c2023$$dQ1$$eT1
000127712 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b24 / 197 = 0.122$$c2023$$dQ1$$eT1
000127712 593__ $$aArtificial Intelligence$$c2023$$dQ1
000127712 593__ $$aControl and Systems Engineering$$c2023$$dQ1
000127712 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ1
000127712 594__ $$a9.6$$b2023
000127712 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127712 700__ $$0(orcid)0000-0003-3157-6267$$aArroyo, Jorge
000127712 700__ $$aCastán-Lascorz, Miguel Ángel
000127712 700__ $$aBarrio, Jorge
000127712 700__ $$0(orcid)0000-0002-0704-4685$$aGil, Antonia$$uUniversidad de Zaragoza
000127712 7102_ $$15004$$2590$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Máquinas y Motores Térmi.
000127712 773__ $$g126 , Part A (2023), 106772 [11 pp.]$$pEng. appl. artif. intell.$$tENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE$$x0952-1976
000127712 8564_ $$s1929684$$uhttps://zaguan.unizar.es/record/127712/files/texto_completo.pdf$$yVersión publicada
000127712 8564_ $$s2833285$$uhttps://zaguan.unizar.es/record/127712/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127712 909CO $$ooai:zaguan.unizar.es:127712$$particulos$$pdriver
000127712 951__ $$a2024-11-22-12:11:11
000127712 980__ $$aARTICLE