Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (λ) has been modelled. The model has been obtained wi...

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Autores principales:	Sedat Golgiyaz, Muhammed Fatih Talu, Mahmut Daşkın, Cem Onat
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Excess air coefficient estimation Flame image Gauss model Flame stability Artificial neural network regression model Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/ad9ad5c978a540169f91b873091c4d3a
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id	oai:doaj.org-article:ad9ad5c978a540169f91b873091c4d3a
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spelling	oai:doaj.org-article:ad9ad5c978a540169f91b873091c4d3a2021-11-18T04:45:11ZEstimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network1110-016810.1016/j.aej.2021.06.022https://doaj.org/article/ad9ad5c978a540169f91b873091c4d3a2022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821003793https://doaj.org/toc/1110-0168It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (λ) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, λ data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature−λ.Sedat GolgiyazMuhammed Fatih TaluMahmut DaşkınCem OnatElsevierarticleExcess air coefficient estimationFlame imageGauss modelFlame stabilityArtificial neural network regression modelEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1079-1089 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Excess air coefficient estimation Flame image Gauss model Flame stability Artificial neural network regression model Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Excess air coefficient estimation Flame image Gauss model Flame stability Artificial neural network regression model Engineering (General). Civil engineering (General) TA1-2040 Sedat Golgiyaz Muhammed Fatih Talu Mahmut Daşkın Cem Onat Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
description	It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (λ) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, λ data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature−λ.
format	article
author	Sedat Golgiyaz Muhammed Fatih Talu Mahmut Daşkın Cem Onat
author_facet	Sedat Golgiyaz Muhammed Fatih Talu Mahmut Daşkın Cem Onat
author_sort	Sedat Golgiyaz
title	Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
title_short	Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
title_full	Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
title_fullStr	Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
title_full_unstemmed	Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
title_sort	estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/ad9ad5c978a540169f91b873091c4d3a
work_keys_str_mv	AT sedatgolgiyaz estimationofexcessaircoefficientoncoalcombustionprocessesviagaussmodelandartificialneuralnetwork AT muhammedfatihtalu estimationofexcessaircoefficientoncoalcombustionprocessesviagaussmodelandartificialneuralnetwork AT mahmutdaskın estimationofexcessaircoefficientoncoalcombustionprocessesviagaussmodelandartificialneuralnetwork AT cemonat estimationofexcessaircoefficientoncoalcombustionprocessesviagaussmodelandartificialneuralnetwork
_version_	1718425060344070144

Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

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